ALBERT

Description: RNA molecules are attractive therapeutic targets because non-coding RNA molecules have increasingly been found to play key regulatory roles in the cell. Comparing and classifying RNA 3D structures yields unique insights into RNA evolution and function. With the rapid increase in the number of atomic-resolution RNA structures, it is crucial to have effective tools to classify RNA structures and to investigate them for structural similarities at different resolutions. We previously developed the algorithm CLICK to superimpose a pair of protein 3D structures by clique matching and 3D least squares fitting. In this study, we extend and optimize the CLICK algorithm to superimpose pairs of RNA 3D structures and RNA–protein complexes, independent of the associated topologies. Benchmarking Rclick on four different datasets showed that it is either comparable to or better than other structural alignment methods in terms of the extent of structural overlaps. Rclick also recognizes conformational changes between RNA structures and produces complementary alignments to maximize the extent of detectable similarity. Applying Rclick to study Ribonuclease III protein correctly aligned the RNA binding sites of RNAse III with its substrate. Rclick can be further extended to identify ligand-binding pockets in RNA. A web server is developed at http://mspc.bii.a-star.edu.sg/minhn/rclick.html .

Description: The goal of pathway analysis is to identify the pathways that are significantly impacted when a biological system is perturbed, e.g. by a disease or drug. Current methods treat pathways as independent entities. However, many signals are constantly sent from one pathway to another, essentially linking all pathways into a global, system-wide complex. In this work, we propose a set of three pathway analysis methods based on the impact analysis, that performs a system-level analysis by considering all signals between pathways, as well as their overlaps. Briefly, the global system is modeled in two ways: (i) considering the inter-pathway interaction exchange for each individual pathways, and (ii) combining all individual pathways to form a global, system-wide graph. The third analysis method is a hybrid of these two models. The new methods were compared with DAVID, GSEA, GSA, PathNet, Crosstalk and SPIA on 23 GEO data sets involving 19 tissues investigated in 12 conditions. The results show that both the ranking and the P -values of the target pathways are substantially improved when the analysis considers the system-wide dependencies and interactions between pathways.

Description: We study a model of lava flow to determine its thermal and dynamic characteristics from thermal measurements of the lava at its surface. Mathematically this problem is reduced to solving an inverse boundary problem. Namely, using known conditions at one part of the model boundary we determine the missing condition at the remaining part of the boundary. We develop a numerical approach to the mathematical problem in the case of steady-state flow. Assuming that the temperature and the heat flow are prescribed at the upper surface of the model domain, we determine the flow characteristics in the entire model domain using a variational (adjoint) method. We have performed computations of model examples and showed that in the case of smooth input data the lava temperature and the flow velocity can be reconstructed with a high accuracy. As expected, a noise imposed on the smooth input data results in a less accurate solution, but still acceptable below some noise level. Also we analyse the influence of optimization methods on the solution convergence rate. The proposed method for reconstruction of physical parameters of lava flows can also be applied to other problems in geophysical fluid flows.

Description: Studies of discrete volcanic explosions, that usually last less than 2 or 3 min, have suggested that the partitioning of seismic-acoustic energy is likely related to a range of physical mechanisms that depend on magma properties and other physical constraints such as the location of the fragmentation surface. In this paper, we explore the energy partition of a paroxysmal eruptive phase of Tungurahua volcano that lasted for over 4 hr, on 2006 July 14–15, using seismic-acoustic information recorded by stations on its flanks (near field). We find evidence of a linear scaling between seismic and acoustic energies, with time-dependent intensities, during the sustained explosive phase of the eruption. Furthermore, we argue that this scaling can be explained by two different processes: (1) the fragmentation region ultimately acts as the common source of energy producing both direct seismic waves, that travel through the volcanic edifice, and direct acoustic waves coming from a disturbed atmosphere above the summit; (2) the coupling of acoustic waves with the ground to cause seismic waves. Both processes are concurrent, however we have found that the first one is dominant for seismic records below 4 Hz. Here we use the linear scaling of intensities to construct seismic and acoustic indices, which, we argue, could be used to track an ongoing eruption. Thus, especially in strong paroxysms that can produce pyroclastic flows, the index correlation and their levels can be used as quantitative monitoring parameters to assess the volcanic hazard in real time. Additionally, we suggest from the linear scaling that the source type for both cases, seismic and acoustic, is dipolar and dominant in the near field.

Description: Bioinformatic analysis often produces large sets of genomic ranges that can be difficult to interpret in the absence of genomic context. Goldmine annotates genomic ranges from any source with gene model and feature contexts to facilitate global descriptions and candidate loci discovery. We demonstrate the value of genomic context by using Goldmine to elucidate context dynamics in transcription factor binding and to reveal differentially methylated regions (DMRs) with context-specific functional correlations. The open source R package and documentation for Goldmine are available at http://jeffbhasin.github.io/goldmine .

Description: CCCTC-binding factor (CTCF) is a multi-functional protein that is assigned various, even contradictory roles in the genome. High-throughput sequencing-based technologies such as ChIP-seq and Hi-C provided us the opportunity to assess the multivalent functions of CTCF in the human genome. The location of CTCF-binding sites with respect to genomic features provides insights into the possible roles of this protein. Here we present the first genome-wide survey and characterization of three important functions of CTCF: enhancer insulator, chromatin barrier and enhancer linker. We developed a novel computational framework to discover the multivalent functions of CTCF based on chromatin state and three-dimensional chromatin architecture. We applied our method to five human cell lines and identified ~46 000 non-redundant CTCF sites related to the three functions. Disparate effects of these functions on gene expression were found and distinct genomic features of these CTCF sites were characterized in GM12878 cells. Finally, we investigated the cell-type specificities of CTCF sites related to these functions across five cell types. Our study provides new insights into the multivalent functions of CTCF in the human genome.

