ALBERT

Description: Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 52(4), (2022): 597–616, https://doi.org/10.1175/jpo-d-21-0121.1.

Description: We provide a first-principles analysis of the energy fluxes in the oceanic internal wave field. The resulting formula is remarkably similar to the renowned phenomenological formula for the turbulent dissipation rate in the ocean, which is known as the finescale parameterization. The prediction is based on the wave turbulence theory of internal gravity waves and on a new methodology devised for the computation of the associated energy fluxes. In the standard spectral representation of the wave energy density, in the two-dimensional vertical wavenumber–frequency (m–ω) domain, the energy fluxes associated with the steady state are found to be directed downscale in both coordinates, closely matching the finescale parameterization formula in functional form and in magnitude. These energy transfers are composed of a “local” and a “scale-separated” contributions; while the former is quantified numerically, the latter is dominated by the induced diffusion process and is amenable to analytical treatment. Contrary to previous results indicating an inverse energy cascade from high frequency to low, at odds with observations, our analysis of all nonzero coefficients of the diffusion tensor predicts a direct energy cascade. Moreover, by the same analysis fundamental spectra that had been deemed “no-flux” solutions are reinstated to the status of “constant-downscale-flux” solutions. This is consequential for an understanding of energy fluxes, sources, and sinks that fits in the observational paradigm of the finescale parameterization, solving at once two long-standing paradoxes that had earned the name of “oceanic ultraviolet catastrophe.”

Description: The authors gratefully acknowledge support from the ONR Grant N00014-17-1-2852. YL gratefully acknowledges support from NSF DMS Award 2009418.

Description: 2022-09-25

Description: Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 52(8), (2022): 1593-1611, https://doi.org/10.1175/jpo-d-21-0180.1.

Description: This study presents novel observational estimates of turbulent dissipation and mixing in a standing meander between the Southeast Indian Ridge and the Macquarie Ridge in the Southern Ocean. By applying a finescale parameterization on the temperature, salinity, and velocity profiles collected from Electromagnetic Autonomous Profiling Explorer (EM-APEX) floats in the upper 1600 m, we estimated the intensity and spatial distribution of dissipation rate and diapycnal mixing along the float tracks and investigated the sources. The indirect estimates indicate strong spatial and temporal variability of turbulent mixing varying from O(10−6) to O(10−3) m2 s−1 in the upper 1600 m. Elevated turbulent mixing is mostly associated with the Subantarctic Front (SAF) and mesoscale eddies. In the upper 500 m, enhanced mixing is associated with downward-propagating wind-generated near-inertial waves as well as the interaction between cyclonic eddies and upward-propagating internal waves. In the study region, the local topography does not play a role in turbulent mixing in the upper part of the water column, which has similar values in profiles over rough and smooth topography. However, both remotely generated internal tides and lee waves could contribute to the upward-propagating energy. Our results point strongly to the generation of turbulent mixing through the interaction of internal waves and the intense mesoscale eddy field.

Description: The observations were funded through grants from the Australian Research Council Discovery Project (DP170102162) and Australia’s Marine National Facility. Surface drifters were provided by Dr. Shaun Dolk of the Global Drifter Program. AC was supported by an Australian Research Council Postdoctoral Fellowship. AC, HEP, and NLB acknowledge support from the Australian Government Department of the Environment and Energy National Environmental Science Program and the ARC Centre of Excellence in Climate Extremes. KP acknowledges the support from the National Science Foundation.

Description: Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 52(1),(2022): 75–97, https://doi.org/10.1175/JPO-D-21-0099.1.

Description: Mesoscale eddies contain the bulk of the ocean’s kinetic energy (KE), but fundamental questions remain on the cross-scale KE transfers linking eddy generation and dissipation. The role of submesoscale flows represents the key point of discussion, with contrasting views of submesoscales as either a source or a sink of mesoscale KE. Here, the first observational assessment of the annual cycle of the KE transfer between mesoscale and submesoscale motions is performed in the upper layers of a typical open-ocean region. Although these diagnostics have marginal statistical significance and should be regarded cautiously, they are physically plausible and can provide a valuable benchmark for model evaluation. The cross-scale KE transfer exhibits two distinct stages, whereby submesoscales energize mesoscales in winter and drain mesoscales in spring. Despite this seasonal reversal, an inverse KE cascade operates throughout the year across much of the mesoscale range. Our results are not incompatible with recent modeling investigations that place the headwaters of the inverse KE cascade at the submesoscale, and that rationalize the seasonality of mesoscale KE as an inverse cascade-mediated response to the generation of submesoscales in winter. However, our findings may challenge those investigations by suggesting that, in spring, a downscale KE transfer could dampen the inverse KE cascade. An exploratory appraisal of the dynamics governing mesoscale–submesoscale KE exchanges suggests that the upscale KE transfer in winter is underpinned by mixed layer baroclinic instabilities, and that the downscale KE transfer in spring is associated with frontogenesis. Current submesoscale-permitting ocean models may substantially understate this downscale KE transfer, due to the models’ muted representation of frontogenesis.

Description: The OSMOSIS experiment was funded by the U.K. Natural Environment Research Council (NERC) through Grants NE/1019999/1 and NE/101993X/1. ACNG acknowledges the support of the Royal Society and the Wolfson Foundation, and XY that of a China Scholarship Council PhD studentship.

Description: Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of the Atmospheric and Oceanic Technology 39(4), (2022): 491–502, https://doi.org/10.1175/jtech-d-21-0046.1.

Description: The Air-Launched Autonomous Micro Observer (ALAMO) is a versatile profiling float that can be launched from an aircraft to make temperature and salinity observations of the upper ocean for over a year with high temporal sampling. Similar in dimensions and weight to an airborne expendable bathythermograph (AXBT), but with the same capability as Argo profiling floats, ALAMOs can be deployed from an A-sized (sonobuoy) launch tube, the stern ramp of a cargo plane, or the door of a small aircraft. Unlike an AXBT, however, the ALAMO float directly measures pressure, can incorporate additional sensors, and is capable of performing hundreds of ocean profiles compared to the single temperature profile provided by an AXBT. Upon deployment, the float parachutes to the ocean, releases the air-deployment package, and immediately begins profiling. Ocean profile data along with position and engineering information are transmitted via the Iridium satellite network, automatically processed, and then distributed by the Global Telecommunications System for use by the operational forecasting community. The ALAMO profiling mission can be modified using the two-way Iridium communications to change the profiling frequency and depth. Example observations are included to demonstrate the ALAMO’s utility.

Description: This work was supported by the National Oceanographic and Atmospheric Administration under Grants NA13OAR4830233 (as part of CINAR Sandy Supplemental funding from the Disaster Relief Appropriations Act of 2013) and NA14OAR4320158 and by Office of Naval Research under Grants N0001416WX01384, N0001416WX01262, and N000141512293. ALAMO floats are commercially available from MRV Systems, LLC (https://www.mrvsys.com).

Description: Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of the Atmospheric and Oceanic Technology 39(5), (2022): 595–617, https://doi.org/10.1175/jtech-d-21-0039.1.

Description: The future Surface Water and Ocean Topography (SWOT) mission aims to map sea surface height (SSH) in wide swaths with an unprecedented spatial resolution and subcentimeter accuracy. The instrument performance needs to be verified using independent measurements in a process known as calibration and validation (Cal/Val). The SWOT Cal/Val needs in situ measurements that can make synoptic observations of SSH field over an O(100) km distance with an accuracy matching the SWOT requirements specified in terms of the along-track wavenumber spectrum of SSH error. No existing in situ observing system has been demonstrated to meet this challenge. A field campaign was conducted during September 2019–January 2020 to assess the potential of various instruments and platforms to meet the SWOT Cal/Val requirement. These instruments include two GPS buoys, two bottom pressure recorders (BPR), three moorings with fixed conductivity–temperature–depth (CTD) and CTD profilers, and a glider. The observations demonstrated that 1) the SSH (hydrostatic) equation can be closed with 1–3 cm RMS residual using BPR, CTD mooring and GPS SSH, and 2) using the upper-ocean steric height derived from CTD moorings enable subcentimeter accuracy in the California Current region during the 2019/20 winter. Given that the three moorings are separated at 10–20–30 km distance, the observations provide valuable information about the small-scale SSH variability associated with the ocean circulation at frequencies ranging from hourly to monthly in the region. The combined analysis sheds light on the design of the SWOT mission postlaunch Cal/Val field campaign.

Description: The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). All authors are supported by the SWOT project. J. T. Farrar was partially supported by NASA NNX16AH76G.

Description: 2022-11-01

Description: Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 52(6), (2022): 1233-1244, https://doi.org/10.1175/jpo-d-21-0223.1.

Description: The Sverdrup relation is the backbone of wind-driven circulation theory; it is a simple relation between the meridional transport of the wind-driven circulation in the upper ocean and the wind stress curl. However, the relation is valid for steady circulation only. In this study, a time-dependent Sverdrup relation is postulated, in which the meridional transport in a time-dependent circulation is the sum of the local wind stress curl term and a time-delayed term representing the effect of the eastern boundary condition. As an example, this time-dependent Sverdrup relation is evaluated through its application to the equatorial circulation in the Indian Ocean, using reanalysis data and a reduced gravity model. Close examination reveals that the southward Somali Current occurring during boreal winter is due to the combination of the local wind stress curl in the Arabian Sea and delayed signals representing the time change of layer thickness at the eastern boundary.

Description: This work is supported by NSFC (41822602, 41976016, 42005035, 42076021), the Strategic Priority Research Program of Chinese Academy of Sciences (XDB42000000, XDA 20060502), Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (GML2019ZD0306), Guangdong Basic and Applied Basic Research Foundation (2021A1515011534), Youth Innovation Promotion Association CAS, ISEE2021ZD01, and LTOZZ2002. The numerical simulation is supported by the High-Performance Computing Division in the South China Sea Institute of Oceanology.

Description: 2022-11-27

Description: Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 52(12), (2022): 2923–2933, https://doi.org/10.1175/jpo-d-22-0064.1.

Description: The characteristics and dynamics of depth-average along-shelf currents at monthly and longer time scales are examined using 17 years of observations from the Martha’s Vineyard Coastal Observatory on the southern New England inner shelf. Monthly averages of the depth-averaged along-shelf current are almost always westward, with the largest interannual variability in winter. There is a consistent annual cycle with westward currents of 5 cm s−1 in summer decreasing to 1–2 cm s−1 in winter. Both the annual cycle and interannual variability in the depth-average along-shelf current are predominantly driven by the along-shelf wind stress. In the absence of wind forcing, there is a westward flow of ∼5 cm s−1 throughout the year. At monthly time scales, the depth-average along-shelf momentum balance is primarily between the wind stress, surface gravity wave–enhanced bottom stress, and an opposing pressure gradient that sets up along the southern New England shelf in response to the wind. Surface gravity wave enhancement of bottom stress is substantial over the inner shelf and is essential to accurately estimating the bottom stress variation across the inner shelf.

Description: The National Science Foundation, Woods Hole Oceanographic Institution, the Massachusetts Technology Collaborative, and the Office of Naval Research have supported the construction and maintenance of MVCO. The analysis presented here was partially funded by the National Science Foundation under Grants OCE 1558874 and OCE 1655686.

Description: Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 52(7), (2022): 1415–1430. https://doi.org/10.1175/JPO-D-21-0147.1.

Description: Strong subinertial variability near a seamount at the Xisha Islands in the South China Sea was revealed by mooring observations from January 2017 to January 2018. The intraseasonal deep flows presented two significant frequency bands, with periods of 9–20 and 30–120 days, corresponding to topographic Rossby waves (TRWs) and deep eddies, respectively. The TRW and deep eddy signals explained approximately 60% of the kinetic energy of the deep subinertial currents. The TRWs at the Ma, Mb, and Mc moorings had 297, 262, and 274 m vertical trapping lengths, and ∼43, 38, and 55 km wavelengths, respectively. Deep eddies were independent from the upper layer, with the largest temperature anomaly being 〉0.4°C. The generation of the TRWs was induced by mesoscale perturbations in the upper layer. The interaction between the cyclonic–anticyclonic eddy pair and the seamount topography contributed to the generation of deep eddies. Owing to the potential vorticity conservation, the westward-propagating tilted interface across the eddy pair squeezed the deep-water column, thereby giving rise to negative vorticity west of the seamount. The strong front between the eddy pair induced a northward deep flow, thereby generating a strong horizontal velocity shear because of lateral friction and enhanced negative vorticity. Approximately 4 years of observations further confirmed the high occurrence of TRWs and deep eddies. TRWs and deep eddies might be crucial for deep mixing near rough topographies by transferring mesoscale energy to small scales.

Description: This work was supported by the National Natural Science Foundation of China (92158204, 91958202, 42076019, 41776036, 91858203), the Open Project Program of State Key Laboratory of Tropical Oceanography (project LTOZZ2001), and Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (GML2019ZD0304).

Description: 2022-12-16

Description: Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 52(8), (2022): 1927-1943, https://doi.org/10.1175/jpo-d-21-0124.1.

Description: The Galápagos Archipelago lies on the equator in the path of the eastward flowing Pacific Equatorial Undercurrent (EUC). When the EUC reaches the archipelago, it upwells and bifurcates into a north and south branch around the archipelago at a latitude determined by topography. Since the Coriolis parameter (f) equals zero at the equator, strong velocity gradients associated with the EUC can result in Ertel potential vorticity (Q) having sign opposite that of planetary vorticity near the equator. Observations collected by underwater gliders deployed just west of the Galápagos Archipelago during 2013–16 are used to estimate Q and to diagnose associated instabilities that may impact the Galápagos Cold Pool. Estimates of Q are qualitatively conserved along streamlines, consistent with the 2.5-layer, inertial model of the EUC by Pedlosky. The Q with sign opposite of f is advected south of the Galápagos Archipelago when the EUC core is located south of the bifurcation latitude. The horizontal gradient of Q suggests that the region between 2°S and 2°N above 100 m is barotropically unstable, while limited regions are baroclinically unstable. Conditions conducive to symmetric instability are observed between the EUC core and the equator and within the southern branch of the undercurrent. Using 2-month and 3-yr averages, e-folding time scales are 2–11 days, suggesting that symmetric instability can persist on those time scales.

Description: This work was supported by the National Science Foundation (Grants OCE-1232971 and OCE-1233282), the NASA Earth and Space Science Fellowship Program (Grant 80NSSC17K0443), and the Global Ocean Monitoring and Observing Program of the National Oceanographic and Atmospheric Administration (NA13OAR4830216). Color maps are from Thyng et al. (2016).

Description: 2023-02-01

Description: Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of the Atmospheric and Oceanic Technology 39(8), (2022): 1183-1198, https://doi.org/10.1175/jtech-d-21-0068.1.

Description: Horizontal kinematic properties, such as vorticity, divergence, and lateral strain rate, are estimated from drifter clusters using three approaches. At submesoscale horizontal length scales O(1–10)km, kinematic properties become as large as planetary vorticity f, but challenging to observe because they evolve on short time scales O(hourstodays). By simulating surface drifters in a model flow field, we quantify the sources of uncertainty in the kinematic property calculations due to the deformation of cluster shape. Uncertainties arise primarily due to (i) violation of the linear estimation methods and (ii) aliasing of unresolved scales. Systematic uncertainties (iii) due to GPS errors, are secondary but can become as large as (i) and (ii) when aspect ratios are small. Ideal cluster parameters (number of drifters, length scale, and aspect ratio) are determined and error functions estimated empirically and theoretically. The most robust method—a two-dimensional, linear least squares fit—is applied to the first few days of a drifter dataset from the Bay of Bengal. Application of the length scale and aspect-ratio criteria minimizes errors (i) and (ii), and reduces the total number of clusters and so computational cost. The drifter-estimated kinematic properties map out a cyclonic mesoscale eddy with a surface, submesoscale fronts at its perimeter. Our analyses suggest methodological guidance for computing the two-dimensional kinematic properties in submesoscale flows, given the recently increasing quantity and quality of drifter observations, while also highlighting challenges and limitations.

Description: This research was supported by the Office of Naval Research (ONR) Departmental Research Initiative ASIRI under Grant N00014-13-1-0451 (SE and AM) and Grant N00014-13-1-0477 (VH and LC). The authors thank the captain and crew of the R/V Roger Revelle, and Andrew Lucas with the Multiscale Ocean Dynamics group at the Scripps Institution for Oceanography for providing the FastCTD data collected in 2015, which was supported by ONR Grant N00014-13-1-0489, as well as Eric D’Asaro for helpful discussions and Lance Braasch for assistance with the drifter dataset. AM and SE further thank NSF (Grant OCE-I434788) and ONR (Grant N00014-16-1-2470) for support. VH and LC were additionally supported by ONR Grants N00014-15-1-2286, N00014-14-1-0183, N00014-19-1-26-91 and NOAA Global Drifter Program (GDP) Grant NA15OAR4320071.

Description: 2023-02-01

Description: Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 50(9), (2020): 2491-2506, doi:10.1175/JPO-D-20-0056.1.

Description: An idealized two-layer shallow water model is applied to the study of the dynamics of the Arctic Ocean halocline. The model is forced by a surface stress distribution reflective of the observed wind stress pattern and ice motion and by an inflow representing the flow of Pacific Water through Bering Strait. The model reproduces the main elements of the halocline circulation: an anticyclonic Beaufort Gyre in the western basin (representing the Canada Basin), a cyclonic circulation in the eastern basin (representing the Eurasian Basin), and a Transpolar Drift between the two gyres directed from the upwind side of the basin to the downwind side of the basin. Analysis of the potential vorticity budget shows a basin-averaged balance primarily between potential vorticity input at the surface and dissipation at the lateral boundaries. However, advection is a leading-order term not only within the anticyclonic and cyclonic gyres but also between the gyres. This means that the eastern and western basins are dynamically connected through the advection of potential vorticity. Both eddy and mean fluxes play a role in connecting the regions of potential vorticity input at the surface with the opposite gyre and with the viscous boundary layers. These conclusions are based on a series of model runs in which forcing, topography, straits, and the Coriolis parameter were varied.

Description: This study was supported by National Science Foundation Grant OPP-1822334. Comments and suggestions from two anonymous referees greatly helped to improve the paper.

Description: 2021-02-17

Description: Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 50(11), (2020): 3235–3251, https://doi.org/10.1175/JPO-D-20-0095.1.

Description: The dense outflow through Denmark Strait is the largest contributor to the lower limb of the Atlantic meridional overturning circulation, yet a description of the full velocity field across the strait remains incomplete. Here we analyze a set of 22 shipboard hydrographic–velocity sections occupied along the Látrabjarg transect at the Denmark Strait sill, obtained over the time period 1993–2018. The sections provide the first complete view of the kinematic components at the sill: the shelfbreak East Greenland Current (EGC), the combined flow of the separated EGC, and the North Icelandic Jet (NIJ), and the northward-flowing North Icelandic Irminger Current (NIIC). The total mean transport of overflow water is 3.54 ± 0.29 Sv (1 Sv ≡ 106 m3 s−1), comparable to previous estimates. The dense overflow is partitioned in terms of water mass constituents and flow components. The mean transports of the two types of overflow water—Atlantic-origin Overflow Water and Arctic-origin Overflow Water—are comparable in Denmark Strait, while the merged NIJ–separated EGC transports 55% more water than the shelfbreak EGC. A significant degree of water mass exchange takes place between the branches as they converge in Denmark Strait. There are two dominant time-varying configurations of the flow that are characterized as a cyclonic state and a noncyclonic state. These appear to be wind-driven. A potential vorticity analysis indicates that the flow through Denmark Strait is subject to symmetric instability. This occurs at the top of the overflow layer, implying that the mixing/entrainment process that modifies the overflow water begins at the sill.

Description: Funding for the study was provided by National Science Foundation (NSF) Grants OCE-1259618, OCE-1756361, and OCE-1558742. The German research cruises were financially supported through various EU Projects (e.g. THOR, NACLIM) and national projects (most recently TRR 181 “Energy Transfer in Atmosphere and Ocean” funded by the German Research Foundation and RACE II “Regional Atlantic Circulation and Global Change” funded by the German Federal Ministry for Education and Research). GWKM acknowledges the support of the Natural Sciences and Engineering Research Council of Canada. LP was supported by NSF Grant OCE-1657870.