Description: Existing methods for interpreting protein variation focus on annotating mutation pathogenicity rather than detailed interpretation of variant deleteriousness and frequently use only sequence-based or structure-based information. We present VIPUR, a computational framework that seamlessly integrates sequence analysis and structural modelling (using the Rosetta protein modelling suite) to identify and interpret deleterious protein variants. To train VIPUR, we collected 9477 protein variants with known effects on protein function from multiple organisms and curated structural models for each variant from crystal structures and homology models. VIPUR can be applied to mutations in any organism's proteome with improved generalized accuracy (AUROC .83) and interpretability (AUPR .87) compared to other methods. We demonstrate that VIPUR's predictions of deleteriousness match the biological phenotypes in ClinVar and provide a clear ranking of prediction confidence. We use VIPUR to interpret known mutations associated with inflammation and diabetes, demonstrating the structural diversity of disrupted functional sites and improved interpretation of mutations associated with human diseases. Lastly, we demonstrate VIPUR's ability to highlight candidate variants associated with human diseases by applying VIPUR to de novo variants associated with autism spectrum disorders.

Description: The expansion of offshore renewable energy infrastructure and the need for trans-continental shelf power transmission require the use of submarine high-voltage (HV) cables. These cables have maximum operating surface temperatures of up to 70 °C and are typically buried 1–2 m beneath the seabed, within the wide range of substrates found on the continental shelf. However, the heat flow pattern and potential effects on the sedimentary environments around such anomalously high heat sources in the near-surface sediments are poorly understood. We present temperature measurements from a 2-D laboratory experiment representing a buried submarine HV cable, and identify the thermal regimes generated within typical unconsolidated shelf sediments—coarse silt, fine sand and very coarse sand. We used a large (2 x 2.5 m 2 ) tank filled with water-saturated spherical glass beads (ballotini) and instrumented with a buried heat source and 120 thermocouples to measure the time-dependent 2-D temperature distributions. The observed and corresponding Finite Element Method simulations of the steady state heat flow regimes and normalized radial temperature distributions were assessed. Our results show that the heat transfer and thus temperature fields generated from submarine HV cables buried within a range of sediments are highly variable. Coarse silts are shown to be purely conductive, producing temperature increases of 〉10 °C up to 40 cm from the source of 60 °C above ambient; fine sands demonstrate a transition from conductive to convective heat transfer between cf. 20 and 36 °C above ambient, with 〉10 °C heat increases occurring over a metre from the source of 55 °C above ambient; and very coarse sands exhibit dominantly convective heat transfer even at very low ( cf. 7 °C) operating temperatures and reaching temperatures of up to 18 °C above ambient at a metre from the source at surface temperatures of only 18 °C. These findings are important for the surrounding near-surface environments experiencing such high temperatures and may have significant implications for chemical and physical processes operating at the grain and subgrain scale; biological activity at both microfaunal and macrofaunal levels; and indeed the operational performance of the cables themselves, as convective heat transport would increase cable current ratings, something neglected in existing standards.

Description: The sequential chain of interactions altering the binary state of a biomolecule represents the ‘information flow’ within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein–protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes—network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code ( http://www.NetDecoder.org ) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets.

Description: It is being increasingly realized that nucleosome organization on DNA crucially regulates DNA–protein interactions and the resulting gene expression. While the spatial character of the nucleosome positioning on DNA has been experimentally and theoretically studied extensively, the temporal character is poorly understood. Accounting for ATPase activity and DNA-sequence effects on nucleosome kinetics, we develop a theoretical method to estimate the time of continuous exposure of binding sites of non-histone proteins (e.g. transcription factors and TATA binding proteins) along any genome. Applying the method to Saccharomyces cerevisiae , we show that the exposure timescales are determined by cooperative dynamics of multiple nucleosomes, and their behavior is often different from expectations based on static nucleosome occupancy. Examining exposure times in the promoters of GAL1 and PHO5, we show that our theoretical predictions are consistent with known experiments. We apply our method genome-wide and discover huge gene-to-gene variability of mean exposure times of TATA boxes and patches adjacent to TSS (+1 nucleosome region); the resulting timescale distributions have non-exponential tails.

Description: The presence of calcite in and near faults, as the dominant material, cement, or vein fill, indicates that the mechanical behaviour of carbonate-dominated material likely plays an important role in shallow- and mid-crustal faulting. To better understand the behaviour of calcite, under loading conditions relevant to earthquake nucleation, we sheared powdered gouge of Carrara Marble, 〉98 per cent CaCO 3 , at constant normal stresses between 1 and 100 MPa under water-saturated conditions at room temperature. We performed slide-hold-slide tests, 1–3000 s, to measure the amount of static frictional strengthening and creep relaxation, and velocity-stepping tests, 0.1–1000 μm s –1 , to evaluate frictional stability. We observe that the rates of frictional strengthening and creep relaxation decrease with increasing normal stress and diverge as shear velocity is increased from 1 to 3000 μm s –1 during slide-hold-slide experiments. We also observe complex frictional stability behaviour that depends on both normal stress and shearing velocity. At normal stresses less than 20 MPa, we observe predominantly velocity-neutral friction behaviour. Above 20 MPa, we observe strong velocity-strengthening frictional behaviour at low velocities, which then evolves towards velocity-weakening friction behaviour at high velocities. Microstructural analyses of recovered samples highlight a variety of deformation mechanisms including grain size reduction and localization, folding of calcite grains and fluid-assisted diffusion mass transfer processes promoting the development of calcite nanograins in the highly deformed portions of the experimental fault. Our combined analyses indicate that calcite fault gouge transitions from brittle to semi-brittle behaviour at high normal stress and slow sliding velocities. This transition has important implications for earthquake nucleation and propagation on faults in carbonate-dominated lithologies.

Description: The elastic properties of homogeneous, isotropic materials are well constrained. However, in heterogeneous and evolving materials, these essential properties are less well-explored. During sintering of volcanic ash particles by viscous processes as well as during compaction and cementation of sediments, microstructure and porosity undergo changes that affect bulk dynamic elastic properties. Here using a model system of glass particles as an analogue for initially granular rock-forming materials, we have determined porosity and P -wave velocity during densification. Using these results, we test models for the kinetics of densification and the resultant evolution of the elastic properties to derive a quantitative description of the coupling between the kinetics of isotropic densification and the evolving dynamic elastic moduli. We demonstrate the power of the resultant model on a wide range of data for non-coherent sediments as well as sedimentary and volcanic rocks. We propose that such constraints be viewed as an essential ingredient of time-dependent models for the deformation of evolving materials in volcanoes and sedimentary basins.

Description: We present a study of the seismic velocity variations that occurred in the structure before the large 2010 eruption of Merapi volcano. For the first time to our knowledge, the technique of coda wave interferometry is applied to both families of similar events (multiplets) and to correlation functions of seismic noise. About half of the seismic events recorded at the summit stations belong to one of the ten multiplets identified, including 120 similar events that occurred in the last 20 hr preceding the eruption onset. Daily noise cross-correlation functions (NCF) were calculated for the six pairs of short-period stations available. Using the stretching method, we estimate time-series of apparent velocity variation (AVV) for each multiplet and each pair of stations. No significant velocity change is detected until September 2010. From 10 October to the beginning of the eruption on 26 October, a complex pattern of AVV is observed with amplitude of up to ±1.5 per cent. Velocity decrease is first observed from families of deep events and then from shallow earthquakes. In the same period, AVV with different signs and chronologies are estimated from NCF calculated for various station pairs. The location in the horizontal plane of the velocity perturbations related with the AVV obtained from NCF is estimated by using an approach based on the radiative transfer approximation. Although their spatial resolution is limited, the resulting maps display velocity decrease in the upper part of the edifice in the period 12–25 October. After the eruption onset, the pattern of velocity perturbations is significantly modified with respect to the previous one. We interpret these velocity variations in the framework of a scenario of magmatic intrusion that integrates most observations. The perturbation of the stress field associated with the magma migration can induce both decrease and increase of the seismic velocity of rocks. Thus the detected AVVs can be considered as precursors of volcanic eruptions in andesitic volcanoes, without taking their sign into account.

Description: The yeast mutant collections are a fundamental tool in deciphering genomic organization and function. Over the last decade, they have been used for the systematic exploration of ~6 000 000 double gene mutants, identifying and cataloging genetic interactions among them. Here we studied the extent to which these data are prone to neighboring gene effects (NGEs), a phenomenon by which the deletion of a gene affects the expression of adjacent genes along the genome. Analyzing ~90,000 negative genetic interactions observed to date, we found that more than 10% of them are incorrectly annotated due to NGEs. We developed a novel algorithm, GINGER, to identify and correct erroneous interaction annotations. We validated the algorithm using a comparative analysis of interactions from Schizosaccharomyces pombe . We further showed that our predictions are significantly more concordant with diverse biological data compared to their mis-annotated counterparts. Our work uncovered about 9500 new genetic interactions in yeast.

Description: Transfer RNAs (tRNAs) are essential for encoding the transcribed genetic information from DNA into proteins. Variations in the human tRNAs are involved in diverse clinical phenotypes. Interestingly, all pathogenic variations in tRNAs are located in mitochondrial tRNAs (mt-tRNAs). Therefore, it is crucial to identify pathogenic variations in mt-tRNAs for disease diagnosis and proper treatment. We collected mt-tRNA variations using a classification based on evidence from several sources and used the data to develop a multifactorial probability-based prediction method, PON-mt-tRNA, for classification of mt-tRNA single nucleotide substitutions. We integrated a machine learning-based predictor and an evidence-based likelihood ratio for pathogenicity using evidence of segregation, biochemistry and histochemistry to predict the posterior probability of pathogenicity of variants. The accuracy and Matthews correlation coefficient (MCC) of PON-mt-tRNA are 1.00 and 0.99, respectively. In the absence of evidence from segregation, biochemistry and histochemistry, PON-mt-tRNA classifies variations based on the machine learning method with an accuracy and MCC of 0.69 and 0.39, respectively. We classified all possible single nucleotide substitutions in all human mt-tRNAs using PON-mt-tRNA. The variations in the loops are more often tolerated compared to the variations in stems. The anticodon loop contains comparatively more predicted pathogenic variations than the other loops. PON-mt-tRNA is available at http://structure.bmc.lu.se/PON-mt-tRNA/ .