Description: Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 33(17), (2020): 7697-7714, https://doi.org/10.1175/JCLI-D-20-0115.1.

Description: The decadal to multidecadal mixed layer variability is investigated in a region south of the Kuroshio Extension (130°E–180°, 25°–35°N), an area where the North Pacific subtropical mode water forms, during 1948–2012. By analyzing the mixed layer heat budget with different observational and reanalysis data, here we show that the decadal to multidecadal variability of the mixed layer temperature and mixed layer depth is covaried with the Atlantic multidecadal oscillation (AMO), instead of the Pacific decadal oscillation (PDO). The mixed layer temperature has strong decadal to multidecadal variability, being warm before 1970 and after 1990 (AMO positive phase) and cold during 1970–90 (AMO negative phase), and so does the mixed layer depth. The dominant process for the mixed layer temperature decadal to multidecadal variability is the Ekman advection, which is controlled by the zonal wind changes related to the AMO. The net heat flux into the ocean surface Qnet acts as a damping term and it is mainly from the effect of latent heat flux and partially from sensible heat flux. While the wind as well as mixed layer temperature decadal changes related to the PDO are weak in the western Pacific Ocean. Our finding proposes the possible influence of the AMO on the northwestern Pacific Ocean mixed layer variability, and could be a potential predictor for the decadal to multidecadal climate variability in the western Pacific Ocean.

Description: Xiaopei Lin is supported by the China’s national key research and development projects (2016YFA0601803) and the National Natural Science Foundation of China (41925025 and U1606402). Baolan Wu is supported by the China Scholarship Council (201806330010). Lisan Yu thanks NOAA for support for her study on climate change and variability.

Description: Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 51(8),(2021): 2425–2441, https://doi.org/10.1175/JPO-D-20-0317.1.

Description: The frequency and latitudinal dependence of the midlatitude wind-driven meridional overturning circulation (MOC) is studied using theory and linear and nonlinear applications of a quasigeostrophic numerical model. Wind forcing is varied either by changing the strength of the wind or by shifting the meridional location of the wind stress curl pattern. At forcing periods of less than the first-mode baroclinic Rossby wave basin crossing time scale, the linear response in the middepth and deep ocean is in phase and opposite to the Ekman transport. For forcing periods that are close to the Rossby wave basin crossing time scale, the upper and deep MOC are enhanced, and the middepth MOC becomes phase shifted, relative to the Ekman transport. At longer forcing periods the deep MOC weakens and the middepth MOC increases, but eventually for long enough forcing periods (decadal) the entire wind-driven MOC spins down. Nonlinearities and mesoscale eddies are found to be important in two ways. First, baroclinic instability causes the middepth MOC to weaken, lose correlation with the Ekman transport, and lose correlation with the MOC in the opposite gyre. Second, eddy thickness fluxes extend the MOC beyond the latitudes of direct wind forcing. These results are consistent with several recent studies describing the four-dimensional structure of the MOC in the North Atlantic Ocean.

Description: This study was supported by National Science Foundation Grant OCE-1947290.

Description: 2022-01-13

Description: Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 50(9), (2020): 2669-2688, doi:10.1175/JPO-D-19-0077.1

Description: The scale-dependent variance of tracer properties in the ocean bears the imprint of the oceanic eddy field. Anomalies in spice (which combines anomalies in temperature T and salinity S on isopycnal surfaces) act as passive tracers beneath the surface mixed layer (ML). We present an analysis of spice distributions along isopycnals in the upper 200 m of the ocean, calculated with over 9000 vertical profiles of T and S measured along ~4800 km of ship tracks in the Bay of Bengal. The data are from three separate research cruises—in the winter monsoon season of 2013 and in the late and early summer monsoon seasons of 2015 and 2018. We present a spectral analysis of horizontal tracer variance statistics on scales ranging from the submesoscale (~1 km) to the mesoscale (~100 km). Isopycnal layers that are closer to the ML-base exhibit redder spectra of tracer variance at scales ≲10 km than is predicted by theories of quasigeostrophic turbulence or frontogenesis. Two plausible explanations are postulated. The first is that stirring by submesoscale motions and shear dispersion by near-inertial waves enhance effective horizontal mixing and deplete tracer variance at horizontal scales ≲10 km in this region. The second is that the spice anomalies are coherent with dynamical properties such as potential vorticity, and not interpretable as passively stirred.

Description: We are grateful to the captain and crew of the R/V Roger Revelle and the R/V Thomas G. Thompson, and all ASIRI-OMM and MISO-BOB scientists. We thank Prof. Andrew Thompson and an anonymous reviewer for suggestions that improved the manuscript. This work was carried out under the Office of Naval Research’s Air-Sea Interaction Regional Initiative (ASIRI) and Monsoon Intra-Seasonal Oscillations in the Bay of Bengal (MISO-BOB) research initiatives, in collaboration with the Indian Ministry of Earth Science’s Ocean Mixing and Monsoons (OMM) initiative supported by the Monsoon Mission. Support came from ONR Grants N00014-16-1-2470, N00014-13-1-0451, N00014-17-1-2390 (G.S.J. and A.M.), N00014-14-1-0455 (J.M. and J.N), N00014-17-1-2511 (J.M.), N00014-13-1-0489, N00014-17-1-2391 (A.L.), N00014-15-1-2634 (E.S.), N00014-13-1-0456, N00014-17-1-2355 (A.T.), and N00014-13-1-0453, N00014-17-1-2880 (J.F.).

Description: 2021-02-28

Description: Author Posting. © American Meteorological Society, 2019. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 49(7), (2019): 1889-1904, doi:10.1175/JPO-D-19-0053.1.

Description: A high-resolution numerical model, together with in situ and satellite observations, is used to explore the nature and dynamics of the dominant high-frequency (from one day to one week) variability in Denmark Strait. Mooring measurements in the center of the strait reveal that warm water “flooding events” occur, whereby the North Icelandic Irminger Current (NIIC) propagates offshore and advects subtropical-origin water northward through the deepest part of the sill. Two other types of mesoscale processes in Denmark Strait have been described previously in the literature, known as “boluses” and “pulses,” associated with a raising and lowering of the overflow water interface. Our measurements reveal that flooding events occur in conjunction with especially pronounced pulses. The model indicates that the NIIC hydrographic front is maintained by a balance between frontogenesis by the large-scale flow and frontolysis by baroclinic instability. Specifically, the temperature and salinity tendency equations demonstrate that the eddies act to relax the front, while the mean flow acts to sharpen it. Furthermore, the model reveals that the two dense water processes—boluses and pulses (and hence flooding events)—are dynamically related to each other and tied to the meandering of the hydrographic front in the strait. Our study thus provides a general framework for interpreting the short-time-scale variability of Denmark Strait Overflow Water entering the Irminger Sea.

Description: MAS was supported by the National Science Foundation (NSF) under Grants OCE-1558742 and OCE-1534618. RSP, PL, and DM were supported by NSF under Grants OCE-1558742 and OCE-1259618. WJvA was supported by the Helmholtz Infrastructure Initiative FRAM. TWNH and MA were supported by NSF under Grants OCE-1633124 and OCE-118123.

Description: 2020-07-01

Description: Author Posting. © American Meteorological Society, 2018. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 48 (2018): 643-646, doi:10.1175/JPO-D-17-0240.1.

Description: A simple oceanic model is presented for source–sink flow on the β plane to discuss the pathways from source to sink when transport boundary layers have large enough Reynolds numbers to be inertial in their dynamics. A representation of the flow as a Fofonoff gyre, suggested by prior work on inertial boundary layers and eddy-driven circulations in two-dimensional turbulent flows, indicates that even when the source and sink are aligned along the same western boundary the flow must intrude deep into the interior before exiting at the sink. The existence of interior pathways for the flow is thus an intrinsic property of an inertial circulation and is not dependent on particular geographical basin geometry.

Description: 2018-09-12

Description: Author Posting. © American Meteorological Society, 2018. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 31 (2018): 4847-4863, doi:10.1175/JCLI-D-17-0802.1.

Description: The sensitivity of sea ice to the temperature of inflowing Atlantic water across the Greenland–Scotland Ridge is investigated using an eddy-resolving configuration of the Massachusetts Institute of Technology General Circulation Model with idealized topography. During the last glacial period, when climate on Greenland is known to have been extremely unstable, sea ice is thought to have covered the Nordic seas. The dramatic excursions in climate during this period, seen as large abrupt warming events on Greenland and known as Dansgaard–Oeschger (DO) events, are proposed to have been caused by a rapid retreat of Nordic seas sea ice. Here, we show that a full sea ice cover and Arctic-like stratification can exist in the Nordic seas given a sufficiently cold Atlantic inflow and corresponding low transport of heat across the Greenland–Scotland Ridge. Once sea ice is established, continued sea ice formation and melt efficiently freshens the surface ocean and makes the deeper layers more saline. This creates a strong salinity stratification in the Nordic seas, similar to today’s Arctic Ocean, with a cold fresh surface layer protecting the overlying sea ice from the warm Atlantic water below. There is a nonlinear response in Nordic seas sea ice to Atlantic water temperature with simulated large abrupt changes in sea ice given small changes in inflowing temperature. This suggests that the DO events were more likely to have occurred during periods of reduced warm Atlantic water inflow to the Nordic seas.

Description: The research was supported by the Centre for Climate Dynamics at the Bjerknes Centre for Climate Research. The research leading to these results is part of the ice2ice project funded by the European Research Council under the European Community Seventh Framework Programme (FP7/2007-2013)/ERC Grant Agreement 610055.

Description: Author Posting. © American Meteorological Society, 2018. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 48 (2018): 163-174, doi:10.1175/JPO-D-17-0161.1.

Description: The general problem of exchange from a shallow shelf across sharp topography to the deep ocean forced by narrow, cross-shelf wind jets is studied using quasigeostrophic theory and an idealized primitive equation numerical model. Interest is motivated by katabatic winds that emanate from narrow fjords in southeast Greenland, although similar topographically constrained wind jets are found throughout the world’s oceans. Because there is no net vorticity input by the wind, the circulation is largely confined to the region near the forcing. Circulation over the shelf is limited by bottom friction for weakly stratified flows, but stratification allows for much stronger upper-layer flows that are regulated by weak coupling to the lower layer. Over the sloping topography, the topographic beta effect limits the deep flow, while, for sufficient stratification, the upper-layer flow can cross the topography to connect the shelf to the open ocean. This can be an effective transport mechanism even for short, strong wind events because damping of the upper-layer flow is weak. A variety of transients are generated for an abrupt onset of winds, including short topography Rossby waves, long topographic Rossby waves, and inertial waves. Using parameters representative of southeast Greenland, katabatic wind events will force an offshore transport of O(0.4) Sv (1 Sv ≡ 106 m3 s−1) that, when considered for 2 days, will result in an offshore flux of O(5 × 1010) m3.

Description: MAS was supported by the National Science Foundation under Grant OCE-1533170.

Description: 2018-07-18

Description: Author Posting. © American Meteorological Society, 2018. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 48 (2018): 1375-1384, doi:10.1175/JPO-D-17-0266.1.

Description: The relationship between net mixing and the estuarine exchange flow may be quantified using a salinity variance budget. Here “mixing” is defined as the rate of destruction of volume-integrated salinity variance, and the exchange flow is quantified using the total exchange flow. These concepts are explored using an idealized 3D model estuary. It is shown that in steady state (e.g., averaging over the spring–neap cycle) the volume-integrated mixing is approximately given by Mixing ≅ SinSoutQr, where Sin and Sout are the representative salinities of in- and outflowing layers at the mouth and Qr is the river volume flux. This relationship provides an extension of the familiar Knudsen relation, in which the exchange flow is diagnosed based on knowledge of these same three quantities, quantitatively linking mixing to the exchange flow.

Description: The work was supported by the National Science Foundation through Grants OCE-1736242 to PM and OCE-1736539 to WRG and by the German Research Foundation through Grants TRR 181 and GRK 2000 to HB.

Description: Author Posting. © American Meteorological Society, 2017. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 30 (2017): 1739-1751, doi:10.1175/JCLI-D-16-0200.1.

Description: The Indian Ocean has sustained robust surface warming in recent decades, but the role of multidecadal variability remains unclear. Using ocean model hindcasts, characteristics of low-frequency Indian Ocean temperature variations are explored. Simulated upper-ocean temperature changes across the Indian Ocean in the hindcast are consistent with those recorded in observational products and ocean reanalyses. Indian Ocean temperatures exhibit strong warming trends since the 1950s limited to the surface and south of 30°S, while extensive subsurface cooling occurs over much of the tropical Indian Ocean. Previous work focused on diagnosing causes of these long-term trends in the Indian Ocean over the second half of the twentieth century. Instead, the temporal evolution of Indian Ocean subsurface heat content is shown here to reveal distinct multidecadal variations associated with the Pacific decadal oscillation, and the long-term trends are thus interpreted to result from aliasing of the low-frequency variability. Transmission of the multidecadal signal occurs via an oceanic pathway through the Indonesian Throughflow and is manifest across the Indian Ocean centered along 12°S as westward-propagating Rossby waves modulating thermocline and subsurface heat content variations. Resulting low-frequency changes in the eastern Indian Ocean thermocline depth are associated with decadal variations in the frequency of Indian Ocean dipole (IOD) events, with positive IOD events unusually common in the 1960s and 1990s with a relatively shallow thermocline. In contrast, the deeper thermocline depth in the 1970s and 1980s is associated with frequent negative IOD and rare positive IOD events. Changes in Pacific wind forcing in recent decades and associated rapid increases in Indian Ocean subsurface heat content can thus affect the basin’s leading mode of variability, with implications for regional climate and vulnerable societies in surrounding countries.

Description: This research was supported by a Research Fellowship by the Alexander von Humboldt Foundation, as well as the Ocean Climate Change Institute and the Investment in Science Fund at WHOI.

Description: 2017-08-15

Description: Author Posting. © American Meteorological Society, 2017. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 47 (2017): 2251-2265, doi:10.1175/JPO-D-17-0042.1.

Description: The problem of localized dense water formation over a sloping bottom is considered for the general case in which the topography forms a closed contour. This class of problems is motivated by topography around islands or shallow shoals in which convection resulting from brine rejection or surface heat loss reaches the bottom. The focus of this study is on the large-scale circulation that is forced far from the region of surface forcing. The authors find that a cyclonic current is generated around the topography, in the opposite sense to the propagation of the dense water plume. In physical terms, this current results from the propagation of low sea surface height from the region of dense water formation anticyclonically along the topographic contours back to the formation region. This pressure gradient is then balanced by a cyclonic geostrophic flow. This basic structure is well predicted by a linear quasigeostrophic theory, a primitive equation model, and in rotating tank experiments. For sufficiently strong forcing, the anticyclonic circulation of the dense plume meets this cyclonic circulation to produce a sharp front and offshore advection of dense water at the bottom and buoyant water at the surface. This nonlinear limit is demonstrated in both the primitive equation model and in the tank experiments.

Description: MAS was supported by the National Science Foundation under Grant OCE-1534618. Support for CC was given by the WHOI Ocean Climate Change Institute Proposal 27071273.

Description: 2018-03-20

Description: Author Posting. © American Meteorological Society, 2016. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 29 (2016): 8317-8331, doi:10.1175/JCLI-D-16-0109.1.

Description: A simple analytic model is developed to represent the offshore decay of cold sea surface temperature (SST) signals that originate from wind-driven upwelling at a coastal boundary. The model couples an oceanic mixed layer to an atmospheric boundary layer through wind stress and air–sea heat exchange. The primary mechanism that controls SST is a balance between Ekman advection and air–sea exchange. The offshore penetration of the cold SST signal decays exponentially with a length scale that is the product of the ocean Ekman velocity and a time scale derived from the air–sea heat flux and the radiative balance in the atmospheric boundary layer. This cold SST signal imprints on the atmosphere in terms of both the boundary layer temperature and surface wind. Nonlinearities due to the feedback between SST and atmospheric wind, baroclinic instability, and thermal wind in the atmospheric boundary layer all slightly modify this linear theory. The decay scales diagnosed from two-dimensional and three-dimensional eddy-resolving numerical ocean models are in close agreement with the theory, demonstrating that the basic physics represented by the theory remain dominant even in these more complete systems. Analysis of climatological SST off the west coast of the United States also shows a decay of the cold SST anomaly with scale roughly in agreement with the theory.

Description: MASwas supported by the Andrew W. Mellon Foundation Endowed Fund for Innovative Research and the National Science Foundation under Grant OCE-1433170 and PLR-1415489. NS was supported by the National Aeronautics and Space Administration under Grant NNX14AL83G, the Department of Energy, Office of Science Grant DE-SC0006766, and the Japan Agency for Marine-Earth Science and Technology as part of the JAMSTEC-IPRC Joint Investigations.

Description: 2017-05-03

Description: At a biogenomics meeting in Washington, D.C., last week, researchers publicly unveiled the Earth BioGenome Project (EBP). The audacious goal of the still-unfunded effort is to decipher the genomes of every species, starting with the 1.5 million named eukaryotes—the group of organisms that includes all plants, animals, and single-celled organisms such as amoebas. Researchers drew parallels to the Human Genome Project, which also began as an ambitious, controversial, and technically daunting proposal. The EBP would focus on the natural world, providing a better understanding of biodiversity by first sequencing in great detail the DNA of a member of each eukaryotic family (about 9000 in all) and eventually generating coarser genomes for the other eukaryotes. Although many biologists are excited about these goals, they point out that significant challenges lie ahead, including funding, sample collection, and broadening international participation. Author: Elizabeth Pennisi

Description: Author Posting. © American Meteorological Society, 2015. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 45 (2015): 2913–2932, doi:10.1175/JPO-D-14-0179.1.

Description: The oceanic deep circulation is shared between concentrated deep western boundary currents (DWBCs) and broader interior pathways, a process that is sensitive to seafloor topography. This study investigates the spreading and deepening of Denmark Strait overflow water (DSOW) in the western subpolar North Atlantic using two ° eddy-resolving Atlantic simulations, including a passive tracer injected into the DSOW. The deepest layers of DSOW transit from a narrow DWBC in the southern Irminger Sea into widespread westward flow across the central Labrador Sea, which remerges along the Labrador coast. This abyssal circulation, in contrast to the upper levels of overflow water that remain as a boundary current, blankets the deep Labrador Sea with DSOW. Farther downstream after being steered around the abrupt topography of Orphan Knoll, DSOW again leaves the boundary, forming cyclonic recirculation cells in the deep Newfoundland basin. The deep recirculation, mostly driven by the meandering pathway of the upper North Atlantic Current, leads to accumulation of tracer offshore of Orphan Knoll, precisely where a local maximum of chlorofluorocarbon (CFC) inventory is observed. At Flemish Cap, eddy fluxes carry ~20% of the tracer transport from the boundary current into the interior. Potential vorticity is conserved as the flow of DSOW broadens at the transition from steep to less steep continental rise into the Labrador Sea, while around the abrupt topography of Orphan Knoll, potential vorticity is not conserved and the DSOW deepens significantly.

Description: This work is supported by ONR Award N00014-09-1-0587, the NSF Physical Oceanography Program, and NASA Ocean Surface Topography Science Team Program.

Description: 2016-06-01

Description: Author Posting. © American Meteorological Society, 2016. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 46 (2016): 361–367, doi:10.1175/JPO-D-15-0171.1.

Description: Idealized laboratory experiments investigate the glacier–ocean boundary dynamics near a vertical glacier in a two-layer stratified fluid. Discharge of meltwater runoff at the base of the glacier (subglacial discharge) enhances submarine melting. In the laboratory, the effect of multiple sources of subglacial discharge is simulated by introducing freshwater at freezing temperature from two point sources at the base of an ice block representing the glacier. The buoyant plumes of cold meltwater and subglacial discharge water entrain warm ambient water, rise vertically, and interact within a layer of depth H2 if the distance between the sources x0 is smaller than H2α/0.35, where α is the entrainment constant. The plume water detaches from the glacier face at the interface between the two layers and/or at the free surface, as confirmed by previous numerical studies and field observations. A plume model is used to explain the observed nonmonotonic dependence of submarine melting on the sources’ separation. The distance between the two sources influences the entrainment of warm water in the plumes and consequently the amount of submarine melting and the final location of the meltwater within the water column. Two interacting plumes located very close together are observed to melt approximately half as much as two independent plumes. The inclusion, or parameterization, of the dynamics regulating multiple plumes’ interaction is therefore necessary for a correct estimate of submarine melting. Hence, the distribution and number of sources of subglacial discharge may play an important role in glacial melt rates and fjord stratification and circulation.