Description: The influx of groundwater into hot kimberlite deposits results in the reaction of water with olivine-rich rocks. The products of the reaction are serpentine and release of latent heat. The rise of temperature due to the heat release increases the rate of the reaction. Under certain conditions, this self-speeding up of the reaction can result in instabilities associated with a significantly higher final serpentinization in slightly warmer regions of the kimberlite deposit. We conduct linear stability analysis of serpentinization in an isolated volume of porous kimberlitic rocks saturated with water and an inert gas. There is a counteracting interplay between the heat release tending to destabilize the uniform distribution of parameters and the heat conduction tending to stabilize it by smoothing out temperature perturbations. We determine the critical spatial scale separating the parameters where one phenomenon dominates over another. The perturbations of longer-than-critical length grow, whereas the perturbations of shorter-than-critical length fade. The analytical results of the linear stability analysis are supported by direct numerical simulations using a full nonlinear model.

Description: Inelastic deformation can either occur with dilatancy or compaction, implying differences in porosity changes, failure and petrophysical properties. In this study, the roles of water as a pore fluid, and of temperature, on the deformation and failure of a micritic limestone (white Tavel limestone, porosity 14.7 per cent) were investigated under triaxial stresses. For each sample, a hydrostatic load was applied up to the desired confining pressure (from 0 up to 85 MPa) at either room temperature or at 70 °C. Two pore fluid conditions were investigated at room temperature: dry and water saturated. The samples were deformed up to failure at a constant strain rate of ~10 –5 s –1 . The experiments were coupled with ultrasonic wave velocity surveys to monitor crack densities. The linear trend between the axial crack density and the relative volumetric strain beyond the onset of dilatancy suggests that cracks propagate at constant aspect ratio. The decrease of ultrasonic wave velocities beyond the onset of inelastic compaction in the semi-brittle regime indicates the ongoing interplay of shear-enhanced compaction and crack development. Water has a weakening effect on the onset of dilatancy in the brittle regime, but no measurable influence on the peak strength. Temperature lowers the confining pressure at which the brittle–semi-brittle transition is observed but does not change the stress states at the onset of inelastic compaction and at the post-yield onset of dilatancy.

Description: Sequence Logos and its variants are the most commonly used method for visualization of multiple sequence alignments (MSAs) and sequence motifs. They provide consensus-based summaries of the sequences in the alignment. Consequently, individual sequences cannot be identified in the visualization and covariant sites are not easily discernible. We recently proposed Sequence Bundles , a motif visualization technique that maintains a one-to-one relationship between sequences and their graphical representation and visualizes covariant sites. We here present Alvis, an open-source platform for the joint explorative analysis of MSAs and phylogenetic trees, employing Sequence Bundles as its main visualization method. Alvis combines the power of the visualization method with an interactive toolkit allowing detection of covariant sites, annotation of trees with synapomorphies and homoplasies, and motif detection. It also offers numerical analysis functionality, such as dimension reduction and classification. Alvis is user-friendly, highly customizable and can export results in publication-quality figures. It is available as a full-featured standalone version ( http://www.bitbucket.org/rfs/alvis ) and its Sequence Bundles visualization module is further available as a web application ( http://science-practice.com/projects/sequence-bundles ).

Description: Remote sensing thermal data of active lava flows allow the evaluation of effusion rates. This is made possible by a simple formula relating the lava effusion rate to the heat flux radiated per unit time from the surface of the flow. Due to the assumptions of the model, this formula implies that heat flux, surface temperature and lava temperature vary as a function of the flow thickness. These relationships, never verified or validated before, have been used by several authors as a proof of the weakness of the model. Here, multispectral infrared and visible imaging spectrometer (MIVIS) high spatial resolution (5–10 m) thermal data acquired during Etna's 2001 eruption were used to investigate downflow heat flux variations in the lava flow emitted from a vent located at 2100 m a.s.l. A high correlation between the downflow heat flux and the lava flow thickness (measured from a pre-existing digital elevation model) was found. Topography beneath the flow appears to play an important role both in lava emplacement mechanisms and flow dynamics. MIVIS-derived downflow effusion rates are consistent with the law of conservation of mass assessing the reliability of remote sensing techniques.

Description: Functional RNA regions are often related to recurrent secondary structure patterns (or motifs), which can exert their role in several different ways, particularly in dictating the interaction with RNA-binding proteins, and acting in the regulation of a large number of cellular processes. Among the available motif-finding tools, the majority focuses on sequence patterns, sometimes including secondary structure as additional constraints to improve their performance. Nonetheless, secondary structures motifs may be concurrent to their sequence counterparts or even encode a stronger functional signal. Current methods for searching structural motifs generally require long pipelines and/or high computational efforts or previously aligned sequences. Here, we present BEAM (BEAr Motif finder), a novel method for structural motif discovery from a set of unaligned RNAs, taking advantage of a recently developed encoding for RNA secondary structure named BEAR (Brand nEw Alphabet for RNAs) and of evolutionary substitution rates of secondary structure elements. Tested in a varied set of scenarios, from small- to large-scale, BEAM is successful in retrieving structural motifs even in highly noisy data sets, such as those that can arise in CLIP-Seq or other high-throughput experiments.

Description: Whole exome sequencing (WES) accelerates disease gene discovery using rare genetic variants, but further statistical and functional evidence is required to avoid false-discovery. To complement variant-driven disease gene discovery, here we present function-driven disease gene discovery in zebrafish ( Danio rerio ), a promising human disease model owing to its high anatomical and genomic similarity to humans. To facilitate zebrafish-based function-driven disease gene discovery, we developed a genome-scale co-functional network of zebrafish genes, DanioNet ( www.inetbio.org/danionet ), which was constructed by Bayesian integration of genomics big data. Rigorous statistical assessment confirmed the high prediction capacity of DanioNet for a wide variety of human diseases. To demonstrate the feasibility of the function-driven disease gene discovery using DanioNet, we predicted genes for ciliopathies and performed experimental validation for eight candidate genes. We also validated the existence of heterozygous rare variants in the candidate genes of individuals with ciliopathies yet not in controls derived from the UK10K consortium, suggesting that these variants are potentially involved in enhancing the risk of ciliopathies. These results showed that an integrated genomics big data for a model animal of diseases can expand our opportunity for harnessing WES data in disease gene discovery.

Description: Population-scale sequencing is increasingly uncovering large numbers of rare single-nucleotide variants (SNVs) in coding regions of the genome. The rarity of these variants makes it challenging to evaluate their deleteriousness with conventional phenotype–genotype associations. Protein structures provide a way of addressing this challenge. Previous efforts have focused on globally quantifying the impact of SNVs on protein stability. However, local perturbations may severely impact protein functionality without strongly disrupting global stability (e.g. in relation to catalysis or allostery). Here, we describe a workflow in which localized frustration, quantifying unfavorable local interactions, is employed as a metric to investigate such effects. Using this workflow on the Protein Databank, we find that frustration produces many immediately intuitive results: for instance, disease-related SNVs create stronger changes in localized frustration than non-disease related variants, and rare SNVs tend to disrupt local interactions to a larger extent than common variants. Less obviously, we observe that somatic SNVs associated with oncogenes and tumor suppressor genes (TSGs) induce very different changes in frustration. In particular, those associated with TSGs change the frustration more in the core than the surface (by introducing loss-of-function events), whereas those associated with oncogenes manifest the opposite pattern, creating gain-of-function events.

Description: A complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a single sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e. a sample-specific network (SSN) method, which allows us to construct individual-specific networks based on molecular expressions of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such SSNs can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various types of cancer. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e. we can even identify such drug resistance genes that actually have no clear differential expression between samples with and without the resistance, due to the additional network information.

Description: Motivation: Many biological processes, such as cell cycle, circadian clock, menstrual cycles, are governed by oscillatory systems consisting of numerous components that exhibit rhythmic patterns over time. It is not always easy to identify such rhythmic components. For example, it is a challenging problem to identify circadian genes in a given tissue using time-course gene expression data. There is a great potential for misclassifying non-rhythmic as rhythmic genes and vice versa. This has been a problem of considerable interest in recent years. In this article we develop a constrained inference based methodology called Order Restricted Inference for Oscillatory Systems (ORIOS) to detect rhythmic signals. Instead of using mathematical functions (e.g. sinusoidal) to describe shape of rhythmic signals, ORIOS uses mathematical inequalities. Consequently, it is robust and not limited by the biologist's choice of the mathematical model. We studied the performance of ORIOS using simulated as well as real data obtained from mouse liver, pituitary gland and data from NIH3T3, U2OS cell lines. Our results suggest that, for a broad collection of patterns of gene expression, ORIOS has substantially higher power to detect true rhythmic genes in comparison to some popular methods, while also declaring substantially fewer non-rhythmic genes as rhythmic. Availability and Implementation: A user friendly code implemented in R language can be downloaded from http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/peddada/index.cfm . Contact: peddada@niehs.nih.gov

Description: We performed complex conductivity measurements on 28 core samples from the hole drilled for the Humu'ula Groundwater Research Project (Hawai'i Island, HI, USA). The complex conductivity measurements were performed at 4 different pore water conductivities (0.07, 0.5, 1.0 or 2.0, and 10 S m –1 prepared with NaCl) over the frequency range 1 mHz to 45 kHz at 22 ± 1 °C. The in-phase conductivity data are plotted against the pore water conductivity to determine, sample by sample, the intrinsic formation factor and the surface conductivity. The intrinsic formation factor is related to porosity by Archie's law with an average value of the cementation exponent m of 2.45, indicating that only a small fraction of the connected pore space controls the transport properties. Both the surface and quadrature conductivities are found to be linearly related to the cation exchange capacity of the material, which was measured with the cobalt hexamine chloride method. Surface and quadrature conductivities are found to be proportional to each other like for sedimentary siliclastic rocks. A Stern layer polarization model is used to explain these experimental results. Despite the fact that the samples contain some magnetite (up to 5 per cent wt.), we were not able to identify the effect of this mineral on the complex conductivity spectra. These results are very encouraging in showing that galvanometric induced polarization measurements can be used in volcanic areas to separate the bulk from the surface conductivity and therefore to define some alteration attributes. Such a goal cannot be achieved with resistivity alone.

Description: We investigate the relationship between complex conductivity spectra and both permeability and pore mean size and distribution of 22 core samples (21 volcanic rocks and 1 clayey sandstone). The volcanic core samples were extracted from a wellbore drilled for the Humu‘ula Groundwater Research Project in the Humu‘ula saddle region between Mauna Kea and Mauna Loa volcanoes (Hawaii). The quadrature conductivity spectra of volcanic rocks exhibit a subtle, but generally detectable, relaxation frequency in the range 0.3 Hz to 45 kHz similar to the relaxation frequency observed for clayey sandstones. We find a fair relationship between this relaxation frequency and the pore size determined by mercury porosimetry. Combined with the intrinsic formation factor of the core samples, the relaxation frequency can be used as an indicator of the permeability of the material. The predicted values of the permeability are grossly consistent with the permeability values to air (in the range 0.001–100 mD) within two orders of magnitude. The measured permeability values are highly correlated to the peak of the pore size distribution determined from mercury porosimetry divided by the intrinsic formation factor. By fitting the complex conductivity spectra with the pore size distribution, we obtain the normalized chargeability of the core samples, which is, in turn, highly correlated to the measured cation exchange capacity.