Description: Support to C.C. was given by the NSF Project OCE-1130008 and OCE-1434041. V.M.G. received support from the “Gori” Fellowship.

Description: 2016-07-01

Description: Author Posting. © American Meteorological Society, 2016. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 46 (2016): 1277-1284, doi:10.1175/JPO-D-16-0027.1.

Description: The contemporary Arctic Ocean differs markedly from midlatitude, ice-free, and relatively warm oceans in the context of density-compensating temperature and salinity variations. These variations are invaluable tracers in the midlatitudes, revealing essential fundamental physical processes of the oceans, on scales from millimeters to thousands of kilometers. However, in the cold Arctic Ocean, temperature variations have little effect on density, and a measure of density-compensating variations in temperature and salinity (i.e., spiciness) is not appropriate. In general, temperature is simply a passive tracer, which implies that most of the heat transported in the Arctic Ocean relies entirely on the ocean dynamics determined by the salinity field. It is shown, however, that as the Arctic Ocean warms up, temperature will take on a new role in setting dynamical balances. Under continued warming, there exists the possibility for a regime shift in the mechanisms by which heat is transported in the Arctic Ocean. This may result in a cap on the storage of deep-ocean heat, having profound implications for future predictions of Arctic sea ice.

Description: Support was provided by the National Science Foundation Division of Polar Programs Award 1350046 and Office of Naval Research Grant Number N00014-12-1-0110.

Description: 2016-10-05

Description: Chromosome-long haplotyping of human genomes is important to identify genetic variants with differing gene expression, in human evolution studies, clinical diagnosis, and other biological and medical fields. Although several methods have realized haplotyping based on sequencing technologies or population statistics, accuracy and cost are factors that prohibit their wide use. Borrowing ideas from group testing theories, we proposed a clone-based haplotyping method by overlapping pool sequencing. The clones from a single individual were pooled combinatorially and then sequenced. According to the distinct pooling pattern for each clone in the overlapping pool sequencing, alleles for the recovered variants could be assigned to their original clones precisely. Subsequently, the clone sequences could be reconstructed by linking these alleles accordingly and assembling them into haplotypes with high accuracy. To verify the utility of our method, we constructed 130 110 clones in silico for the individual NA12878 and simulated the pooling and sequencing process. Ultimately, 99.9% of variants on chromosome 1 that were covered by clones from both parental chromosomes were recovered correctly, and 112 haplotype contigs were assembled with an N50 length of 3.4 Mb and no switch errors. A comparison with current clone-based haplotyping methods indicated our method was more accurate.

Description: Sexual differentiation of malaria parasites into gametocytes in the vertebrate host and subsequent gamete fertilization in mosquitoes is essential for the spreading of the disease. The molecular processes orchestrating these transitions are far from fully understood. Here, we report the first transcriptome analysis of male and female Plasmodium falciparum gametocytes coupled with a comprehensive proteome analysis. In male gametocytes there is an enrichment of proteins involved in the formation of flagellated gametes; proteins involved in DNA replication, chromatin organization and axoneme formation. On the other hand, female gametocytes are enriched in proteins required for zygote formation and functions after fertilization; protein-, lipid- and energy-metabolism. Integration of transcriptome and proteome data revealed 512 highly expressed maternal transcripts without corresponding protein expression indicating large scale translational repression in P. falciparum female gametocytes for the first time. Despite a high degree of conservation between Plasmodium species, 260 of these ‘repressed transcripts’ have not been previously described. Moreover, for some of these genes, protein expression is only reported in oocysts and sporozoites indicating that repressed transcripts can be partitioned into short- and long-term storage. Finally, these data sets provide an essential resource for identification of vaccine/drug targets and for further mechanistic studies.

Description: Identifying large-scale structural variation in cancer genomes continues to be a challenge to researchers. Current methods rely on genome alignments based on a reference that can be a poor fit to highly variant and complex tumor genomes. To address this challenge we developed a method that uses available breakpoint information to generate models of structural variations. We use these models as references to align previously unmapped and discordant reads from a genome. By using these models to align unmapped reads, we show that our method can help to identify large-scale variations that have been previously missed.

Description: Advanced sequencing technologies have generated a plethora of data for many chromatin marks in multiple tissues and cell types, yet there is lack of a generalized tool for optimal utility of those data. A major challenge is to quantitatively model the epigenetic dynamics across both the genome and many cell types for understanding their impacts on differential gene regulation and disease. We introduce IDEAS, an i ntegrative and d iscriminative e pigenome a nnotation s ystem, for jointly characterizing epigenetic landscapes in many cell types and detecting differential regulatory regions. A key distinction between our method and existing state-of-the-art algorithms is that IDEAS integrates epigenomes of many cell types simultaneously in a way that preserves the position-dependent and cell type-specific information at fine scales, thereby greatly improving segmentation accuracy and producing comparable annotations across cell types.

Description: Early data-sharing efforts have led to improved variant interpretation and development of treatments for rare diseases and some cancer types (1–3). However, such benefits will only be available to the general population if researchers and clinicians can access and make comparisons across data from millions of individuals. Author:

Description: A majority of large-scale bacterial genome rearrangements involve mobile genetic elements such as insertion sequence (IS) elements. Here we report novel insertions and excisions of IS elements and recombination between homologous IS elements identified in a large collection of Escherichia coli mutation accumulation lines by analysis of whole genome shotgun sequencing data. Based on 857 identified events (758 IS insertions, 98 recombinations and 1 excision), we estimate that the rate of IS insertion is 3.5 x 10 –4 insertions per genome per generation and the rate of IS homologous recombination is 4.5 x 10 –5 recombinations per genome per generation. These events are mostly contributed by the IS elements IS 1 , IS 2 , IS 5 and IS 186 . Spatial analysis of new insertions suggest that transposition is biased to proximal insertions, and the length spectrum of IS-caused deletions is largely explained by local hopping. For any of the ISs studied there is no region of the circular genome that is favored or disfavored for new insertions but there are notable hotspots for deletions. Some elements have preferences for non-coding sequence or for the beginning and end of coding regions, largely explained by target site motifs. Interestingly, transposition and deletion rates remain constant across the wild-type and 12 mutant E. coli lines, each deficient in a distinct DNA repair pathway. Finally, we characterized the target sites of four IS families, confirming previous results and characterizing a highly specific pattern at IS 186 target-sites, 5'-GGGG(N6/N7)CCCC-3'. We also detected 48 long deletions not involving IS elements.

Description: Genetic variants in or near miRNA genes can have profound effects on miRNA expression and targeting. As user-friendly software for the impact prediction of miRNA variants on a large scale is still lacking, we created a tool called miRVaS. miRVaS automates this prediction by annotating the location of the variant relative to functional regions within the miRNA hairpin (seed, mature, loop, hairpin arm, flanks) and by annotating all predicted structural changes within the miRNA due to the variant. In addition, the tool defines the most important region that is predicted to have structural changes and calculates a conservation score that is indicative of the reliability of the structure prediction. The output is presented in a tab-separated file, which enables fast screening, and in an html file, which allows visual comparison between wild-type and variant structures. All separate images are provided for downstream use. Finally, we tested two different approaches on a small test set of published functionally validated genetic variants for their capacity to predict the impact of variants on miRNA expression.

Description: Analysis of RNA-seq data often detects numerous ‘non-co-linear’ (NCL) transcripts, which comprised sequence segments that are topologically inconsistent with their corresponding DNA sequences in the reference genome. However, detection of NCL transcripts involves two major challenges: removal of false positives arising from alignment artifacts and discrimination between different types of NCL transcripts ( trans -spliced, circular or fusion transcripts). Here, we developed a new NCL-transcript-detecting method (‘NCLscan’), which utilized a stepwise alignment strategy to almost completely eliminate false calls (〉98% precision) without sacrificing true positives, enabling NCLscan outperform 18 other publicly-available tools (including fusion- and circular-RNA-detecting tools) in terms of sensitivity and precision, regardless of the generation strategy of simulated dataset, type of intragenic or intergenic NCL event, read depth of coverage, read length or expression level of NCL transcript. With the high accuracy, NCLscan was applied to distinguishing between trans -spliced, circular and fusion transcripts on the basis of poly(A)- and nonpoly(A)-selected RNA-seq data. We showed that circular RNAs were expressed more ubiquitously, more abundantly and less cell type-specifically than trans -spliced and fusion transcripts. Our study thus describes a robust pipeline for the discovery of NCL transcripts, and sheds light on the fundamental biology of these non-canonical RNA events in human transcriptome.

Description: Visualization of chromosomal dynamics is important for understanding many fundamental intra-nuclear processes. Efficient and reliable live-cell multicolor labeling of chromosomal loci can realize this goal. However, the current methods are constrained mainly by insufficient labeling throughput, efficiency, flexibility as well as photostability. Here we have developed a new approach to realize dual-color chromosomal loci imaging based on a modified single-guide RNA (sgRNA) of the CRISPR/Cas9 system. The modification of sgRNA was optimized by structure-guided engineering of the original sgRNA, consisting of RNA aptamer insertions that bind fluorescent protein-tagged effectors. By labeling and tracking telomeres, centromeres and genomic loci, we demonstrate that the new approach is easy to implement and enables robust dual-color imaging of genomic elements. Importantly, our data also indicate that the fast exchange rate of RNA aptamer binding effectors makes our sgRNA-based labeling method much more tolerant to photobleaching than the Cas9-based labeling method. This is crucial for continuous, long-term tracking of chromosomal dynamics. Lastly, as our method is complementary to other live-cell genomic labeling systems, it is therefore possible to combine them into a plentiful palette for the study of native chromatin organization and genome ultrastructure dynamics in living cells.

Description: Genetic research has moved rapidly since the publication of Richard Dawkins's The Selfish Gene 40 years ago. In the intervening years, we have come to realize that many of the most interesting and important phenomena in human biology are not caused by any single gene. Citing a wealth of recent research that explores the ways genes work together to produce complex biological processes, Itai Yanai and Martin Lercher argue that it is time to embrace a new, more holistic, metaphor in their book, The Society of Genes. Author: Joseph Swift

Description: Ecological character displacement is a process of morphological divergence that reduces competition for limited resources. We used genomic analysis to investigate the genetic basis of a documented character displacement event in Darwin's finches on Daphne Major in the Galapagos Islands: The medium ground finch diverged from its competitor, the large ground finch, during a severe drought. We discovered a genomic region containing the HMGA2 gene that varies systematically among Darwin's finch species with different beak sizes. Two haplotypes that diverged early in the radiation were involved in the character displacement event: Genotypes associated with large beak size were at a strong selective disadvantage in medium ground finches (selection coefficient s = 0.59). Thus, a major locus has apparently facilitated a rapid ecological diversification in the adaptive radiation of Darwin's finches.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Lamichhaney, Sangeet -- Han, Fan -- Berglund, Jonas -- Wang, Chao -- Almen, Markus Sallman -- Webster, Matthew T -- Grant, B Rosemary -- Grant, Peter R -- Andersson, Leif -- New York, N.Y. -- Science. 2016 Apr 22;352(6284):470-4. doi: 10.1126/science.aad8786.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden. ; Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA. ; Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden. Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden. Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA. leif.andersson@imbim.uu.se.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/27102486" target="_blank"〉PubMed〈/a〉

Description: To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Tirosh, Itay -- Izar, Benjamin -- Prakadan, Sanjay M -- Wadsworth, Marc H 2nd -- Treacy, Daniel -- Trombetta, John J -- Rotem, Asaf -- Rodman, Christopher -- Lian, Christine -- Murphy, George -- Fallahi-Sichani, Mohammad -- Dutton-Regester, Ken -- Lin, Jia-Ren -- Cohen, Ofir -- Shah, Parin -- Lu, Diana -- Genshaft, Alex S -- Hughes, Travis K -- Ziegler, Carly G K -- Kazer, Samuel W -- Gaillard, Aleth -- Kolb, Kellie E -- Villani, Alexandra-Chloe -- Johannessen, Cory M -- Andreev, Aleksandr Y -- Van Allen, Eliezer M -- Bertagnolli, Monica -- Sorger, Peter K -- Sullivan, Ryan J -- Flaherty, Keith T -- Frederick, Dennie T -- Jane-Valbuena, Judit -- Yoon, Charles H -- Rozenblatt-Rosen, Orit -- Shalek, Alex K -- Regev, Aviv -- Garraway, Levi A -- 1U24CA180922/CA/NCI NIH HHS/ -- DP2 OD020839/OD/NIH HHS/ -- K99 CA194163/CA/NCI NIH HHS/ -- K99CA194163/CA/NCI NIH HHS/ -- P01CA163222/CA/NCI NIH HHS/ -- P30-CA14051/CA/NCI NIH HHS/ -- P50GM107618/GM/NIGMS NIH HHS/ -- R35CA197737/CA/NCI NIH HHS/ -- U54CA112962/CA/NCI NIH HHS/ -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2016 Apr 8;352(6282):189-96. doi: 10.1126/science.aad0501.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA. Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA 02215, USA. bizar@partners.org aregev@broadinstitute.org levi_garraway@dfci.harvard.edu. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA. Department of Chemistry, MIT, Cambridge, MA 02142, USA. Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA 02139, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA. Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA 02215, USA. ; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. ; Program in Therapeutic Sciences, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA. Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. ; HMS LINCS Center and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA. ; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA. Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA 02139, USA. Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA. ; Department of Surgical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA. Department of Surgical Oncology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. ; Program in Therapeutic Sciences, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. HMS LINCS Center and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA. Ludwig Center at Harvard, Boston, MA 02215, USA. ; Division of Medical Oncology, Massachusetts General Hospital Cancer Center, Boston, MA 02114, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA. Department of Chemistry, MIT, Cambridge, MA 02142, USA. Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA 02139, USA. Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA. Department of Immunology, Massachusetts General Hospital, Boston, MA 02114, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Department of Biology and Koch Institute, MIT, Boston, MA 02142, USA. Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA. bizar@partners.org aregev@broadinstitute.org levi_garraway@dfci.harvard.edu. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. bizar@partners.org aregev@broadinstitute.org levi_garraway@dfci.harvard.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/27124452" target="_blank"〉PubMed〈/a〉

Description: Accurate sequence and assembly of genomes is a critical first step for studies of genetic variation. We generated a high-quality assembly of the gorilla genome using single-molecule, real-time sequence technology and a string graph de novo assembly algorithm. The new assembly improves contiguity by two to three orders of magnitude with respect to previously released assemblies, recovering 87% of missing reference exons and incomplete gene models. Although regions of large, high-identity segmental duplications remain largely unresolved, this comprehensive assembly provides new biological insight into genetic diversity, structural variation, gene loss, and representation of repeat structures within the gorilla genome. The approach provides a path forward for the routine assembly of mammalian genomes at a level approaching that of the current quality of the human genome.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Gordon, David -- Huddleston, John -- Chaisson, Mark J P -- Hill, Christopher M -- Kronenberg, Zev N -- Munson, Katherine M -- Malig, Maika -- Raja, Archana -- Fiddes, Ian -- Hillier, LaDeana W -- Dunn, Christopher -- Baker, Carl -- Armstrong, Joel -- Diekhans, Mark -- Paten, Benedict -- Shendure, Jay -- Wilson, Richard K -- Haussler, David -- Chin, Chen-Shan -- Eichler, Evan E -- HG002385/HG/NHGRI NIH HHS/ -- HG003079/HG/NHGRI NIH HHS/ -- HG007234/HG/NHGRI NIH HHS/ -- HG007635/HG/NHGRI NIH HHS/ -- HG007990/HG/NHGRI NIH HHS/ -- R01 HG002385/HG/NHGRI NIH HHS/ -- U41 HG007635/HG/NHGRI NIH HHS/ -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2016 Apr 1;352(6281):aae0344. doi: 10.1126/science.aae0344.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA. Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA. ; Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA. ; Genomics Institute, University of California Santa Cruz and Howard Hughes Medical Institute, Santa Cruz, CA 95064, USA. ; McDonnell Genome Institute, Department of Medicine, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA. ; Pacific Biosciences of California, Menlo Park, CA 94025, USA. ; Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA. Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA. eee@gs.washington.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/27034376" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Pennisi, Elizabeth -- New York, N.Y. -- Science. 2016 Feb 19;351(6275):800-1. doi: 10.1126/science.351.6275.800. Epub 2016 Feb 18.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26912872" target="_blank"〉PubMed〈/a〉

Description: Presence of excess unaltered, wild-type (WT) DNA providing no information of biological or clinical value often masks rare alterations containing diagnostic or therapeutic clues in cancer, prenatal diagnosis, infectious diseases or organ transplantation. With the surge of high-throughput technologies there is a growing demand for removing unaltered DNA over large pools-of-sequences. Here we present nuclease-assisted minor-allele enrichment with probe-overlap (NaME-PrO), a single-step approach with broad genome coverage that can remove WT-DNA from numerous sequences simultaneously, prior to genomic analysis. NaME-PrO employs a double-strand-DNA-specific nuclease and overlapping oligonucleotide-probes interrogating WT-DNA targets and guiding nuclease digestion to these sites. Mutation-containing DNA creates probe-DNA mismatches that inhibit digestion, thus subsequent DNA-amplification magnifies DNA-alterations at all selected targets. We demonstrate several-hundred-fold mutation enrichment in diverse human samples on multiple clinically relevant targets including tumor samples and circulating DNA in 50-plex reactions. Enrichment enables routine mutation detection at 0.01% abundance while by adjusting conditions it is possible to sequence mutations down to 0.00003% abundance, or to scan tumor-suppressor genes for rare mutations. NaME-PrO introduces a simple and highly parallel process to remove un-informative DNA sequences and unmask clinically and biologically useful alterations.

Description: The RAG1/RAG2 endonuclease initiates V(D)J recombination at antigen receptor loci but also binds to thousands of places outside of these loci. RAG2 localizes directly to lysine 4 trimethylated histone 3 (H3K4me3) through a plant homeodomain (PHD) finger. The relative contribution of RAG2-dependent and RAG1-intrinsic mechanisms in determining RAG1 binding patterns is not known. Through analysis of deep RAG1 ChIP-seq data, we provide a quantitative description of the forces underlying genome-wide targeting of RAG1. Surprisingly, sequence-specific DNA binding contributes minimally to RAG1 targeting outside of antigen receptor loci. Instead, RAG1 binding is driven by two distinct modes of interaction with chromatin: the first is driven by H3K4me3, promoter-focused and dependent on the RAG2 PHD, and the second is defined by H3K27Ac, enhancer-focused and dependent on ‘non-core’ portions of RAG1. Based on this and additional chromatin and genomic features, we formulated a predictive model of RAG1 targeting to the genome. RAG1 binding sites predicted by our model correlate well with observed patterns of RAG1-mediated breaks in human pro-B acute lymphoblastic leukemia. Overall, this study provides an integrative model for RAG1 genome-wide binding and off-target activity and reveals a novel role for the RAG1 non-core region in RAG1 targeting.

Description: Author Posting. © American Meteorological Society, 2015. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 45 (2015): 1410–1425, doi:10.1175/JPO-D-14-0192.1.

Description: The west-to-east crossover of boundary currents has been seen in mean circulation schemes from several past models of the Red Sea. This study investigates the mechanisms that produce and control the crossover in an idealized, eddy-resolving numerical model of the Red Sea. The authors also review the observational evidence and derive an analytical estimate for the crossover latitude. The surface buoyancy loss increases northward in the idealized model, and the resultant mean circulation consists of an anticyclonic gyre in the south and a cyclonic gyre in the north. In the midbasin, the northward surface flow crosses from the western boundary to the eastern boundary. Numerical experiments with different parameters indicate that the crossover latitude of the boundary currents changes with f0, β, and the meridional gradient of surface buoyancy forcing. In the analytical estimate, which is based on quasigeostrophic, β-plane dynamics, the crossover is predicted to lie at the latitude where the net potential vorticity advection (including an eddy component) is zero. Various terms in the potential vorticity budget can be estimated using a buoyancy budget, a thermal wind balance, and a parameterization of baroclinic instability.