Description: The Puyehue-Cordón Caulle Volcanic Complex (PCCVC) is one of the best examples of tectonic control on volcanism at the Southern Volcanic Zone of the Andes (southern Chile). The PCCVC comprises several volcanic centres that erupted dominantly SiO 2 -rich magmas at the intersection of the trench-parallel Liquiñe-Ofqui Fault Zone (LOFZ) and an inherited NW–SE basement structure. The PCCVC began an explosive and later effusive eruption on 2011 June 4 causing decimetre- to metre-scale surface deformation that was observed by a series of Envisat ASAR satellite scenes. We modelled this data and complemented it with time-series of two continuous GPS stations and seismicity recorded by a local network. Deformation during the first 3 days of the eruption can be modelled either by two point sources aligned with the NW–SE Cordón Caulle graben or by a closing dyke with a significant component of left-lateral motion along the graben. These models are discussed with respect to their implications on the estimated rheology and the eruption mechanism. GPS observations near the volcanic complex reveal an additional, more localised effect related to the LOFZ in the south of the complex. Coeruptive deformation at the main geological structures of the PCCVC is further supported by relocated seismicity, which is concentrated along the Cordón Caulle graben and to the western side of the LOFZ.

Description: Coal fires are severe hazards to environment, health and safety throughout the world. Efficient and economical extinguishing of these fires requires that the extent of the subsurface coal fires should be delineated. Electrical and electromagnetic methods have been used to detect coal fires in recent years. However, the resistivity change of coal-bearing rocks at high temperature is rarely investigated. The resistivity characteristics of coal fires at different temperatures and depths are seldomly researched as well. In this paper, we present the results of measurements of several coal-bearing rocks’ resistivity and permeability under high temperature. Two major causes for the change in resistivity with increasing temperature are recognized, there are the increase of charge carriers and thermal fracturing, of which the first one is probably the dominant cause. A set of 2-D simulations is carried out to compare the relation of resolution and efficiency of coal fires detection to temperature and depth when adopting the electrical resistance tomography. The simulation results show that the resolution and efficiency decrease with the decrease of temperature and the increase of depth. Finally, the electrical resistance tomography is used to delineate coal fires in the Anjialing Open Pit Mine. Most low-resistivity regions are verified as coal-fire areas according to the long-term monitoring of borehole temperature. The results indicate that the electrical resistance tomography can be used as a tool for the detection of coal fires.

Description: Accurate information on thermal conductivity and thermal diffusivity of materials is of central importance in relation to geoscience and engineering problems involving the transfer of heat. Several methods, including the classical divided bar technique, are available for laboratory measurements of thermal conductivity, but much fewer for thermal diffusivity. We have generalized the divided bar technique to the transient case in which thermal conductivity, volumetric heat capacity and thereby also thermal diffusivity are measured simultaneously. As the density of samples is easily determined independently, specific heat capacity can also be determined. The finite element formulation provides a flexible forward solution for heat transfer across the bar, and thermal properties are estimated by inverse Monte Carlo modelling. This methodology enables a proper quantification of experimental uncertainties on measured thermal properties and information on their origin. The developed methodology was applied to various materials, including a standard ceramic material and different rock samples, and measuring results were compared with results applying traditional steady-state divided bar and an independent line-source method. All measurements show highly consistent results and with excellent reproducibility and high accuracy. For conductivity the obtained uncertainty is typically 1–3 per cent, and for diffusivity uncertainty may be reduced to about 3–5 per cent. The main uncertainty originates from the presence of thermal contact resistance associated with the internal interfaces in the bar. These are not resolved during inversion and it is imperative that they are minimized. The proposed procedure is simple and may quite easily be implemented to the many steady-state divided bar systems in operation. A thermally controlled bath, as applied here, may not be needed. Simpler systems, such as applying temperature-controlled water directly from a tap, may also be applied.

Description: Sequence alignment is a long standing problem in bioinformatics. The Basic Local Alignment Search Tool (BLAST) is one of the most popular and fundamental alignment tools. The explosive growth of biological sequences calls for speedup of sequence alignment tools such as BLAST. To this end, we develop high speed BLASTN (HS-BLASTN), a parallel and fast nucleotide database search tool that accelerates MegaBLAST—the default module of NCBI-BLASTN. HS-BLASTN builds a new lookup table using the FMD-index of the database and employs an accurate and effective seeding method to find short stretches of identities (called seeds) between the query and the database. HS-BLASTN produces the same alignment results as MegaBLAST and its computational speed is much faster than MegaBLAST. Specifically, our experiments conducted on a 12-core server show that HS-BLASTN can be 22 times faster than MegaBLAST and exhibits better parallel performance than MegaBLAST. HS-BLASTN is written in C++ and the related source code is available at https://github.com/chenying2016/queries under the GPLv3 license.

Description: Model evaluation is a necessary step for better prediction and design of 3D RNA structures. For proteins, this has been widely studied and the knowledge-based statistical potential has been proved to be one of effective ways to solve this problem. Currently, a few knowledge-based statistical potentials have also been proposed to evaluate predicted models of RNA tertiary structures. The benchmark tests showed that they can identify the native structures effectively but further improvements are needed to identify near-native structures and those with non-canonical base pairs. Here, we present a novel knowledge-based potential, 3dRNAscore, which combines distance-dependent and dihedral-dependent energies. The benchmarks on different testing datasets all show that 3dRNAscore are more efficient than existing evaluation methods in recognizing native state from a pool of near-native states of RNAs as well as in ranking near-native states of RNA models.