Description: This work is supported by Award USA 00002, KSA 00011, and KSA 00011/02 made by King Abdullah University of Science and Technology (KAUST), by National Science Foundation Grants OCE0927017, OCE1154641, and OCE85464100, and by the Woods Hole Oceanographic Institution Academic Program Office.

Description: 2015-11-01

Description: Author Posting. © American Meteorological Society, 2015. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 45 (2015): 1822–1842, doi:10.1175/JPO-D-14-0147.1.

Description: Influences of time-dependent precipitation on water mass transformation and heat budgets in an idealized marginal sea are examined using theoretical and numerical models. The equations proposed by Spall in 2012 are extended to cases with time-dependent precipitation whose form is either a step function or a sinusoidal function. The theory predicts the differences in temperature and salinity between the convective water and the boundary current as well as the magnitudes of heat fluxes into the marginal sea and across the sea surface. Moreover, the theory reveals that there are three inherent time scales: relaxation time scales for temperature and salinity and a precipitation time scale. The relaxation time scales are determined by a steady solution of the theoretical model with steady precipitation. The relaxation time scale for temperature is always smaller than that for salinity as a result of not only the difference in the form of fluxes at the surface but also the variation in the eddy transport from the boundary current. These three time scales and the precipitation amplitude determine the strength of the ocean response to changes in precipitation and the phase relation between precipitation, changes in salinity and temperature, and changes in heat fluxes. It is demonstrated that the theoretical predictions agree qualitatively well with results from the eddy-resolving numerical model. This demonstrates the fundamental role of mesoscale eddies in the ocean response to time-dependent forcing and provides a framework with which to assess the extent to which observed variability in marginal sea convection and water mass transformation are consistent with an external forcing by variations in precipitation.

Description: This work was initiated at the 2013 WHOI Geophysical Fluid Dynamics Summer Program, which was supported by the National Science Foundation and the Office of Naval Research. This work was also supported by Grant-in-Aid for Research Fellow (25·8466) of the Ministry of Education, Culture, Sports and Technology (MEXT), Japan, the Program for Leading Graduate Schools, MEXT, Japan (YY), and by the National Science Foundation Grant OCE-1232389 (MAS).

Description: 2016-01-01

Description: Author Posting. © American Meteorological Society, 2015. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 45 (2015): 2806–2819, doi:10.1175/JPO-D-15-0061.1.

Description: An eastward-flowing current of a homogeneous fluid with velocity U, contained in a channel of width L, impinges on an island of width of O(L), and the resulting interaction and dynamics are studied for values of the supercriticality parameter, b = βL2/U, both larger and smaller than π2. The former case is subcritical with respect to Rossby waves, and the latter is supercritical. The nature of the flow field depends strongly on b, and in particular, the nature of the flow around the island and the proportion of the flow passing to the north or south of the island are sensitive to b and to the position of the island in the channel. The problem is studied analytically in a relatively simple, nonlinear quasigeostrophic and adiabatic framework and numerically with a shallow-water model that allows a qualitative extension of the results to the equator. Although the issues involved are motivated by the interaction of the Equatorial Undercurrent and the Galapagos Islands, the analysis presented here focuses on the fundamental issue of the distinctive nature of the flow as a function of Rossby wave criticality.

Description: Supported by the National Science Foundation Grant OCE-0959381.

Description: 2016-05-01

Description: Clonal populations accumulate mutations over time, resulting in different haplotypes. Deep sequencing of such a population in principle provides information to reconstruct these haplotypes and the frequency at which the haplotypes occur. However, this reconstruction is technically not trivial, especially not in clonal systems with a relatively low mutation frequency. The low number of segregating sites in those systems adds ambiguity to the haplotype phasing and thus obviates the reconstruction of genome-wide haplotypes based on sequence overlap information. Therefore, we present EVORhA, a haplotype reconstruction method that complements phasing information in the non-empty read overlap with the frequency estimations of inferred local haplotypes. As was shown with simulated data, as soon as read lengths and/or mutation rates become restrictive for state-of-the-art methods, the use of this additional frequency information allows EVORhA to still reliably reconstruct genome-wide haplotypes. On real data, we show the applicability of the method in reconstructing the population composition of evolved bacterial populations and in decomposing mixed bacterial infections from clinical samples.

Description: Data on biological mechanisms of aging are mostly obtained from cross-sectional study designs. An inherent disadvantage of this design is that inter-individual differences can mask small but biologically significant age-dependent changes. A serially sampled design (same individual at different time points) would overcome this problem but is often limited by the relatively small numbers of available paired samples and the statistics being used. To overcome these limitations, we have developed a new vector-based approach, termed three-component analysis, which incorporates temporal distance, signal intensity and variance into one single score for gene ranking and is combined with gene set enrichment analysis. We tested our method on a unique age-based sample set of human skin fibroblasts and combined genome-wide transcription, DNA methylation and histone methylation (H3K4me3 and H3K27me3) data. Importantly, our method can now for the first time demonstrate a clear age-dependent decrease in expression of genes coding for proteins involved in translation and ribosome function. Using analogies with data from lower organisms, we propose a model where age-dependent down-regulation of protein translation-related components contributes to extend human lifespan.

Description: A major challenge in the field of shotgun metagenomics is the accurate identification of organisms present within a microbial community, based on classification of short sequence reads. Though existing microbial community profiling methods have attempted to rapidly classify the millions of reads output from modern sequencers, the combination of incomplete databases, similarity among otherwise divergent genomes, errors and biases in sequencing technologies, and the large volumes of sequencing data required for metagenome sequencing has led to unacceptably high false discovery rates (FDR). Here, we present the application of a novel, gene-independent and signature-based metagenomic taxonomic profiling method with significantly and consistently smaller FDR than any other available method. Our algorithm circumvents false positives using a series of non-redundant signature databases and examines G enomic O rigins T hrough T axonomic CHA llenge (GOTTCHA). GOTTCHA was tested and validated on 20 synthetic and mock datasets ranging in community composition and complexity, was applied successfully to data generated from spiked environmental and clinical samples, and robustly demonstrates superior performance compared with other available tools.

Description: With read lengths of currently up to 2 x 300 bp, high throughput and low sequencing costs Illumina's MiSeq is becoming one of the most utilized sequencing platforms worldwide. The platform is manageable and affordable even for smaller labs. This enables quick turnaround on a broad range of applications such as targeted gene sequencing, metagenomics, small genome sequencing and clinical molecular diagnostics. However, Illumina error profiles are still poorly understood and programs are therefore not designed for the idiosyncrasies of Illumina data. A better knowledge of the error patterns is essential for sequence analysis and vital if we are to draw valid conclusions. Studying true genetic variation in a population sample is fundamental for understanding diseases, evolution and origin. We conducted a large study on the error patterns for the MiSeq based on 16S rRNA amplicon sequencing data. We tested state-of-the-art library preparation methods for amplicon sequencing and showed that the library preparation method and the choice of primers are the most significant sources of bias and cause distinct error patterns. Furthermore we tested the efficiency of various error correction strategies and identified quality trimming (Sickle) combined with error correction (BayesHammer) followed by read overlapping (PANDAseq) as the most successful approach, reducing substitution error rates on average by 93%.

Description: RNA-seq is a sensitive and accurate technique to compare steady-state levels of RNA between different cellular states. However, as it does not provide an account of transcriptional activity per se , other technologies are needed to more precisely determine acute transcriptional responses. Here, we have developed an easy, sensitive and accurate novel computational method, iRNA-seq , for genome-wide assessment of transcriptional activity based on analysis of intron coverage from total RNA-seq data. Comparison of the results derived from iRNA-seq analyses with parallel results derived using current methods for genome-wide determination of transcriptional activity, i.e. global run-on (GRO)-seq and RNA polymerase II (RNAPII) ChIP-seq, demonstrate that iRNA-seq provides similar results in terms of number of regulated genes and their fold change. However, unlike the current methods that are all very labor-intensive and demanding in terms of sample material and technologies, iRNA-seq is cheap and easy and requires very little sample material. In conclusion, iRNA-seq offers an attractive novel alternative to current methods for determination of changes in transcriptional activity at a genome-wide level.

Description: Any given human individual carries multiple genetic variants that disrupt protein-coding genes, through structural variation, as well as nucleotide variants and indels. Predicting the phenotypic consequences of a gene disruption remains a significant challenge. Current approaches employ information from a range of biological networks to predict which human genes are haploinsufficient (meaning two copies are required for normal function) or essential (meaning at least one copy is required for viability). Using recently available study gene sets, we show that these approaches are strongly biased towards providing accurate predictions for well-studied genes. By contrast, we derive a haploinsufficiency score from a combination of unbiased large-scale high-throughput datasets, including gene co-expression and genetic variation in over 6000 human exomes. Our approach provides a haploinsufficiency prediction for over twice as many genes currently unassociated with papers listed in Pubmed as three commonly-used approaches, and outperforms these approaches for predicting haploinsufficiency for less-studied genes. We also show that fine-tuning the predictor on a set of well-studied ‘gold standard’ haploinsufficient genes does not improve the prediction for less-studied genes. This new score can readily be used to prioritize gene disruptions resulting from any genetic variant, including copy number variants, indels and single-nucleotide variants.

Description: Meta-analysis of gene expression has enabled numerous insights into biological systems, but current methods have several limitations. We developed a method to perform a meta-analysis using the elastic net, a powerful and versatile approach for classification and regression. To demonstrate the utility of our method, we conducted a meta-analysis of lung cancer gene expression based on publicly available data. Using 629 samples from five data sets, we trained a multinomial classifier to distinguish between four lung cancer subtypes. Our meta-analysis-derived classifier included 58 genes and achieved 91% accuracy on leave-one-study-out cross-validation and on three independent data sets. Our method makes meta-analysis of gene expression more systematic and expands the range of questions that a meta-analysis can be used to address. As the amount of publicly available gene expression data continues to grow, our method will be an effective tool to help distill these data into knowledge.

Description: Extensive and multi-dimensional data sets generated from recent cancer omics profiling projects have presented new challenges and opportunities for unraveling the complexity of cancer genome landscapes. In particular, distinguishing the unique complement of genes that drive tumorigenesis in each patient from a sea of passenger mutations is necessary for translating the full benefit of cancer genome sequencing into the clinic. We address this need by presenting a data integration framework (OncoIMPACT) to nominate patient-specific driver genes based on their phenotypic impact. Extensive in silico and in vitro validation helped establish OncoIMPACT's robustness, improved precision over competing approaches and verifiable patient and cell line specific predictions (2/2 and 6/7 true positives and negatives, respectively). In particular, we computationally predicted and experimentally validated the gene TRIM24 as a putative novel amplified driver in a melanoma patient. Applying OncoIMPACT to more than 1000 tumor samples, we generated patient-specific driver gene lists in five different cancer types to identify modes of synergistic action. We also provide the first demonstration that computationally derived driver mutation signatures can be overall superior to single gene and gene expression based signatures in enabling patient stratification and prognostication. Source code and executables for OncoIMPACT are freely available from http://sourceforge.net/projects/oncoimpact .

Description: Analysis of rewired upstream subnetworks impacting downstream differential gene expression aids the delineation of evolving molecular mechanisms. Cumulative statistics based on conventional differential correlation are limited for subnetwork rewiring analysis since rewiring is not necessarily equivalent to change in correlation coefficients. Here we present a computational method ChiNet to quantify subnetwork rewiring by statistical heterogeneity that enables detection of potential genotype changes causing altered transcription regulation in evolving organisms. Given a differentially expressed downstream gene set, ChiNet backtracks a rewired upstream subnetwork from a super-network including gene interactions known to occur under various molecular contexts. We benchmarked ChiNet for its high accuracy in distinguishing rewired artificial subnetworks, in silico yeast transcription-metabolic subnetworks, and rewired transcription subnetworks for Candida albicans versus Saccharomyces cerevisiae , against two differential-correlation based subnetwork rewiring approaches. Then, using transcriptome data from tolerant S. cerevisiae strain NRRL Y-50049 and a wild-type intolerant strain, ChiNet identified 44 metabolic pathways affected by rewired transcription subnetworks anchored to major adaptively activated transcription factor genes YAP1 , RPN4 , SFP1 and ROX1 , in response to toxic chemical challenges involved in lignocellulose-to-biofuels conversion. These findings support the use of ChiNet in rewiring analysis of subnetworks where differential interaction patterns resulting from divergent nonlinear dynamics abound.

Description: Ribosome biogenesis, a central and essential cellular process, occurs through sequential association and mutual co-folding of protein–RNA constituents in a well-defined assembly pathway. Here, we construct a network of co-evolving nucleotide/amino acid residues within the ribosome and demonstrate that assembly constraints are strong predictors of co-evolutionary patterns. Predictors of co-evolution include a wide spectrum of structural reconstitution events, such as cooperativity phenomenon, protein-induced rRNA reconstitutions, molecular packing of different rRNA domains, protein–rRNA recognition, etc. A correlation between folding rate of small globular proteins and their topological features is known. We have introduced an analogous topological characteristic for co-evolutionary network of ribosome, which allows us to differentiate between rRNA regions subjected to rapid reconstitutions from those hindered by kinetic traps. Furthermore, co-evolutionary patterns provide a biological basis for deleterious mutation sites and further allow prediction of potential antibiotic targeting sites. Understanding assembly pathways of multicomponent macromolecules remains a key challenge in biophysics. Our study provides a ‘proof of concept’ that directly relates co-evolution to biophysical interactions during multicomponent assembly and suggests predictive power to identify candidates for critical functional interactions as well as for assembly-blocking antibiotic target sites.

Description: The emergence of new sequencing technologies has facilitated the use of bacterial whole genome alignments for evolutionary studies and outbreak analyses. These datasets, of increasing size, often include examples of multiple different mechanisms of horizontal sequence transfer resulting in substantial alterations to prokaryotic chromosomes. The impact of these processes demands rapid and flexible approaches able to account for recombination when reconstructing isolates’ recent diversification. Gubbins is an iterative algorithm that uses spatial scanning statistics to identify loci containing elevated densities of base substitutions suggestive of horizontal sequence transfer while concurrently constructing a maximum likelihood phylogeny based on the putative point mutations outside these regions of high sequence diversity. Simulations demonstrate the algorithm generates highly accurate reconstructions under realistically parameterized models of bacterial evolution, and achieves convergence in only a few hours on alignments of hundreds of bacterial genome sequences. Gubbins is appropriate for reconstructing the recent evolutionary history of a variety of haploid genotype alignments, as it makes no assumptions about the underlying mechanism of recombination. The software is freely available for download at github.com/sanger-pathogens/Gubbins , implemented in Python and C and supported on Linux and Mac OS X.

Description: Genomic structural variation (SV), a common hallmark of cancer, has important predictive and therapeutic implications. However, accurately detecting SV using high-throughput sequencing data remains challenging, especially for ‘targeted’ resequencing efforts. This is critically important in the clinical setting where targeted resequencing is frequently being applied to rapidly assess clinically actionable mutations in tumor biopsies in a cost-effective manner. We present BreaKmer, a novel approach that uses a ‘kmer’ strategy to assemble misaligned sequence reads for predicting insertions, deletions, inversions, tandem duplications and translocations at base-pair resolution in targeted resequencing data. Variants are predicted by realigning an assembled consensus sequence created from sequence reads that were abnormally aligned to the reference genome. Using targeted resequencing data from tumor specimens with orthogonally validated SV, non-tumor samples and whole-genome sequencing data, BreaKmer had a 97.4% overall sensitivity for known events and predicted 17 positively validated, novel variants. Relative to four publically available algorithms, BreaKmer detected SV with increased sensitivity and limited calls in non-tumor samples, key features for variant analysis of tumor specimens in both the clinical and research settings.

Description: Detecting allelic biases from high-throughput sequencing data requires an approach that maximises sensitivity while minimizing false positives. Here, we present Allelome.PRO, an automated user-friendly bioinformatics pipeline, which uses high-throughput sequencing data from reciprocal crosses of two genetically distinct mouse strains to detect allele-specific expression and chromatin modifications. Allelome.PRO extends approaches used in previous studies that exclusively analyzed imprinted expression to give a complete picture of the ‘allelome’ by automatically categorising the allelic expression of all genes in a given cell type into imprinted, strain-biased, biallelic or non-informative. Allelome.PRO offers increased sensitivity to analyze lowly expressed transcripts, together with a robust false discovery rate empirically calculated from variation in the sequencing data. We used RNA-seq data from mouse embryonic fibroblasts from F1 reciprocal crosses to determine a biologically relevant allelic ratio cutoff, and define for the first time an entire allelome. Furthermore, we show that Allelome.PRO detects differential enrichment of H3K4me3 over promoters from ChIP-seq data validating the RNA-seq results. This approach can be easily extended to analyze histone marks of active enhancers, or transcription factor binding sites and therefore provides a powerful tool to identify candidate cis regulatory elements genome wide.

Description: Methods to interpret personal genome sequences are increasingly required. Here, we report a novel framework (EvoTol) to identify disease-causing genes using patient sequence data from within protein coding-regions. EvoTol quantifies a gene's intolerance to mutation using evolutionary conservation of protein sequences and can incorporate tissue-specific gene expression data. We apply this framework to the analysis of whole-exome sequence data in epilepsy and congenital heart disease, and demonstrate EvoTol's ability to identify known disease-causing genes is unmatched by competing methods. Application of EvoTol to the human interactome revealed networks enriched for genes intolerant to protein sequence variation, informing novel polygenic contributions to human disease.

Description: Detecting in vivo transcription factor (TF) binding is important for understanding gene regulatory circuitries. ChIP-seq is a powerful technique to empirically define TF binding in vivo . However, the multitude of distinct TFs makes genome-wide profiling for them all labor-intensive and costly. Algorithms for in silico prediction of TF binding have been developed, based mostly on histone modification or DNase I hypersensitivity data in conjunction with DNA motif and other genomic features. However, technical limitations of these methods prevent them from being applied broadly, especially in clinical settings. We conducted a comprehensive survey involving multiple cell lines, TFs, and methylation types and found that there are intimate relationships between TF binding and methylation level changes around the binding sites. Exploiting the connection between DNA methylation and TF binding, we proposed a novel supervised learning approach to predict TF–DNA interaction using data from base-resolution whole-genome methylation sequencing experiments. We devised beta-binomial models to characterize methylation data around TF binding sites and the background. Along with other static genomic features, we adopted a random forest framework to predict TF–DNA interaction. After conducting comprehensive tests, we saw that the proposed method accurately predicts TF binding and performs favorably versus competing methods.

Description: Monoclonal antibodies directed against cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), such as ipilimumab, yield considerable clinical benefit for patients with metastatic melanoma by inhibiting immune checkpoint activity, but clinical predictors of response to these therapies remain incompletely characterized. To investigate the roles of tumor-specific neoantigens and alterations in the tumor microenvironment in the response to ipilimumab, we analyzed whole exomes from pretreatment melanoma tumor biopsies and matching germline tissue samples from 110 patients. For 40 of these patients, we also obtained and analyzed transcriptome data from the pretreatment tumor samples. Overall mutational load, neoantigen load, and expression of cytolytic markers in the immune microenvironment were significantly associated with clinical benefit. However, no recurrent neoantigen peptide sequences predicted responder patient populations. Thus, detailed integrated molecular characterization of large patient cohorts may be needed to identify robust determinants of response and resistance to immune checkpoint inhibitors.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Van Allen, Eliezer M -- Miao, Diana -- Schilling, Bastian -- Shukla, Sachet A -- Blank, Christian -- Zimmer, Lisa -- Sucker, Antje -- Hillen, Uwe -- Foppen, Marnix H Geukes -- Goldinger, Simone M -- Utikal, Jochen -- Hassel, Jessica C -- Weide, Benjamin -- Kaehler, Katharina C -- Loquai, Carmen -- Mohr, Peter -- Gutzmer, Ralf -- Dummer, Reinhard -- Gabriel, Stacey -- Wu, Catherine J -- Schadendorf, Dirk -- Garraway, Levi A -- U54 HG003067/HG/NHGRI NIH HHS/ -- New York, N.Y. -- Science. 2015 Oct 9;350(6257):207-11. doi: 10.1126/science.aad0095. Epub 2015 Sep 10.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA 02215, USA. ; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. ; Department of Dermatology, University Hospital, University Duisburg-Essen, 45147 Essen, Germany. German Cancer Consortium(DKTK), 69121 Heidelberg, Germany. ; Department of Medical Oncology, Netherlands Cancer Institute, 1066 CX Amsterdam, Netherlands. ; Department of Dermatology, University Hospital Zurich, 8091 Zurich, Switzerland. ; Skin Cancer Unit, German Cancer Research Center(DKTK), 69121 Heidelberg, Germany. Skin Cancer Unit, German Cancer Research Center(DKTK), 69121 Heidelberg, Germany. Department of Dermatology, Venerology, and Allergology, University Medical Center, Ruprecht-Karls University of Heidelberg, 68167 Mannheim, Germany. ; Department of Dermatology, University Hospital, Ruprecht-Karls University of Heidelberg, 69120 Heidelberg, Germany. ; Department of Dermatology, University Hospital Tubingen, 72076 Tubingen, Germany. ; Department of Dermatology, University Hospital Kiel, 24105 Kiel, Germany. ; Department of Dermatology, University Medical Center, 55131 Mainz, Germany. ; Department of Dermatology, Elbe-Kliniken, 21614 Buxtehude, Germany. ; Department of Dermatology and Allergy, Skin Cancer Center Hannover, Hannover Medical School, 30625 Hannover, Germany. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. ; Department of Dermatology, University Hospital, University Duisburg-Essen, 45147 Essen, Germany. German Cancer Consortium(DKTK), 69121 Heidelberg, Germany. levi_garraway@dfci.harvard.edu dirk.schadendorf@uk-essen.de. ; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA 02215, USA. levi_garraway@dfci.harvard.edu dirk.schadendorf@uk-essen.de.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26359337" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Lloyd, K C Kent -- Meehan, Terry -- Beaudet, Arthur -- Murray, Steve -- Svenson, Karen -- McKerlie, Colin -- West, David -- Morse, Iva -- Parkinson, Helen -- Brown, Steve -- Mallon, Ann-Marie -- Moore, Mark -- U42 OD011175/OD/NIH HHS/ -- U42 OD011185/OD/NIH HHS/ -- U54 HG006332/HG/NHGRI NIH HHS/ -- U54 HG006364/HG/NHGRI NIH HHS/ -- New York, N.Y. -- Science. 2015 Jul 24;349(6246):390. doi: 10.1126/science.349.6246.390-a. Epub 2015 Jul 23.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉University of California, Davis, Davis, CA 95616, USA. kclloyd@ucdavis.edu. ; European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. ; Baylor College of Medicine, Houston, TX 77030, USA. ; The Jackson Laboratory, Bar Harbor, ME 04609, USA. ; Toronto Centre for Phenogenomics, Toronto, ON, M5T 3H7, Canada. ; Children's Hospital Oakland Research Institute, Oakland, CA 94609, USA. ; Charles River Laboratories, Wilmington, MA 01887, USA. ; Medical Research Council Harwell, Oxfordshire, OX11 0RD, UK. ; International Mouse Phenotyping Consortium, Hinxton, Cambridge, CB10 1SD, UK.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26206923" target="_blank"〉PubMed〈/a〉

Description: Large-scale genomic investigations have just begun to illuminate the molecular genetic contributions to major psychiatric illnesses, ranging from small-effect-size common variants to larger-effect-size rare mutations. The findings provide causal anchors from which to understand their neurobiological basis. Although these studies represent enormous success, they highlight major challenges reflected in the heterogeneity and polygenicity of all of these conditions and the difficulty of connecting multiple levels of molecular, cellular, and circuit functions to complex human behavior. Nevertheless, these advances place us on the threshold of a new frontier in the pathophysiological understanding, diagnosis, and treatment of psychiatric disease.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4694563/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4694563/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Geschwind, Daniel H -- Flint, Jonathan -- 5R01 MH094714/MH/NIMH NIH HHS/ -- 5R01 MH100027/MH/NIMH NIH HHS/ -- R01 MH094714/MH/NIMH NIH HHS/ -- R01 MH100027/MH/NIMH NIH HHS/ -- Wellcome Trust/United Kingdom -- New York, N.Y. -- Science. 2015 Sep 25;349(6255):1489-94. doi: 10.1126/science.aaa8954. Epub 2015 Sep 24.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Departments of Neurology, Psychiatry, and Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA. dhg@mednet.ucla jf@well.ox.ac.uk. ; Wellcome Trust Center for Human Genetics, University of Oxford, Oxford, UK. dhg@mednet.ucla jf@well.ox.ac.uk.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26404826" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Kaiser, Jocelyn -- New York, N.Y. -- Science. 2015 Sep 25;349(6255):1475. doi: 10.1126/science.349.6255.1475.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26404823" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4394183/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4394183/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Baltimore, David -- Berg, Paul -- Botchan, Michael -- Carroll, Dana -- Charo, R Alta -- Church, George -- Corn, Jacob E -- Daley, George Q -- Doudna, Jennifer A -- Fenner, Marsha -- Greely, Henry T -- Jinek, Martin -- Martin, G Steven -- Penhoet, Edward -- Puck, Jennifer -- Sternberg, Samuel H -- Weissman, Jonathan S -- Yamamoto, Keith R -- P50 HG005550/HG/NHGRI NIH HHS/ -- T32 GM066698/GM/NIGMS NIH HHS/ -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2015 Apr 3;348(6230):36-8. doi: 10.1126/science.aab1028. Epub 2015 Mar 19.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉California Institute of Technology, Mail Code 147-75, Pasadena, CA 91125, USA. ; Stanford University School of Medicine, 291 Campus Drive, Stanford, CA 94305, USA. ; University of California, Berkeley, 450 Li Ka Shing no. 3370, Berkeley, CA 94720-3370, USA. Innovative Genomics Initiative, University of California, Berkeley, 188 Li Ka Shing Center, Berkeley, CA 94720-3370, USA. ; Department of Biochemistry, University of Utah School of Medicine, 15 North Medical Drive East, Room 4100, Salt Lake City, UT 84112-5650, USA. ; Department of Medical History and Bioethics, School of Medicine and Public Health, University of Wisconsin Law School, 975 Bascom Mall, Madison, WI 53706, USA. ; Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA. ; Innovative Genomics Initiative, University of California, Berkeley, 188 Li Ka Shing Center, Berkeley, CA 94720-3370, USA. ; Boston Children's Hospital, 300 Longwood Avenue, Karp Family Building, 7th Floor, Boston, MA 02115, USA. Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, MD 20815, USA. ; Innovative Genomics Initiative, University of California, Berkeley, 188 Li Ka Shing Center, Berkeley, CA 94720-3370, USA. Departments of Molecular and Cell Biology and Chemistry, Howard Hughes Medical Institute, 731 Stanley Hall, MS 3220, University of California, Berkeley, Berkeley, CA 94720-3220, USA. doudna@berkeley.edu. ; Center for Law and the Biosciences, Crown Quadrangle 559 Nathan Abbott Way Stanford, CA 94305-8610, USA. ; Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland. ; Department of Molecular and Cell Biology, College of Letters and Science, University of California, Berkeley, 210K Durant Hall, Berkeley, CA 94720-2920, USA. ; Alta Partners, One Embarcadero Center, 37th Floor, San Francisco, CA 94111, USA. ; Department of Pediatrics UCSF School of Medicine, 513 Parnassus Avenue, San Francisco, CA 94143, USA. ; Department of Chemistry, 731 Stanley Hall, MS 3220, University of California, Berkeley, CA 94720-3220, USA. ; Innovative Genomics Initiative, University of California, Berkeley, 188 Li Ka Shing Center, Berkeley, CA 94720-3370, USA. Department of Cellular and Molecular Pharmacology, Howard Hughes Medical Institute, University of California, San Francisco, Byers Hall, 1700 4th Street, San Francisco, CA 94158-2330, USA. ; Innovative Genomics Initiative, University of California, Berkeley, 188 Li Ka Shing Center, Berkeley, CA 94720-3370, USA. UCSF School of Medicine, 600 16th Street, San Francisco, CA 94158, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25791083" target="_blank"〉PubMed〈/a〉

Description: How and when the Americas were populated remains contentious. Using ancient and modern genome-wide data, we found that the ancestors of all present-day Native Americans, including Athabascans and Amerindians, entered the Americas as a single migration wave from Siberia no earlier than 23 thousand years ago (ka) and after no more than an 8000-year isolation period in Beringia. After their arrival to the Americas, ancestral Native Americans diversified into two basal genetic branches around 13 ka, one that is now dispersed across North and South America and the other restricted to North America. Subsequent gene flow resulted in some Native Americans sharing ancestry with present-day East Asians (including Siberians) and, more distantly, Australo-Melanesians. Putative "Paleoamerican" relict populations, including the historical Mexican Pericues and South American Fuego-Patagonians, are not directly related to modern Australo-Melanesians as suggested by the Paleoamerican Model.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4733658/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4733658/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Raghavan, Maanasa -- Steinrucken, Matthias -- Harris, Kelley -- Schiffels, Stephan -- Rasmussen, Simon -- DeGiorgio, Michael -- Albrechtsen, Anders -- Valdiosera, Cristina -- Avila-Arcos, Maria C -- Malaspinas, Anna-Sapfo -- Eriksson, Anders -- Moltke, Ida -- Metspalu, Mait -- Homburger, Julian R -- Wall, Jeff -- Cornejo, Omar E -- Moreno-Mayar, J Victor -- Korneliussen, Thorfinn S -- Pierre, Tracey -- Rasmussen, Morten -- Campos, Paula F -- Damgaard, Peter de Barros -- Allentoft, Morten E -- Lindo, John -- Metspalu, Ene -- Rodriguez-Varela, Ricardo -- Mansilla, Josefina -- Henrickson, Celeste -- Seguin-Orlando, Andaine -- Malmstrom, Helena -- Stafford, Thomas Jr -- Shringarpure, Suyash S -- Moreno-Estrada, Andres -- Karmin, Monika -- Tambets, Kristiina -- Bergstrom, Anders -- Xue, Yali -- Warmuth, Vera -- Friend, Andrew D -- Singarayer, Joy -- Valdes, Paul -- Balloux, Francois -- Leboreiro, Ilan -- Vera, Jose Luis -- Rangel-Villalobos, Hector -- Pettener, Davide -- Luiselli, Donata -- Davis, Loren G -- Heyer, Evelyne -- Zollikofer, Christoph P E -- Ponce de Leon, Marcia S -- Smith, Colin I -- Grimes, Vaughan -- Pike, Kelly-Anne -- Deal, Michael -- Fuller, Benjamin T -- Arriaza, Bernardo -- Standen, Vivien -- Luz, Maria F -- Ricaut, Francois -- Guidon, Niede -- Osipova, Ludmila -- Voevoda, Mikhail I -- Posukh, Olga L -- Balanovsky, Oleg -- Lavryashina, Maria -- Bogunov, Yuri -- Khusnutdinova, Elza -- Gubina, Marina -- Balanovska, Elena -- Fedorova, Sardana -- Litvinov, Sergey -- Malyarchuk, Boris -- Derenko, Miroslava -- Mosher, M J -- Archer, David -- Cybulski, Jerome -- Petzelt, Barbara -- Mitchell, Joycelynn -- Worl, Rosita -- Norman, Paul J -- Parham, Peter -- Kemp, Brian M -- Kivisild, Toomas -- Tyler-Smith, Chris -- Sandhu, Manjinder S -- Crawford, Michael -- Villems, Richard -- Smith, David Glenn -- Waters, Michael R -- Goebel, Ted -- Johnson, John R -- Malhi, Ripan S -- Jakobsson, Mattias -- Meltzer, David J -- Manica, Andrea -- Durbin, Richard -- Bustamante, Carlos D -- Song, Yun S -- Nielsen, Rasmus -- Willerslev, Eske -- 098051/Wellcome Trust/United Kingdom -- 261213/European Research Council/International -- 2R01HG003229-09/HG/NHGRI NIH HHS/ -- BB/H005854/1/Biotechnology and Biological Sciences Research Council/United Kingdom -- R01-AI17892/AI/NIAID NIH HHS/ -- R01-GM094402/GM/NIGMS NIH HHS/ -- New York, N.Y. -- Science. 2015 Aug 21;349(6250):aab3884. doi: 10.1126/science.aab3884. Epub 2015 Jul 21.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. ; Computer Science Division, University of California, Berkeley, Berkeley, CA 94720, USA. Department of Statistics, University of California, Berkeley, Berkeley, CA 94720, USA. Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003, USA. ; Department of Mathematics, University of California, Berkeley, Berkeley, CA 94720, USA. ; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK. ; Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Building 208, 2800 Kongens Lyngby, Denmark. ; Departments of Biology and Statistics, Pennsylvania State University, 502 Wartik Laboratory, University Park, PA 16802, USA. ; The Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen, Denmark. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Department of Archaeology and History, La Trobe University, Melbourne, Victoria 3086, Australia. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Department of Genetics, School of Medicine, Stanford University, 300 Pasteur Drive, Lane Building, Room L331, Stanford, CA 94305, USA. ; Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK. Integrative Systems Biology Laboratory, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia. ; Estonian Biocentre, Evolutionary Biology Group, Tartu 51010, Estonia. Department of Evolutionary Biology, University of Tartu, Tartu 51010, Estonia. ; Department of Genetics, School of Medicine, Stanford University, 300 Pasteur Drive, Lane Building, Room L331, Stanford, CA 94305, USA. ; Institute for Human Genetics, University of California San Francisco, 513 Parnassus Avenue, San Francisco, CA 94143, USA. ; School of Biological Sciences, Washington State University, Post Office Box 644236, Heald 429, Pullman, WA 99164, USA. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Centro de Investigacion en Ciencias del Mar y Limnologia/Centro Interdisciplinar de Investigacao Marinha e Ambiental, Centro Interdisciplinar de Investigacao Marinha e Ambiental, Universidade do Porto, Rua dos Bragas 289, 4050-123 Porto, Portugal. ; Department of Anthropology, University of Illinois at Urbana-Champaign, 607 S. Mathews Avenue, Urbana, IL 61801, USA. ; Centro Mixto, Universidad Complutense de Madrid-Instituto de Salud Carlos III de Evolucion y Comportamiento Humano, Madrid, Spain. ; Instituto Nacional de Antropologia e Historia, Moneda 13, Centro, Cuauhtemoc, 06060 Mexico City, Mexico. ; University of Utah, Department of Anthropology, 270 S 1400 E, Salt Lake City, UT 84112, USA. ; Department of Evolutionary Biology and Science for Life Laboratory, Uppsala University, Norbyvagen 18D, SE-752 36 Uppsala, Sweden. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Acceleration Mass Spectrometry 14C Dating Centre, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, 8000 Aarhus, Denmark. ; Department of Genetics, School of Medicine, Stanford University, 300 Pasteur Drive, Lane Building, Room L331, Stanford, CA 94305, USA. Laboratorio Nacional de Genomica para la Biodiversidad (LANGEBIO), Centro de Investigacion y de Estudios Avanzados, Irapuato, Guanajuato 36821, Mexico. ; Estonian Biocentre, Evolutionary Biology Group, Tartu 51010, Estonia. ; Genetics Institute, University College London, Gower Street, London WC1E 6BT, UK. Evolutionsbiologiskt Centrum, Norbyvagen 18D, 75236 Uppsala, Sweden. ; Department of Geography, University of Cambridge, Downing Place, Cambridge CB2 3EN, UK. ; Centre for Past Climate Change and Department of Meteorology, University of Reading, Earley Gate, Post Office Box 243, Reading, UK. ; School of Geographical Sciences, University Road, Clifton, Bristol BS8 1SS, UK. ; Genetics Institute, University College London, Gower Street, London WC1E 6BT, UK. ; Escuela Nacional de AntropologIa e Historia, Periferico Sur y Zapote s/n Colonia Isidro Fabela, Tlalpan, Isidro Fabela, 14030 Mexico City, Mexico. ; Instituto de Investigacion en Genetica Molecular, Universidad de Guadalajara, Ocotlan, Mexico. ; Dipartimento di Scienze Biologiche, Geologiche e Ambientali (BiGeA), Universita di Bologna, Via Selmi 3, 40126 Bologna, Italy. ; Department of Anthropology, Oregon State University, 238 Waldo Hall, Corvallis, OR 97331 USA. ; Museum National d'Histoire Naturelle, CNRS, Universite Paris 7 Diderot, Sorbonne Paris Cite, Sorbonne Universites, Unite Eco-Anthropologie et Ethnobiologie (UMR7206), Paris, France. ; Anthropological Institute and Museum, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland. ; Department of Archaeology and History, La Trobe University, Melbourne, Victoria 3086, Australia. ; Department of Archaeology, Memorial University, Queen's College, 210 Prince Philip Drive, St. John's, Newfoundland A1C 5S7, Canada. Department of Human Evolution, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, Leipzig 04103, Germany. ; Department of Archaeology, Memorial University, Queen's College, 210 Prince Philip Drive, St. John's, Newfoundland A1C 5S7, Canada. ; Department of Earth System Science, University of California, Irvine, Keck Carbon Cycle Accelerator Mass Spectrometry Group, B321 Croul Hall, Irvine, CA 92697, USA. ; Instituto de Alta Investigacion, Universidad de Tarapaca, 18 de Septiembre 2222, Carsilla 6-D Arica, Chile. ; Departamento de Antropologia, Universidad de Tarapaca, 18 de Septiembre 2222, Carsilla 6-D Arica, Chile. ; Fundacao Museu do Homem Americano, Centro Cultural Sergio Motta, Campestre, 64770-000 Sao Raimundo Nonato, Brazil. ; Laboratoire d'Anthropologie Moleculaire et Imagerie de Synthese UMR-5288, CNRS, Universite de Toulouse, 31073 Toulouse, France. ; Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090 Novosibirsk, Russia. Novosibirsk State University, 2 Pirogova Street, 630090 Novosibirsk, Russia. ; Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090 Novosibirsk, Russia. Institute of Internal Medicine, Siberian Branch of RAS, 175/1 ul. B. Bogatkova, Novosibirsk 630089, Russia. Novosibirsk State University, Laboratory of Molecular Epidemiology and Bioinformatics, 630090 Novosibirsk, Russia. ; Vavilov Institute of General Genetics, Gubkina 3, 119333 Moscow, Russia. Research Centre for Medical Genetics, Moskvorechie 1, 115478 Moscow, Russia. ; Kemerovo State University, Krasnaya 3, 650000 Kemerovo, Russia. ; Vavilov Institute of General Genetics, Gubkina 3, 119333 Moscow, Russia. ; Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Prospekt Oktyabrya 71, 450054 Ufa, Russia. Department of Genetics and Fundamental Medicine, Bashkir State University, Zaki Validi 32, 450076 Ufa, Russia. ; Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090 Novosibirsk, Russia. ; Research Centre for Medical Genetics, Moskvorechie 1, 115478 Moscow, Russia. ; Department of Molecular Genetics, Yakut Scientific Centre of Complex Medical Problems, Sergelyahskoe Shosse 4, 677010 Yakutsk, Russia. Laboratory of Molecular Biology, Institute of Natural Sciences, M. K. Ammosov North-Eastern Federal University, 677000 Yakutsk, Russia. ; Estonian Biocentre, Evolutionary Biology Group, Tartu 51010, Estonia. Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Prospekt Oktyabrya 71, 450054 Ufa, Russia. ; Institute of Biological Problems of the North, Russian Academy of Sciences, Portovaya Street 18, Magadan 685000, Russia. ; Department of Anthropology, Western Washington University, Bellingham, WA 98225, USA. ; Department of Anthropology, Northwest Community College, 353 Fifth Street, Prince Rupert, British Columbia V8J 3L6, Canada. ; Canadian Museum of History, 100 Rue Laurier, Gatineau, Quebec K1A 0M8, Canada. University of Western Ontario, London, Ontario N6A 3K7, Canada. Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada. ; Metlakatla Treaty Office, Post Office Box 224, Prince Rupert, BC V8J 3P6, Canada. ; Sealaska Heritage Institute, 105 S. Seward Street, Juneau, AK 99801, USA. ; Department of Structural Biology, Stanford University School of Medicine, D100 Fairchild Science Building, Stanford, CA 94305-5126, USA. ; School of Biological Sciences, Washington State University, Post Office Box 644236, Heald 429, Pullman, WA 99164, USA. Department of Anthropology, Washington State University, Pullman, WA 99163, USA. ; Estonian Biocentre, Evolutionary Biology Group, Tartu 51010, Estonia. Division of Biological Anthropology, University of Cambridge, Henry Wellcome Building, Fitzwilliam Street, Cambridge CB2 1QH, UK. ; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK. Department of Medicine, University of Cambridge, Medical Research Council Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK. ; Laboratory of Biological Anthropology, University of Kansas, 1415 Jayhawk Boulevard, 622 Fraser Hall, Lawrence, KS 66045, USA. ; Molecular Anthropology Laboratory, 209 Young Hall, Department of Anthropology, University of California, One Shields Avenue, Davis, CA 95616, USA. ; Center for the Study of the First Americans, Texas A&M University, College Station, TX 77843-4352, USA. Department of Anthropology, Texas A&M University, College Station, TX 77843-4352, USA. Department of Geography, Texas A&M University, College Station, TX 77843-4352, USA. ; Center for the Study of the First Americans, Texas A&M University, College Station, TX 77843-4352, USA. ; Santa Barbara Museum of Natural History, 2559 Puesta del Sol, Santa Barbara, CA 93105, USA. ; Department of Anthropology, University of Illinois at Urbana-Champaign, 607 S. Mathews Avenue, Urbana, IL 61801, USA. Carle R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Department of Anthropology, Southern Methodist University, Dallas, TX 75275, USA. ; Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK. ; Computer Science Division, University of California, Berkeley, Berkeley, CA 94720, USA. Department of Statistics, University of California, Berkeley, Berkeley, CA 94720, USA. Department of Integrative Biology, University of California, 3060 Valley Life Sciences Building 3140, Berkeley, CA 94720, USA. ewillierslev@snm.ku.dk rasmus_nielsen@berkeley.edu yss@berkeley.edu. ; Department of Integrative Biology, University of California, 3060 Valley Life Sciences Building 3140, Berkeley, CA 94720, USA. ewillierslev@snm.ku.dk rasmus_nielsen@berkeley.edu yss@berkeley.edu. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. ewillierslev@snm.ku.dk rasmus_nielsen@berkeley.edu yss@berkeley.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26198033" target="_blank"〉PubMed〈/a〉