Description: MicroRNAs (miRNAs) regulate gene expression by binding to partially complementary sequences on target mRNA transcripts, thereby causing their degradation, deadenylation, or inhibiting their translation. Genomic variants can alter miRNA regulation by modifying miRNA target sites, and multiple human disease phenotypes have been linked to such miRNA target site variants (miR-TSVs). However, systematic genome-wide identification of functional miR-TSVs is difficult due to high false positive rates; functional miRNA recognition sequences can be as short as six nucleotides, with the human genome encoding thousands of miRNAs. Furthermore, while large-scale clinical genomic data sets are becoming increasingly commonplace, existing miR-TSV prediction methods are not designed to analyze these data. Here, we present an open-source tool called SubmiRine that is designed to perform efficient miR-TSV prediction systematically on variants identified in novel clinical genomic data sets. Most importantly, SubmiRine allows for the prioritization of predicted miR-TSVs according to their relative probability of being functional. We present the results of SubmiRine using integrated clinical genomic data from a large-scale cohort study on chronic obstructive pulmonary disease (COPD), making a number of high-scoring, novel miR-TSV predictions. We also demonstrate SubmiRine's ability to predict and prioritize known miR-TSVs that have undergone experimental validation in previous studies.

Description: Lying below Vatnajökull ice cap in Iceland, Bárðarbunga stratovolcano began experiencing wholesale caldera collapse in 2014 August 16, one of the largest such events recorded in the modern instrumental era. Simultaneous with this collapse is the initiation of a plate boundary rifting episode north of the caldera. Observations using the international constellation of radar satellites indicate rapid 50 cm d –1 subsidence of the glacier surface overlying the collapsing caldera and metre-scale crustal deformation in the active rift zone. Anomalous earthquakes around the rim of the caldera with highly nondouble-couple focal mechanisms provide a mechanical link to the dynamics of the collapsing magma chamber. A model of the collapse consistent with available geodetic and seismic observations suggests that the majority of the observed subsidence occurs aseismically via a deflating sill-like magma chamber.

Description: Of the ~1.3 million Alu elements in the human genome, only a tiny number are estimated to be active in transcription by RNA polymerase (Pol) III. Tracing the individual loci from which Alu transcripts originate is complicated by their highly repetitive nature. By exploiting RNA-Seq data sets and unique Alu DNA sequences, we devised a bioinformatic pipeline allowing us to identify Pol III-dependent transcripts of individual Alu elements. When applied to ENCODE transcriptomes of seven human cell lines, this search strategy identified ~1300 Alu loci corresponding to detectable transcripts, with ~120 of them expressed in at least three cell lines. In vitro transcription of selected Alu s did not reflect their in vivo expression properties, and required the native 5'-flanking region in addition to internal promoter. We also identified a cluster of expressed Alu Ya5-derived transcription units, juxtaposed to snaR genes on chromosome 19, formed by a promoter-containing left monomer fused to an Alu -unrelated downstream moiety. Autonomous Pol III transcription was also revealed for Alu s nested within Pol II-transcribed genes. The ability to investigate Alu transcriptomes at single-locus resolution will facilitate both the identification of novel biologically relevant Alu RNAs and the assessment of Alu expression alteration under pathological conditions.

Description: Deformation experiments on partially molten rocks in simple shear form melt bands at 20° to the shear plane instead of at the expected 45° principal compressive stress direction. These melt bands may play an important role in melt focusing in mid-ocean ridges. Such shallow bands are known to form for two-phase media under shear if strongly non-Newtonian power-law creep is employed for the solid phase, or anisotropy imposed. However laboratory experiments show that shallow bands occur regardless of creep mechanism, even in diffusion creep, which is nominally Newtonian. Here we propose that a couple of forms of two-phase damage allow for shallow melt bands even in diffusion creep.

Description: Progressive deformation of upper mantle rocks via dislocation creep causes their constituent crystals to take on a non-random orientation distribution (crystallographic preferred orientation or CPO) whose observable signatures include shear-wave splitting and azimuthal dependence of surface wave speeds. Comparison of these signatures with mantle flow models thus allows mantle dynamics to be unraveled on global and regional scales. However, existing self-consistent models of CPO evolution are computationally expensive when used with 3-D and/or time-dependent convection models. Here we propose a new method, called ANPAR, which is based on an analytical parametrization of the crystallographic spin predicted by the second-order (SO) self-consistent theory. Our parametrization runs 2–6  x  10 4 times faster than the SO model and fits its predictions for CPO and crystallographic spin with a variance reduction 〉99 per cent. We illustrate the ANPAR model predictions for the deformation of olivine with three dominant slip systems, (010)[100], (001)[100] and (010)[001], for three uniform deformations (uniaxial compression, pure shear and simple shear) and for a corner-flow model of a spreading mid-ocean ridge.