Description: Copy-number variants (CNVs) are a major form of genetic variation and a risk factor for various human diseases, so it is crucial to accurately detect and characterize them. It is conceivable that allele-specific reads from high-throughput sequencing data could be leveraged to both enhance CNV detection and produce allele-specific copy number (ASCN) calls. Although statistical methods have been developed to detect CNVs using whole-genome sequence (WGS) and/or whole-exome sequence (WES) data, information from allele-specific read counts has not yet been adequately exploited. In this paper, we develop an integrated method, called AS-GENSENG, which incorporates allele-specific read counts in CNV detection and estimates ASCN using either WGS or WES data. To evaluate the performance of AS-GENSENG, we conducted extensive simulations, generated empirical data using existing WGS and WES data sets and validated predicted CNVs using an independent methodology. We conclude that AS-GENSENG not only predicts accurate ASCN calls but also improves the accuracy of total copy number calls, owing to its unique ability to exploit information from both total and allele-specific read counts while accounting for various experimental biases in sequence data. Our novel, user-friendly and computationally efficient method and a complete analytic protocol is freely available at https://sourceforge.net/projects/asgenseng/ .

Description: Increased sequencing of microbial genomes has revealed that prevailing prokaryotic species assignments can be inconsistent with whole genome information for a significant number of species. The long-standing need for a systematic and scalable species assignment technique can be met by the genome-wide Average Nucleotide Identity (gANI) metric, which is widely acknowledged as a robust measure of genomic relatedness. In this work, we demonstrate that the combination of gANI and the alignment fraction (AF) between two genomes accurately reflects their genomic relatedness. We introduce an efficient implementation of AF,gANI and discuss its successful application to 86.5M genome pairs between 13,151 prokaryotic genomes assigned to 3032 species. Subsequently, by comparing the genome clusters obtained from complete linkage clustering of these pairs to existing taxonomy, we observed that nearly 18% of all prokaryotic species suffer from anomalies in species definition. Our results can be used to explore central questions such as whether microorganisms form a continuum of genetic diversity or distinct species represented by distinct genetic signatures. We propose that this precise and objective AF,gANI-based species definition: the MiSI (Microbial Species Identifier) method, be used to address previous inconsistencies in species classification and as the primary guide for new taxonomic species assignment, supplemented by the traditional polyphasic approach, as required.

Description: Classically or alternatively activated macrophages (M1 and M2, respectively) play distinct and important roles for microbiocidal activity, regulation of inflammation and tissue homeostasis. Despite this, their transcriptional regulatory dynamics are poorly understood. Using promoter-level expression profiling by non-biased deepCAGE we have studied the transcriptional dynamics of classically and alternatively activated macrophages. Transcription factor (TF) binding motif activity analysis revealed four motifs, NFKB1_REL_RELA, IRF1,2, IRF7 and TBP that are commonly activated but have distinct activity dynamics in M1 and M2 activation. We observe matching changes in the expression profiles of the corresponding TFs and show that only a restricted set of TFs change expression. There is an overall drastic and transient up-regulation in M1 and a weaker and more sustainable up-regulation in M2. Novel TFs, such as Thap6, Maff , (M1) and Hivep1, Nfil3, Prdm1 , (M2) among others, were suggested to be involved in the activation processes. Additionally, 52 (M1) and 67 (M2) novel differentially expressed genes and, for the first time, several differentially expressed long non-coding RNA (lncRNA) transcriptome markers were identified. In conclusion, the finding of novel motifs, TFs and protein-coding and lncRNA genes is an important step forward to fully understand the transcriptional machinery of macrophage activation.

Description: Author Posting. © American Meteorological Society, 2014. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 27 (2014): 2405–2416, doi:10.1175/JCLI-D-13-00359.1.

Description: Several recent studies utilizing global climate models predict that the Pacific Equatorial Undercurrent (EUC) will strengthen over the twenty-first century. Here, historical changes in the tropical Pacific are investigated using the Simple Ocean Data Assimilation (SODA) reanalysis toward understanding the dynamics and mechanisms that may dictate such a change. Although SODA does not assimilate velocity observations, the seasonal-to-interannual variability of the EUC estimated by SODA corresponds well with moored observations over a ~20-yr common period. Long-term trends in SODA indicate that the EUC core velocity has increased by 16% century−1 and as much as 47% century−1 at fixed locations since the mid-1800s. Diagnosis of the zonal momentum budget in the equatorial Pacific reveals two distinct seasonal mechanisms that explain the EUC strengthening. The first is characterized by strengthening of the western Pacific trade winds and hence oceanic zonal pressure gradient during boreal spring. The second entails weakening of eastern Pacific trade winds during boreal summer, which weakens the surface current and reduces EUC deceleration through vertical friction. EUC strengthening has important ecological implications as upwelling affects the thermal and biogeochemical environment. Furthermore, given the potential large-scale influence of EUC strength and depth on the heat budget in the eastern Pacific, the seasonal strengthening of the EUC may help reconcile paradoxical observations of Walker circulation slowdown and zonal SST gradient strengthening. Such a process would represent a new dynamical “thermostat” on CO2-forced warming of the tropical Pacific Ocean, emphasizing the importance of ocean dynamics and seasonality in understanding climate change projections.

Description: EJDis supported by NSFGrantsOCE-1031971 and OCE-1233282. KBK is supported by NSF Grant OCE-1233282.

Description: 2014-09-15

Description: Author Posting. © American Meteorological Society, 2014. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Atmospheric and Oceanic Technology 21 (2014): 2015–2025, doi:10.1175/JTECH-D-13-00262.1.

Description: The NOAA Tropical Atmosphere Ocean (TAO) moored array has, for three decades, been a valuable resource for monitoring and forecasting El Niño–Southern Oscillation and understanding physical oceanographic as well as coupled processes in the tropical Pacific influencing global climate. Acoustic Doppler current profiler (ADCP) measurements by TAO moorings provide benchmarks for evaluating numerical simulations of subsurface circulation including the Equatorial Undercurrent (EUC). Meanwhile, the Sea Education Association (SEA) has been collecting data during repeat cruises to the central equatorial Pacific Ocean (160°–126°W) throughout the past decade that provide useful cross validation and quantitative insight into the potential for stationary observing platforms such as TAO to incur sampling biases related to the strength of the EUC. This paper describes some essential sampling characteristics of the SEA dataset, compares SEA and TAO velocity measurements in the vicinity of the EUC, shares new insight into EUC characteristics and behavior only observable in repeat cross-equatorial sections, and estimates the sampling bias incurred by equatorial TAO moorings in their estimates of the velocity and transport of the EUC. The SEA high-resolution ADCP dataset compares well with concurrent TAO measurements (RMSE = 0.05 m s−1; R2 = 0.98), suggests that the EUC core meanders sinusoidally about the equator between ±0.4° latitude, and reveals a mean sampling bias of equatorial measurements (e.g., TAO) of the EUC’s zonal velocity of −0.14 ± 0.03 m s−1 as well as a ~10% underestimation of EUC volume transport. A bias-corrected monthly record and climatology of EUC strength at 140°W for 1990–2010 is presented.

Description: The authors thank the NSF Physical Oceanography program (OCE-1233282) and the WHOI Academic Programs Office for funding.

Description: 2015-03-01

Description: Author Posting. © American Meteorological Society, 2013. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 43 (2013): 1398–1406, doi:10.1175/JPO-D-13-028.1.

Description: An adiabatic, inertial, and quasigeostrophic model is used to discuss the interaction of surface Ekman transport with an island. The theory extends the recent work of Spall and Pedlosky to include an analytical and nonlinear model for the interaction. The presence of an island that interrupts a uniform Ekman layer transport raises interesting questions about the resulting circulation. The consequential upwelling around the island can lead to a local intake of fluid from the geostrophic region beneath the Ekman layer or to a more complex flow around the island in which the fluid entering the Ekman layer on one portion of the island's perimeter is replaced by a flow along the island's boundary from a downwelling region located elsewhere on the island. This becomes especially pertinent when the flow is quasigeostrophic and adiabatic. The oncoming geostrophic flow that balances the offshore Ekman flux is largely diverted around the island, and the Ekman flux is fed by a transfer of fluid from the western to the eastern side of the island. As opposed to the linear, dissipative model described earlier, this transfer takes place even in the absence of a topographic skirt around the island. The principal effect of topography in the inertial model is to introduce an asymmetry between the circulation on the northern and southern sides of the island. The quasigeostrophic model allows a simple solution to the model problem with topography and yet the resulting three-dimensional circulation is surprisingly complex with streamlines connecting each side of the island.

Description: This research was supported in part by NSF Grant OCE Grant 0925061.

Description: Author Posting. © American Meteorological Society, 2014. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 27 (2014): 2842–2860, doi:10.1175/JCLI-D-13-00227.1.

Description: Mooring measurements from the Kuroshio Extension System Study (June 2004–June 2006) and from the ongoing Kuroshio Extension Observatory (June 2004–present) are combined with float measurements of the Argo network to study the variability of the North Pacific Subtropical Mode Water (STMW) across the entire gyre, on time scales from days, to seasons, to a decade. The top of the STMW follows a seasonal cycle, although observations reveal that it primarily varies in discrete steps associated with episodic wind events. The variations of the STMW bottom depth are tightly related to the sea surface height (SSH), reflecting mesoscale eddies and large-scale variations of the Kuroshio Extension and recirculation gyre systems. Using the observed relationship between SSH and STMW, gridded SSH products and in situ estimates from floats are used to construct weekly maps of STMW thickness, providing nonbiased estimates of STMW total volume, annual formation and erosion volumes, and seasonal and interannual variability for the past decade. Year-to-year variations are detected, particularly a significant decrease of STMW volume in 2007–10 primarily attributable to a smaller volume formed. Variability of the heat content in the mode water region is dominated by the seasonal cycle and mesoscale eddies; there is only a weak link to STMW on interannual time scales, and no long-term trends in heat content and STMW thickness between 2002 and 2011 are detected. Weak lagged correlations among air–sea fluxes, oceanic heat content, and STMW thickness are found when averaged over the northwestern Pacific recirculation gyre region.

Description: This work was sponsored by the National Science Foundation (Grants OCE-0220161, OCE-0825152, and OCE-0827125).

Description: 2014-10-15

Description: Author Posting. © American Meteorological Society, 2014. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 44 (2014): 149–163, doi:10.1175/JPO-D-13-0136.1.

Description: Monthly mapped sea level anomalies (MSLAs) of the NW Atlantic in the region immediately downstream of the Gulf Stream (GS) separation point reveal a leading mode in which the path shifts approximately 100 km meridionally about a nominal latitude of 39°N, producing coherent sea level anomaly (SLA) variability from 72° to 50°W. This mode can be captured by use of a simple 16-point index based on SLA data taken along the maximum of the observed variability in the region 33°–46°N and 45°–75°W. The GS shifts between 2010 and 2012 are the largest of the last decade and equal to the largest of the entire record. The second group of EOF modes of variability describes GS meanders, which propagate mainly westward interrupted by brief periods of eastward or stationary meanders. These meanders have wavelengths of approximately 400 km and can be seen in standard EOFs by spatial phase shifting of a standing meander pattern in the SLA data. The spectral properties of these modes indicate strong variability at interannual and longer periods for the first mode and periods of a few to several months for the meanders. While the former is quite similar to a previous use of the altimeter for GS path, the simple index is a useful measure of the large-scale shifts in the GS path that is quickly estimated and updated without changes in previous estimates. The time-scale separation allows a low-pass filtered 16-point index to be reflective of large-scale, coherent shifts in the GS path.

Description: Agencia Canaria de Investigación, Innovación y Sociedad de la Información (ACIISI) grant program of Apoyo al Personal Investigador en Formación and NSF Grant OCE-0726720

Description: 2014-07-01

Description: © The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Standards in Genomic Sciences 9 (2014): 1236-1250, doi:10.4056/sigs.4319681.

Description: This report summarizes the proceedings of the 14th workshop of the Genomic Standards Consortium (GSC) held at the University of Oxford in September 2012. The workshop’s primary goal was to work towards the launch of the Genomic Observatories (GOs) Network under the GSC. For the first time, it brought together potential GOs sites, GSC members, and a range of interested partner organizations. It thus represented the first meeting of the GOs Network (GOs1). Key outcomes include the formation of a core group of “champions” ready to take the GOs Network forward, as well as the formation of working groups. The workshop also served as the first meeting of a wide range of participants in the Ocean Sampling Day (OSD) initiative, a first GOs action. Three projects with complementary interests – COST Action ES1103, MG4U and Micro B3 – organized joint sessions at the workshop. A two-day GSC Hackathon followed the main three days of meetings.

Description: This work was supported in part by the US Na-tional Science Foundation through the research coordination network award RCN4GSC, DBI-0840989 and in part by a grant from the Gordon and Betty Moore Foundation, and travel grants of COST Action ES1103. The stakeholder session was supported by the European Union’s Seventh Framework Programme (FP7 /2007-2013) under grant agreement no 266055, and the Marine Ge-nomics for Users EU FP7 project (Coordination and support action, call FP7-KBBE-2010-4) grant no. 266055. We thank Eppendorf and Biomatters Ltd. for their sponsorship of the meeting.

Description: © The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Standards in Genomic Sciences 9 (2014): 599-601, doi:10.4056/sigs.5559680.

Description: The Genomic Standards Consortium (GSC) is an open-membership community working towards the development, implementation and harmonization of standards in the field of genomics. The mission of the GSC is to improve digital descriptions of genomes, metagenomes and gene marker sequences. The GSC started in late 2005 with the defined task of establishing what is now termed the “Minimum Information about any Sequence” (MIxS) standard [1,2]. As an outgrowth of the activities surrounding the creation and implementation of the MixS standard there are now 18 projects within the GSC [3]. These efforts cover an ever widening range of standardization activities. Given the growth of projects and to promote transparency, participation and adoption the GSC has developed a “GSC Project Description Template”. A complete set of GSC Project Descriptions and the template are available on the GSC website. The GSC has an open policy of participation and continues to welcome new efforts. Any projects that facilitate the standard descriptions and exchange of data are potential candidates for inclusion under the GSC umbrella. Areas that expand the scope of the GSC are encouraged. Through these collective activities we hope to help foster the growth of the ‘bioinformatics standards’ community. For more information on the GSC and its range of projects, please see http://gensc.org/.

Description: The advent in high-throughput-sequencing (HTS) technologies has revolutionized conventional biodiversity research by enabling parallel capture of DNA sequences possessing species-level diagnosis. However, polymerase chain reaction (PCR)-based implementation is biased by the efficiency of primer binding across lineages of organisms. A PCR-free HTS approach will alleviate this artefact and significantly improve upon the multi-locus method utilizing full mitogenomes. Here we developed a novel multiplex sequencing and assembly pipeline allowing for simultaneous acquisition of full mitogenomes from pooled animals without DNA enrichment or amplification. By concatenating assemblies from three de novo assemblers, we obtained high-quality mitogenomes for all 49 pooled taxa, with 36 species 〉15 kb and the remaining 〉10 kb, including 20 complete mitogenomes and nearly all protein coding genes (99.6%). The assembly quality was carefully validated with Sanger sequences, reference genomes and conservativeness of protein coding genes across taxa. The new method was effective even for closely related taxa, e.g. three Drosophila spp., demonstrating its broad utility for biodiversity research and mito-phylogenomics. Finally, the in silico simulation showed that by recruiting multiple mito-loci, taxon detection was improved at a fixed sequencing depth. Combined, these results demonstrate the plausibility of a multi-locus mito-metagenomics approach as the next phase of the current single-locus metabarcoding method.

Description: Chromatin modifiers and histone modifications are components of a chromatin-signaling network involved in transcription and its regulation. The interactions between chromatin modifiers and histone modifications are often unknown, are based on the analysis of few genes or are studied in vitro . Here, we apply computational methods to recover interactions between chromatin modifiers and histone modifications from genome-wide ChIP-Seq data. These interactions provide a high-confidence backbone of the chromatin-signaling network. Many recovered interactions have literature support; others provide hypotheses about yet unknown interactions. We experimentally verified two of these predicted interactions, leading to a link between H4K20me1 and members of the Polycomb Repressive Complexes 1 and 2. Our results suggest that our computationally derived interactions are likely to lead to novel biological insights required to establish the connectivity of the chromatin-signaling network involved in transcription and its regulation.

Description: A major challenge in cancer genomics is uncovering genes with an active role in tumorigenesis from a potentially large pool of mutated genes across patient samples. Here we focus on the interactions that proteins make with nucleic acids, small molecules, ions and peptides, and show that residues within proteins that are involved in these interactions are more frequently affected by mutations observed in large-scale cancer genomic data than are other residues. We leverage this observation to predict genes that play a functionally important role in cancers by introducing a computational pipeline ( http://canbind.princeton.edu ) for mapping large-scale cancer exome data across patients onto protein structures, and automatically extracting proteins with an enriched number of mutations affecting their nucleic acid, small molecule, ion or peptide binding sites. Using this computational approach, we show that many previously known genes implicated in cancers are enriched in mutations within the binding sites of their encoded proteins. By focusing on functionally relevant portions of proteins—specifically those known to be involved in molecular interactions—our approach is particularly well suited to detect infrequent mutations that may nonetheless be important in cancer, and should aid in expanding our functional understanding of the genomic landscape of cancer.

Description: Genome duplication with hybridization, or allopolyploidization, occurs commonly in plants, and is considered to be a strong force for generating new species. However, genome-wide quantification of homeolog expression ratios was technically hindered because of the high homology between homeologous gene pairs. To quantify the homeolog expression ratio using RNA-seq obtained from polyploids, a new method named HomeoRoq was developed, in which the genomic origin of sequencing reads was estimated using mismatches between the read and each parental genome. To verify this method, we first assembled the two diploid parental genomes of Arabidopsis halleri subsp. gemmifera and Arabidopsis lyrata subsp. petraea ( Arabidopsis petraea subsp. umbrosa ), then generated a synthetic allotetraploid, mimicking the natural allopolyploid Arabidopsis kamchatica . The quantified ratios corresponded well to those obtained by Pyrosequencing. We found that the ratios of homeologs before and after cold stress treatment were highly correlated ( r = 0.870). This highlights the presence of nonstochastic polyploid gene regulation despite previous research identifying stochastic variation in expression. Moreover, our new statistical test incorporating overdispersion identified 226 homeologs (1.11% of 20 369 expressed homeologs) with significant ratio changes, many of which were related to stress responses. HomeoRoq would contribute to the study of the genes responsible for polyploid-specific environmental responses.

Description: Reconstructing the evolutionary relationships of species is a major goal in biology. Despite the increasing number of completely sequenced genomes, a large number of phylogenetic projects rely on targeted sequencing and analysis of a relatively small sample of marker genes. The selection of these phylogenetic markers should ideally be based on accurate predictions of their combined, rather than individual, potential to accurately resolve the phylogeny of interest. Here we present and validate a new phylogenomics strategy to efficiently select a minimal set of stable markers able to reconstruct the underlying species phylogeny. In contrast to previous approaches, our methodology does not only rely on the ability of individual genes to reconstruct a known phylogeny, but it also explores the combined power of sets of concatenated genes to accurately infer phylogenetic relationships of species not previously analyzed. We applied our approach to two broad sets of cyanobacterial and ascomycetous fungal species, and provide two minimal sets of six and four genes, respectively, necessary to fully resolve the target phylogenies. This approach paves the way for the informed selection of phylogenetic markers in the effort of reconstructing the tree of life.

Description: Birds are the most species-rich class of tetrapod vertebrates and have wide relevance across many research fields. We explored bird macroevolution using full genomes from 48 avian species representing all major extant clades. The avian genome is principally characterized by its constrained size, which predominantly arose because of lineage-specific erosion of repetitive elements, large segmental deletions, and gene loss. Avian genomes furthermore show a remarkably high degree of evolutionary stasis at the levels of nucleotide sequence, gene synteny, and chromosomal structure. Despite this pattern of conservation, we detected many non-neutral evolutionary changes in protein-coding genes and noncoding regions. These analyses reveal that pan-avian genomic diversity covaries with adaptations to different lifestyles and convergent evolution of traits.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4390078/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4390078/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Zhang, Guojie -- Li, Cai -- Li, Qiye -- Li, Bo -- Larkin, Denis M -- Lee, Chul -- Storz, Jay F -- Antunes, Agostinho -- Greenwold, Matthew J -- Meredith, Robert W -- Odeen, Anders -- Cui, Jie -- Zhou, Qi -- Xu, Luohao -- Pan, Hailin -- Wang, Zongji -- Jin, Lijun -- Zhang, Pei -- Hu, Haofu -- Yang, Wei -- Hu, Jiang -- Xiao, Jin -- Yang, Zhikai -- Liu, Yang -- Xie, Qiaolin -- Yu, Hao -- Lian, Jinmin -- Wen, Ping -- Zhang, Fang -- Li, Hui -- Zeng, Yongli -- Xiong, Zijun -- Liu, Shiping -- Zhou, Long -- Huang, Zhiyong -- An, Na -- Wang, Jie -- Zheng, Qiumei -- Xiong, Yingqi -- Wang, Guangbiao -- Wang, Bo -- Wang, Jingjing -- Fan, Yu -- da Fonseca, Rute R -- Alfaro-Nunez, Alonzo -- Schubert, Mikkel -- Orlando, Ludovic -- Mourier, Tobias -- Howard, Jason T -- Ganapathy, Ganeshkumar -- Pfenning, Andreas -- Whitney, Osceola -- Rivas, Miriam V -- Hara, Erina -- Smith, Julia -- Farre, Marta -- Narayan, Jitendra -- Slavov, Gancho -- Romanov, Michael N -- Borges, Rui -- Machado, Joao Paulo -- Khan, Imran -- Springer, Mark S -- Gatesy, John -- Hoffmann, Federico G -- Opazo, Juan C -- Hastad, Olle -- Sawyer, Roger H -- Kim, Heebal -- Kim, Kyu-Won -- Kim, Hyeon Jeong -- Cho, Seoae -- Li, Ning -- Huang, Yinhua -- Bruford, Michael W -- Zhan, Xiangjiang -- Dixon, Andrew -- Bertelsen, Mads F -- Derryberry, Elizabeth -- Warren, Wesley -- Wilson, Richard K -- Li, Shengbin -- Ray, David A -- Green, Richard E -- O'Brien, Stephen J -- Griffin, Darren -- Johnson, Warren E -- Haussler, David -- Ryder, Oliver A -- Willerslev, Eske -- Graves, Gary R -- Alstrom, Per -- Fjeldsa, Jon -- Mindell, David P -- Edwards, Scott V -- Braun, Edward L -- Rahbek, Carsten -- Burt, David W -- Houde, Peter -- Zhang, Yong -- Yang, Huanming -- Wang, Jian -- Avian Genome Consortium -- Jarvis, Erich D -- Gilbert, M Thomas P -- Wang, Jun -- DP1 OD000448/OD/NIH HHS/ -- DP1OD000448/OD/NIH HHS/ -- R01 HL087216/HL/NHLBI NIH HHS/ -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2014 Dec 12;346(6215):1311-20. doi: 10.1126/science.1251385. Epub 2014 Dec 11.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, 518083, China. Centre for Social Evolution, Department of Biology, Universitetsparken 15, University of Copenhagen, DK-2100 Copenhagen, Denmark. zhanggj@genomics.cn jarvis@neuro.duke.edu mtpgilbert@gmail.com wangj@genomics.cn. ; China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, 518083, China. Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. ; China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, 518083, China. ; Royal Veterinary College, University of London, London, UK. ; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Republic of Korea. Cho and Kim Genomics, Seoul National University Research Park, Seoul 151-919, Republic of Korea. ; School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA. ; Centro de Investigacion en Ciencias del Mar y Limnologia (CIMAR)/Centro Interdisciplinar de Investigacao Marinha e Ambiental (CIIMAR), Universidade do Porto, Rua dos Bragas, 177, 4050-123 Porto, Portugal. Departamento de Biologia, Faculdade de Ciencias, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal. ; Department of Biological Sciences, University of South Carolina, Columbia, SC, USA. ; Department of Biology and Molecular Biology, Montclair State University, Montclair, NJ 07043, USA. ; Department of Animal Ecology, Uppsala University, Norbyvagen 18D, S-752 36 Uppsala, Sweden. ; Marie Bashir Institute for Infectious Diseases and Biosecurity, Charles Perkins Centre, School of Biological Sciences and Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia. Program in Emerging Infectious Diseases, Duke-NUS Graduate Medical School, Singapore 169857, Singapore. ; Department of Integrative Biology University of California, Berkeley, CA 94720, USA. ; China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, 518083, China. College of Life Sciences, Wuhan University, Wuhan 430072, China. ; China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, 518083, China. School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510006, China. ; China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, 518083, China. BGI Education Center,University of Chinese Academy of Sciences,Shenzhen, 518083, China. ; Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. ; Department of Neurobiology, Howard Hughes Medical Institute, Duke University Medical Center, Durham, NC 27710, USA. ; Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK. ; School of Biosciences, University of Kent, Canterbury CT2 7NJ, UK. ; Centro de Investigacion en Ciencias del Mar y Limnologia (CIMAR)/Centro Interdisciplinar de Investigacao Marinha e Ambiental (CIIMAR), Universidade do Porto, Rua dos Bragas, 177, 4050-123 Porto, Portugal. Instituto de Ciencias Biomedicas Abel Salazar (ICBAS), Universidade do Porto, Portugal. ; Department of Biology, University of California Riverside, Riverside, CA 92521, USA. ; Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Mississippi State, MS 39762, USA. Institute for Genomics, Biocomputing and Biotechnology, Mississippi State University, Mississippi State, MS 39762, USA. ; Instituto de Ciencias Ambientales y Evolutivas, Facultad de Ciencias, Universidad Austral de Chile, Valdivia, Chile. ; Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Post Office Box 7011, S-750 07, Uppsala, Sweden. ; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Republic of Korea. Cho and Kim Genomics, Seoul National University Research Park, Seoul 151-919, Republic of Korea. Department of Agricultural Biotechnology and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul 151-742, Republic of Korea. ; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Republic of Korea. ; Cho and Kim Genomics, Seoul National University Research Park, Seoul 151-919, Republic of Korea. ; State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing 100094, China. ; State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing 100094, China. College of Animal Science and Technology, China Agricultural University, Beijing 100094, China. ; Organisms and Environment Division, Cardiff School of Biosciences, Cardiff University, Cardiff CF10 3AX, Wales, UK. ; Organisms and Environment Division, Cardiff School of Biosciences, Cardiff University, Cardiff CF10 3AX, Wales, UK. Key Lab of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101 China. ; International Wildlife Consultants, Carmarthen SA33 5YL, Wales, UK. ; Centre for Zoo and Wild Animal Health, Copenhagen Zoo, Roskildevej 38, DK-2000 Frederiksberg, Denmark. ; Department of Ecology and Evolutionary Biology, Tulane University, New Orleans, LA, USA. Museum of Natural Science, Louisiana State University, Baton Rouge, LA 70803, USA. ; The Genome Institute at Washington University, St. Louis, MO 63108, USA. ; College of Medicine and Forensics, Xi'an Jiaotong University, Xi'an, 710061, China. ; Institute for Genomics, Biocomputing and Biotechnology, Mississippi State University, Mississippi State, MS 39762, USA. ; Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA. ; Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia. Nova Southeastern University Oceanographic Center 8000 N Ocean Drive, Dania, FL 33004, USA. ; Smithsonian Conservation Biology Institute, National Zoological Park, 1500 Remount Road, Front Royal, VA 22630, USA. ; Genetics Division, San Diego Zoo Institute for Conservation Research, 15600 San Pasqual Valley Road, Escondido, CA 92027, USA. ; Department of Vertebrate Zoology, MRC-116, National Museum of Natural History, Smithsonian Institution, Post Office Box 37012, Washington, DC 20013-7012, USA. Center for Macroecology, Evolution and Climate, the Natural History Museum of Denmark, University of Copenhagen, Universitetsparken 15, DK-2100 Copenhagen O, Denmark. ; Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China. Swedish Species Information Centre, Swedish University of Agricultural Sciences, Box 7007, SE-750 07 Uppsala, Sweden. ; Center for Macroecology, Evolution and Climate, the Natural History Museum of Denmark, University of Copenhagen, Universitetsparken 15, DK-2100 Copenhagen O, Denmark. ; Department of Biochemistry & Biophysics, University of California, San Francisco, CA 94158, USA. ; Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, USA. ; Department of Biology and Genetics Institute, University of Florida, Gainesville, FL 32611, USA. ; Center for Macroecology, Evolution and Climate, the Natural History Museum of Denmark, University of Copenhagen, Universitetsparken 15, DK-2100 Copenhagen O, Denmark. Imperial College London, Grand Challenges in Ecosystems and the Environment Initiative, Silwood Park Campus, Ascot, Berkshire SL5 7PY, UK. ; Division of Genetics and Genomics, The Roslin Institute and Royal (Dick) School of Veterinary Studies, The Roslin Institute Building, University of Edinburgh, Easter Bush Campus, Midlothian EH25 9RG, UK. ; Department of Biology, New Mexico State University, Box 30001 MSC 3AF, Las Cruces, NM 88003, USA. ; China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, 518083, China. Macau University of Science and Technology, Avenida Wai long, Taipa, Macau 999078, China. ; Department of Neurobiology, Howard Hughes Medical Institute, Duke University Medical Center, Durham, NC 27710, USA. zhanggj@genomics.cn jarvis@neuro.duke.edu mtpgilbert@gmail.com wangj@genomics.cn. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Trace and Environmental DNA Laboratory, Department of Environment and Agriculture, Curtin University, Perth, Western Australia, 6102, Australia. zhanggj@genomics.cn jarvis@neuro.duke.edu mtpgilbert@gmail.com wangj@genomics.cn. ; China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, 518083, China. Macau University of Science and Technology, Avenida Wai long, Taipa, Macau 999078, China. Department of Biology, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen, Denmark. Princess Al Jawhara Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Department of Medicine, University of Hong Kong, Hong Kong. zhanggj@genomics.cn jarvis@neuro.duke.edu mtpgilbert@gmail.com wangj@genomics.cn.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25504712" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4408607/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4408607/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Halloran, M Elizabeth -- Vespignani, Alessandro -- Bharti, Nita -- Feldstein, Leora R -- Alexander, K A -- Ferrari, Matthew -- Shaman, Jeffrey -- Drake, John M -- Porco, Travis -- Eisenberg, Joseph N S -- Del Valle, Sara Y -- Lofgren, Eric -- Scarpino, Samuel V -- Eisenberg, Marisa C -- Gao, Daozhou -- Hyman, James M -- Eubank, Stephen -- Longini, Ira M Jr -- R01 GM100467/GM/NIGMS NIH HHS/ -- U01 GM070694/GM/NIGMS NIH HHS/ -- U01 GM087728/GM/NIGMS NIH HHS/ -- U01 GM097661/GM/NIGMS NIH HHS/ -- U01 GM110712/GM/NIGMS NIH HHS/ -- U01 GM110744/GM/NIGMS NIH HHS/ -- U01 GM110748/GM/NIGMS NIH HHS/ -- U54 GM111274/GM/NIGMS NIH HHS/ -- New York, N.Y. -- Science. 2014 Oct 24;346(6208):433. doi: 10.1126/science.346.6208.433-a.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA 98109, USA. Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. betz@u.washington.edu. ; Department of Physics, Northeastern University, Boston, MA 02115, USA. ; Department of Biology, Pennsylvania State University, University Park, PA 16802, USA. ; Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA 98109, USA. Department of Epidemiology, University of Washington, Seattle, WA 98195, USA. ; Department of Fish and Wildlife Conservation, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. ; Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA. ; Odum School of Ecology, University of Georgia, Athens, GA 30602, USA. ; Francis I. Proctor Foundation, University of California, San Francisco, CA 94143, USA. ; Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109, USA. ; Los Alamos National Laboratory, Los Alamos, NM 87545, USA. ; Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. ; Santa Fe Institute, Santa Fe, NM 87501, USA. ; Department of Mathematics, Tulane University, New Orleans, LA 70118, USA. ; Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. Department of Population Health Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. ; Department of Biostatistics, University of Florida, Gainesville, FL 32611, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25342792" target="_blank"〉PubMed〈/a〉

Description: The importance, extent, and mode of interspecific gene flow for the evolution of species has long been debated. Characterization of genomic differentiation in a classic example of hybridization between all-black carrion crows and gray-coated hooded crows identified genome-wide introgression extending far beyond the morphological hybrid zone. Gene expression divergence was concentrated in pigmentation genes expressed in gray versus black feather follicles. Only a small number of narrow genomic islands exhibited resistance to gene flow. One prominent genomic region (〈2 megabases) harbored 81 of all 82 fixed differences (of 8.4 million single-nucleotide polymorphisms in total) linking genes involved in pigmentation and in visual perception-a genomic signal reflecting color-mediated prezygotic isolation. Thus, localized genomic selection can cause marked heterogeneity in introgression landscapes while maintaining phenotypic divergence.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Poelstra, J W -- Vijay, N -- Bossu, C M -- Lantz, H -- Ryll, B -- Muller, I -- Baglione, V -- Unneberg, P -- Wikelski, M -- Grabherr, M G -- Wolf, J B W -- New York, N.Y. -- Science. 2014 Jun 20;344(6190):1410-4. doi: 10.1126/science.1253226.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Science for Life Laboratory and Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, 75236 Uppsala, Sweden. ; Bioinformatics Infrastructure for Life Sciences, Uppsala University, 75124 Uppsala, Sweden. Science for Life Laboratory and Department of Medical Biochemistry and Microbiology, Uppsala University, 75123 Uppsala, Sweden. ; Department of Organismal Biology, Evolutionary Biology Centre, Uppsala University, 75236 Uppsala, Sweden. ; Max Planck Institute for Ornithology, 78315 Radolfzell, Germany. Department of Biology, University of Konstanz, 78464 Konstanz, Germany. ; Departamento de Ciencias Agro-Forestales, Campus La Yutera, Universidad de Valladolid, 34004 Palencia, Spain. ; Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, 171 21 Solna, Sweden. ; Science for Life Laboratory and Department of Medical Biochemistry and Microbiology, Uppsala University, 75123 Uppsala, Sweden. ; Science for Life Laboratory and Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, 75236 Uppsala, Sweden. jochen.wolf@ebc.uu.se.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24948738" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Pennisi, Elizabeth -- New York, N.Y. -- Science. 2014 Dec 12;346(6215):1275-6. doi: 10.1126/science.346.6215.1275.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25504693" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4407557/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4407557/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Zhang, Guojie -- Jarvis, Erich D -- Gilbert, M Thomas P -- DP1 OD000448/OD/NIH HHS/ -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2014 Dec 12;346(6215):1308-9. doi: 10.1126/science.346.6215.1308.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25504710" target="_blank"〉PubMed〈/a〉

Description: Prehistoric population structure associated with the transition to an agricultural lifestyle in Europe remains a contentious idea. Population-genomic data from 11 Scandinavian Stone Age human remains suggest that hunter-gatherers had lower genetic diversity than that of farmers. Despite their close geographical proximity, the genetic differentiation between the two Stone Age groups was greater than that observed among extant European populations. Additionally, the Scandinavian Neolithic farmers exhibited a greater degree of hunter-gatherer-related admixture than that of the Tyrolean Iceman, who also originated from a farming context. In contrast, Scandinavian hunter-gatherers displayed no significant evidence of introgression from farmers. Our findings suggest that Stone Age foraging groups were historically in low numbers, likely owing to oscillating living conditions or restricted carrying capacity, and that they were partially incorporated into expanding farming groups.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Skoglund, Pontus -- Malmstrom, Helena -- Omrak, Ayca -- Raghavan, Maanasa -- Valdiosera, Cristina -- Gunther, Torsten -- Hall, Per -- Tambets, Kristiina -- Parik, Juri -- Sjogren, Karl-Goran -- Apel, Jan -- Willerslev, Eske -- Stora, Jan -- Gotherstrom, Anders -- Jakobsson, Mattias -- New York, N.Y. -- Science. 2014 May 16;344(6185):747-50. doi: 10.1126/science.1253448. Epub 2014 Apr 24.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Evolutionary Biology, Uppsala University, Uppsala 752 36, Sweden. ; Department of Archaeology and Classical studies, Stockholm University, Stockholm 106 91, Sweden. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Copenhagen 1350, Denmark. ; Department of Archaeology, Environment and Community Planning, La Trobe University, Melbourne VIC 3086, Australia. ; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 77, Sweden. ; Evolutionary Biology Group, Estonian Biocentre and University of Tartu, Tartu 51010, Estonia. ; Department of Historical Studies, University of Gothenburg, Gothenburg, 405 30, Sweden. ; Department of Archaeology and Ancient History, Lund University, Lund, 221 00, Sweden. ; Department of Archaeology and Classical studies, Stockholm University, Stockholm 106 91, Sweden. tsarapkin@googlemail.com mattias.jakobsson@ebc.uu.se. ; Department of Evolutionary Biology, Uppsala University, Uppsala 752 36, Sweden. Science for Life Laboratory, Uppsala University, Uppsala 752 36, Sweden. tsarapkin@googlemail.com mattias.jakobsson@ebc.uu.se.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24762536" target="_blank"〉PubMed〈/a〉

Description: Edentulism, the absence of teeth, has evolved convergently among vertebrates, including birds, turtles, and several lineages of mammals. Instead of teeth, modern birds (Neornithes) use a horny beak (rhamphotheca) and a muscular gizzard to acquire and process food. We performed comparative genomic analyses representing lineages of nearly all extant bird orders and recovered shared, inactivating mutations within genes expressed in both the enamel and dentin of teeth of other vertebrate species, indicating that the common ancestor of modern birds lacked mineralized teeth. We estimate that tooth loss, or at least the loss of enamel caps that provide the outer layer of mineralized teeth, occurred about 116 million years ago.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Meredith, Robert W -- Zhang, Guojie -- Gilbert, M Thomas P -- Jarvis, Erich D -- Springer, Mark S -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2014 Dec 12;346(6215):1254390. doi: 10.1126/science.1254390. Epub 2014 Dec 11.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Biology and Molecular Biology, Montclair State University, Montclair, NJ 07043, USA. meredithr@mail.montclair.edu mark.springer@ucr.edu. ; China National GeneBank, Beijing Genomics Institute-Shenzhen, Shenzhen, 518083, China. Centre for Social Evolution, Department of Biology, Universitetsparken 15, University of Copenhagen, DK-2100 Copenhagen, Denmark. ; Center for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Trace and Environmental DNA Laboratory, Department of Environment and Agriculture, Curtin University, Perth, Western Australia 6102, Australia. ; Department of Neurobiology, Howard Hughes Medical Institute and Duke University Medical Center, Durham, NC 27710, USA. ; Department of Biology, University of California, Riverside, CA 92521, USA. meredithr@mail.montclair.edu mark.springer@ucr.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25504730" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4312280/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4312280/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Gorbunova, Vera -- Boeke, Jef D -- Helfand, Stephen L -- Sedivy, John M -- P01 AG047200/AG/NIA NIH HHS/ -- P01AG047200/AG/NIA NIH HHS/ -- P30GM103410/GM/NIGMS NIH HHS/ -- P50 GM107632/GM/NIGMS NIH HHS/ -- P50GM107632/GM/NIGMS NIH HHS/ -- R01 AG024353/AG/NIA NIH HHS/ -- R01 AG027237/AG/NIA NIH HHS/ -- R01AG024353/AG/NIA NIH HHS/ -- R01AG027237/AG/NIA NIH HHS/ -- R37 AG016667/AG/NIA NIH HHS/ -- R37 AG016694/AG/NIA NIH HHS/ -- R37AG016667/AG/NIA NIH HHS/ -- R37AG016694/AG/NIA NIH HHS/ -- T32 AG041688/AG/NIA NIH HHS/ -- T32AG041688/AG/NIA NIH HHS/ -- New York, N.Y. -- Science. 2014 Dec 5;346(6214):1187-8. doi: 10.1126/science.aaa3177.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Biology, University of Rochester, Rochester, NY 14627, USA. ; Institute for Systems Genetics, New York University Langone Medical Center, New York, NY 10016, USA. ; Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA. ; Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA. john_sedivy@brown.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25477445" target="_blank"〉PubMed〈/a〉

Description: Insects are the most speciose group of animals, but the phylogenetic relationships of many major lineages remain unresolved. We inferred the phylogeny of insects from 1478 protein-coding genes. Phylogenomic analyses of nucleotide and amino acid sequences, with site-specific nucleotide or domain-specific amino acid substitution models, produced statistically robust and congruent results resolving previously controversial phylogenetic relations hips. We dated the origin of insects to the Early Ordovician [~479 million years ago (Ma)], of insect flight to the Early Devonian (~406 Ma), of major extant lineages to the Mississippian (~345 Ma), and the major diversification of holometabolous insects to the Early Cretaceous. Our phylogenomic study provides a comprehensive reliable scaffold for future comparative analyses of evolutionary innovations among insects.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Misof, Bernhard -- Liu, Shanlin -- Meusemann, Karen -- Peters, Ralph S -- Donath, Alexander -- Mayer, Christoph -- Frandsen, Paul B -- Ware, Jessica -- Flouri, Tomas -- Beutel, Rolf G -- Niehuis, Oliver -- Petersen, Malte -- Izquierdo-Carrasco, Fernando -- Wappler, Torsten -- Rust, Jes -- Aberer, Andre J -- Aspock, Ulrike -- Aspock, Horst -- Bartel, Daniela -- Blanke, Alexander -- Berger, Simon -- Bohm, Alexander -- Buckley, Thomas R -- Calcott, Brett -- Chen, Junqing -- Friedrich, Frank -- Fukui, Makiko -- Fujita, Mari -- Greve, Carola -- Grobe, Peter -- Gu, Shengchang -- Huang, Ying -- Jermiin, Lars S -- Kawahara, Akito Y -- Krogmann, Lars -- Kubiak, Martin -- Lanfear, Robert -- Letsch, Harald -- Li, Yiyuan -- Li, Zhenyu -- Li, Jiguang -- Lu, Haorong -- Machida, Ryuichiro -- Mashimo, Yuta -- Kapli, Pashalia -- McKenna, Duane D -- Meng, Guanliang -- Nakagaki, Yasutaka -- Navarrete-Heredia, Jose Luis -- Ott, Michael -- Ou, Yanxiang -- Pass, Gunther -- Podsiadlowski, Lars -- Pohl, Hans -- von Reumont, Bjorn M -- Schutte, Kai -- Sekiya, Kaoru -- Shimizu, Shota -- Slipinski, Adam -- Stamatakis, Alexandros -- Song, Wenhui -- Su, Xu -- Szucsich, Nikolaus U -- Tan, Meihua -- Tan, Xuemei -- Tang, Min -- Tang, Jingbo -- Timelthaler, Gerald -- Tomizuka, Shigekazu -- Trautwein, Michelle -- Tong, Xiaoli -- Uchifune, Toshiki -- Walzl, Manfred G -- Wiegmann, Brian M -- Wilbrandt, Jeanne -- Wipfler, Benjamin -- Wong, Thomas K F -- Wu, Qiong -- Wu, Gengxiong -- Xie, Yinlong -- Yang, Shenzhou -- Yang, Qing -- Yeates, David K -- Yoshizawa, Kazunori -- Zhang, Qing -- Zhang, Rui -- Zhang, Wenwei -- Zhang, Yunhui -- Zhao, Jing -- Zhou, Chengran -- Zhou, Lili -- Ziesmann, Tanja -- Zou, Shijie -- Li, Yingrui -- Xu, Xun -- Zhang, Yong -- Yang, Huanming -- Wang, Jian -- Wang, Jun -- Kjer, Karl M -- Zhou, Xin -- New York, N.Y. -- Science. 2014 Nov 7;346(6210):763-7. doi: 10.1126/science.1257570. Epub 2014 Nov 6.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Zoologisches Forschungsmuseum Alexander Koenig (ZFMK)/Zentrum fur Molekulare Biodiversitatsforschung (ZMB), Bonn, Germany. xinzhou@genomics.cn b.misof.zfmk@uni-bonn.de kjer@aesop.rutgers.edu wangj@genomics.cn. ; China National GeneBank, BGI-Shenzhen, China. BGI-Shenzhen, China. ; Zoologisches Forschungsmuseum Alexander Koenig (ZFMK)/Zentrum fur Molekulare Biodiversitatsforschung (ZMB), Bonn, Germany. Australian National Insect Collection, Commonwealth Scientific and Industrial Research Organization (Australia) (CSIRO), National Research Collections Australia, Canberra, ACT, Australia. ; Abteilung Arthropoda, Zoologisches Forschungsmuseum Alexander Koenig (ZFMK), Bonn, Germany. ; Zoologisches Forschungsmuseum Alexander Koenig (ZFMK)/Zentrum fur Molekulare Biodiversitatsforschung (ZMB), Bonn, Germany. ; Department of Entomology, Rutgers University, New Brunswick, NJ 08854, USA. ; Department of Biological Sciences, Rutgers University, Newark, NJ 08854, USA. ; Scientific Computing, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany. ; Institut fur Spezielle Zoologie und Evolutionsbiologie mit Phyletischem Museum Jena, FSU Jena, Germany. ; Steinmann-Institut, Bereich Palaontologie, Universitat Bonn, Germany. ; 2. Zoologische Abteilung (Insekten), Naturhistorisches Museum Wien, Vienna, Austria. Department of Integrative Zoology, Universitat Wien, Vienna, Austria. ; Institut fur Spezifische Prophylaxe und Tropenmedizin, Medizinische Parasitologie, Medizinische Universitat Wien (MUW), Vienna, Austria. ; Department of Integrative Zoology, Universitat Wien, Vienna, Austria. ; Zoologisches Forschungsmuseum Alexander Koenig (ZFMK)/Zentrum fur Molekulare Biodiversitatsforschung (ZMB), Bonn, Germany. Sugadaira Montane Research Center/Hexapod Comparative Embryology Laboratory, University of Tsukuba, Japan. ; Manaaki Whenua Landcare Research, Auckland, New Zealand. ; Center for Advanced Modeling, Emergency Medicine Department, Johns Hopkins University, Baltimore, MD 21209, USA. ; BGI-Shenzhen, China. ; Biozentrum Grindel und Zoologisches Museum, Universitat Hamburg, Hamburg, Germany. ; Evolutionary Morphology Laboratory, Graduate School of Science and Engineering, Ehime University, Japan. ; Sugadaira Montane Research Center/Hexapod Comparative Embryology Laboratory, University of Tsukuba, Japan. ; Land and Water Flagship, CSIRO, Canberra, ACT, Australia. ; Florida Museum of Natural History, University of Florida, Gainesville, FL 32611, USA. ; Entomology, Staatliches Museum fur Naturkunde Stuttgart (SMNS), Germany. ; Ecology Evolution and Genetics, Research School of Biology, Australian National University, Canberra, ACT, Australia. National Evolutionary Synthesis Center, Durham, NC 27705, USA. Department of Biological Sciences, Macquarie University, Sydney, Australia. ; Department fur Botanik und Biodiversitatsforschung, Universitat Wien, Vienna, Austria. ; Scientific Computing, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany. Natural History Museum of Crete, University of Crete, Post Office Box 2208, Gr-71409, Iraklio, and Biology Department, University of Crete, Iraklio, Crete, Greece. ; Department of Biological Sciences and Feinstone Center for Genomic Research, University of Memphis, Memphis, TN 38152, USA. ; Centro Universitario de Ciencias Biologicas y Agropecuarias, Centro de Estudios en Zoologia, Universidad de Guadalajara, Zapopan, Jalisco, Mexico. ; Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities, Garching, Germany. ; Institute of Evolutionary Biology and Ecology, Zoology and Evolutionary Biology, University of Bonn, Bonn, Germany. ; Department of Life Sciences, The Natural History Museum London, London, UK. ; Abteilung Entomologie, Biozentrum Grindel und Zoologisches Museum, Universitat Hamburg, Hamburg, Germany. ; Australian National Insect Collection, Commonwealth Scientific and Industrial Research Organization (Australia) (CSIRO), National Research Collections Australia, Canberra, ACT, Australia. ; Scientific Computing, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany. Fakultat fur Informatik, Karlsruher Institut fur Technologie, Karlsruhe, Germany. ; California Academy of Sciences, San Francisco, CA 94118, USA. ; Department of Entomology, College of Natural Resources and Environment, South China Agricultural University, China. ; Sugadaira Montane Research Center/Hexapod Comparative Embryology Laboratory, University of Tsukuba, Japan. Yokosuka City Museum, Yokosuka, Kanagawa, Japan. ; Department of Entomology, North Carolina State University, Raleigh, NC 27695, USA. ; Systematic Entomology, Hokkaido University, Sapporo, Japan. ; BGI-Shenzhen, China. Department of Biology, University of Copenhagen, Copenhagen, Denmark. Princess Al Jawhara Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia. Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China. Department of Medicine, University of Hong Kong, Hong Kong. xinzhou@genomics.cn b.misof.zfmk@uni-bonn.de kjer@aesop.rutgers.edu wangj@genomics.cn. ; Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08854, USA. xinzhou@genomics.cn b.misof.zfmk@uni-bonn.de kjer@aesop.rutgers.edu wangj@genomics.cn. ; China National GeneBank, BGI-Shenzhen, China. BGI-Shenzhen, China. xinzhou@genomics.cn b.misof.zfmk@uni-bonn.de kjer@aesop.rutgers.edu wangj@genomics.cn.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25378627" target="_blank"〉PubMed〈/a〉

Description: All plants synthesize basic metabolites needed for survival (primary metabolism), but different taxa produce distinct metabolites that are specialized for specific environmental interactions (specialized metabolism). Because evolutionary pressures on primary and specialized metabolism differ, we investigated differences in the emergence and maintenance of these processes across 16 species encompassing major plant lineages from algae to angiosperms. We found that, relative to their primary metabolic counterparts, genes coding for specialized metabolic functions have proliferated to a much greater degree and by different mechanisms and display lineage-specific patterns of physical clustering within the genome and coexpression. These properties illustrate the differential evolution of specialized metabolism in plants, and collectively they provide unique signatures for the potential discovery of novel specialized metabolic processes.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Chae, Lee -- Kim, Taehyong -- Nilo-Poyanco, Ricardo -- Rhee, Seung Y -- New York, N.Y. -- Science. 2014 May 2;344(6183):510-3. doi: 10.1126/science.1252076.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94305, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24786077" target="_blank"〉PubMed〈/a〉

Description: The origin of contemporary Europeans remains contentious. We obtained a genome sequence from Kostenki 14 in European Russia dating from 38,700 to 36,200 years ago, one of the oldest fossils of anatomically modern humans from Europe. We find that Kostenki 14 shares a close ancestry with the 24,000-year-old Mal'ta boy from central Siberia, European Mesolithic hunter-gatherers, some contemporary western Siberians, and many Europeans, but not eastern Asians. Additionally, the Kostenki 14 genome shows evidence of shared ancestry with a population basal to all Eurasians that also relates to later European Neolithic farmers. We find that Kostenki 14 contains more Neandertal DNA that is contained in longer tracts than present Europeans. Our findings reveal the timing of divergence of western Eurasians and East Asians to be more than 36,200 years ago and that European genomic structure today dates back to the Upper Paleolithic and derives from a metapopulation that at times stretched from Europe to central Asia.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Seguin-Orlando, Andaine -- Korneliussen, Thorfinn S -- Sikora, Martin -- Malaspinas, Anna-Sapfo -- Manica, Andrea -- Moltke, Ida -- Albrechtsen, Anders -- Ko, Amy -- Margaryan, Ashot -- Moiseyev, Vyacheslav -- Goebel, Ted -- Westaway, Michael -- Lambert, David -- Khartanovich, Valeri -- Wall, Jeffrey D -- Nigst, Philip R -- Foley, Robert A -- Lahr, Marta Mirazon -- Nielsen, Rasmus -- Orlando, Ludovic -- Willerslev, Eske -- New York, N.Y. -- Science. 2014 Nov 28;346(6213):1113-8. doi: 10.1126/science.aaa0114. Epub 2014 Nov 6.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. ; Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK. ; Department of Human Genetics, University of Chicago, 920 East 58th Street, Cummings Life Science Center, Chicago, IL 60637, USA. The Bioinformatics Center, University of Copenhagen, Ole Maaloes Vej 5, 2200 Kobenhavn N, Denmark. ; The Bioinformatics Center, University of Copenhagen, Ole Maaloes Vej 5, 2200 Kobenhavn N, Denmark. ; Environmental Futures Research Institute, Griffith University, 170 Kessels Road, Nathan, Brisbane, Queensland 4111, Australia. ; Department of Physical Anthropology, Kunstkamera, Peter the Great Museum of Anthropology and Ethnography, Russian Academy of Sciences, 24 Srednii Prospect, Vassilievskii Island, St. Petersburg, Russia. ; Center for the Study of the First Americans and Department of Anthropology, Texas A&M University, TAMU-4352, College Station, Texas 77845-4352, USA. ; Department of Epidemiology and Biostatistics, University of California San Francisco, 185 Berry Street, Lobby 5, Suite 5700, San Francisco, CA 94107, USA. ; Division of Archaeology, University of Cambridge, Cambridge, Downing Street, CB2 3DZ, UK. Department of Human Evolution, Max-Planck-Institute for Evolutionary Anthropology, Leipzig, Deutscher Platz 6, D-04103, Germany. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Leverhulme Centre for Human Evolutionary Studies, Department of Archaeology and Anthropology, University of Cambridge, Cambridge, Fitzwilliam Street, CB2 1QH, UK. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. Leverhulme Centre for Human Evolutionary Studies, Department of Archaeology and Anthropology, University of Cambridge, Cambridge, Fitzwilliam Street, CB2 1QH, UK. ewillerslev@snm.ku.dk rasmus_nielsen@berkeley.edu mbml1@cam.ac.uk. ; Environmental Futures Research Institute, Griffith University, 170 Kessels Road, Nathan, Brisbane, Queensland 4111, Australia. ewillerslev@snm.ku.dk rasmus_nielsen@berkeley.edu mbml1@cam.ac.uk. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Oster Voldgade 5-7, 1350 Copenhagen, Denmark. ewillerslev@snm.ku.dk rasmus_nielsen@berkeley.edu mbml1@cam.ac.uk.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25378462" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Friend, Stephen H -- Schadt, Eric E -- New York, N.Y. -- Science. 2014 May 30;344(6187):970-2. doi: 10.1126/science.1255648.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Sage Bionetworks, Seattle, WA, 98109 USA Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences and the Icahn Institute for Genomics and Multiscale Biology, New York, NY 10029, USA. friend@sagebase.org eric.schadt@exchange.mssm.edu. ; Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences and the Icahn Institute for Genomics and Multiscale Biology, New York, NY 10029, USA. friend@sagebase.org eric.schadt@exchange.mssm.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24876479" target="_blank"〉PubMed〈/a〉

Description: Introgressive hybridization is now recognized as a widespread phenomenon, but its role in evolution remains contested. Here, we use newly available reference genome assemblies to investigate phylogenetic relationships and introgression in a medically important group of Afrotropical mosquito sibling species. We have identified the correct species branching order to resolve a contentious phylogeny and show that lineages leading to the principal vectors of human malaria were among the first to split. Pervasive autosomal introgression between these malaria vectors means that only a small fraction of the genome, mainly on the X chromosome, has not crossed species boundaries. Our results suggest that traits enhancing vectorial capacity may be gained through interspecific gene flow, including between nonsister species.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4380269/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4380269/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Fontaine, Michael C -- Pease, James B -- Steele, Aaron -- Waterhouse, Robert M -- Neafsey, Daniel E -- Sharakhov, Igor V -- Jiang, Xiaofang -- Hall, Andrew B -- Catteruccia, Flaminia -- Kakani, Evdoxia -- Mitchell, Sara N -- Wu, Yi-Chieh -- Smith, Hilary A -- Love, R Rebecca -- Lawniczak, Mara K -- Slotman, Michel A -- Emrich, Scott J -- Hahn, Matthew W -- Besansky, Nora J -- HHSN272200900039C/PHS HHS/ -- R01 AI063508/AI/NIAID NIH HHS/ -- R01 AI076584/AI/NIAID NIH HHS/ -- R01 AI104956/AI/NIAID NIH HHS/ -- R01AI076584/AI/NIAID NIH HHS/ -- R01AI085079/AI/NIAID NIH HHS/ -- R01AI104956/AI/NIAID NIH HHS/ -- R21 AI101459/AI/NIAID NIH HHS/ -- R21AI094289/AI/NIAID NIH HHS/ -- R21AI099528/AI/NIAID NIH HHS/ -- R21AI101459/AI/NIAID NIH HHS/ -- T32GM007757/GM/NIGMS NIH HHS/ -- Medical Research Council/United Kingdom -- New York, N.Y. -- Science. 2015 Jan 2;347(6217):1258524. doi: 10.1126/science.1258524. Epub 2014 Nov 27.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA. Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA. ; Department of Biology, Indiana University, Bloomington, IN 47405, USA. ; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA. ; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA. The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA. Department of Genetic Medicine and Development, University of Geneva Medical School, rue Michel-Servet 1, 1211 Geneva, Switzerland. Swiss Institute of Bioinformatics, rue Michel-Servet 1, 1211 Geneva, Switzerland. ; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA. ; Department of Entomology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. The Interdisciplinary PhD Program in Genetics, Bioinformatics, and Computational Biology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. ; The Interdisciplinary PhD Program in Genetics, Bioinformatics, and Computational Biology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. ; Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA 02115, USA. Dipartimento di Medicina Sperimentale e Scienze Biochimiche, Universita degli Studi di Perugia, Perugia, Italy. ; Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA 02115, USA. ; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA. ; Department of Life Sciences, Imperial College London, South Kensington Campus, London SW7 2AZ, UK. ; Department of Entomology, Texas A&M University, College Station, TX 77843, USA. ; Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA. ; Department of Biology, Indiana University, Bloomington, IN 47405, USA. School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. mwh@indiana.edu nbesansk@nd.edu. ; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA. Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA. mwh@indiana.edu nbesansk@nd.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25431491" target="_blank"〉PubMed〈/a〉