Description: We investigate the capability of Time Reversal Mirror (TRM) algorithm to image local acoustic sources (〈3.5 km) associated with complex, sustained volcanic eruptions. Accurate source localization for volcano infrasound (low-frequency acoustic waves) is often challenging due to pronounced volcanic topography and emergent arrivals of infrasound signals. While the accuracy of the conventional approaches (e.g. triangulation and semblance method) can be severely compromised by the complex volcanic settings, a TRM-based method may have the potential to properly image acoustic sources by the use of full waveform information and numerical modelling of the time-reversed wavefield. We apply the TRM algorithm to a pyroclastic-laden eruption (sustained for ~60 s) at Santiaguito Volcano, Guatemala, and show that an ordinary TRM operation can undergo significant reduction of its focusing power due to strong topographic propagation effects (e.g. reflection and diffraction). We propose a weighted imaging condition to compensate for complicated transmission loss of the time-reversed wavefield and demonstrate that the presented condition significantly improves the focusing quality of TRM in the presence of complex topography. The consequent TRM source images exhibit remarkable agreement with the visual observation of the eruption implying that the TRM method with a proper imaging condition can be used to localize and track acoustic sources associated with complex volcanic eruptions.

Description: Intrinsically disordered proteins and regions (IDPs and IDRs) lack stable 3D structure under physiological conditions in-vitro , are common in eukaryotes, and facilitate interactions with RNA, DNA and proteins. Current methods for prediction of IDPs and IDRs do not provide insights into their functions, except for a handful of methods that address predictions of protein-binding regions. We report first-of-its-kind computational method DisoRDPbind for high-throughput prediction of RNA, DNA and protein binding residues located in IDRs from protein sequences. DisoRDPbind is implemented using a runtime-efficient multi-layered design that utilizes information extracted from physiochemical properties of amino acids, sequence complexity, putative secondary structure and disorder and sequence alignment. Empirical tests demonstrate that it provides accurate predictions that are competitive with other predictors of disorder-mediated protein binding regions and complementary to the methods that predict RNA- and DNA-binding residues annotated based on crystal structures. Application in Homo sapiens, Mus musculus, Caenorhabditis elegans and Drosophila melanogaster proteomes reveals that RNA- and DNA-binding proteins predicted by DisoRDPbind complement and overlap with the corresponding known binding proteins collected from several sources. Also, the number of the putative protein-binding regions predicted with DisoRDPbind correlates with the promiscuity of proteins in the corresponding protein–protein interaction networks. Webserver: http://biomine.ece.ualberta.ca/DisoRDPbind/

Description: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

Description: We present a strategy to thoroughly investigate the effects of prominent topography on the surface tilt due to a spherical pressure source. We use Etna's topography as a case of study and, for different source positions, we compare the tilt fields calculated through (i) a 3-D boundary element method and (ii) analytical half-space solutions. We systematically determine (i) the source positions leading to the strongest tilt misfits when numerical and analytical results are compared and (ii) the surface areas where the strongest distortions in the tilt field are most likely to be observed. We also demonstrate that, under critical circumstances, in terms of respective positions of pressure source and observation points, results of inversion procedures aimed at retrieving the source parameters can be misleading, if tilt data are analysed using models that do not account for topography.

Description: The agreement between shear wave velocities for the Earth's inner core observed from seismology with those derived from mineral physics is considerably worse than for any other region of the Earth. Furthermore, there is still debate as to the phase of iron present in the inner core, particularly when alloying with nickel and light elements is taken into account. To investigate the extent to which the mismatch between seismology and mineral physics is a function of either crystal structure and/or the amount of nickel present, we have used ab initio molecular dynamics simulations to calculate the elastic constants and seismic velocities ( V p and V s ) of face centred cubic (fcc) iron at Earth's inner core pressures (360 GPa) and at temperatures up to ~7000 K. We find that V p for fcc iron (fcc-Fe) is very similar to that for hexagonal close packed (hcp) iron at all temperatures. In contrast, V s for fcc-Fe is significantly higher than in hcp-Fe, with the difference increasing with increasing temperature; the difference between V s for the core (from seismology) and V s for fcc-Fe exceeds 40 per cent. These results are consistent with previous work at lower temperatures. We have also investigated the effect of 6.5 and 13 atm% Ni in fcc-Fe. We find that Ni only slightly reduces V p and V s (e.g. by 2 per cent in V s for 13 atm% Ni at 5500 K), and cannot account for the difference between the velocities observed in the core and those of pure fcc-Fe. We also tried to examine pre-melting behaviour in fcc-Fe, as reported in hcp-Fe by extending the study to very high temperatures (at which superheating may occur). However, we find that fcc-Fe spontaneously transforms to other hcp-like structures before melting; two hcp-like structures were found, both of hexagonal symmetry, which may most easily be regarded as being derived from an hcp crystal with stacking faults. That the structure did not transform to a true hcp phase is likely as a consequence of the limited size of the simulation box (108 atoms). At 360 GPa, in pure fcc-Fe, we find that the transition from fcc to the hcp-like structure occurs at 7000 K, whereas in the Ni bearing system, the transition occurs at higher temperature (7250 K). This reinforces previous work showing that fcc-Fe might transform to hcp-Fe just before melting, and that Ni tends to stabilize the fcc structure with respect to hcp.

Description: Viscoelastic behaviour of materials in nature is observed in post-event deformations due to seismic or volcanic activities. In this paper, by adopting the correspondence principle, we propose an inelastic model to predict first the Laplace-domain response of a transversely isotropic viscoelastic half-space due to a shear or tensile fault of polygonal shape. The displacement and stress fields in the time domain are then obtained using an efficient and accurate algorithm for the inverse Laplace transform. Numerical examples are presented to validate the proposed solution and to show the viscoelastic displacement and stress fields due to a strike-slip, dip-slip and tensile fault of rectangular shape. The obtained results indicate that both viscoelasticity and transverse isotropy play significant roles in the viscoelastic response of the half-space due to faults, which could be used as benchmarks for the future numerical analysis of realistic post-seismic or volcanic event.

Description: One of the unresolved questions concerning fault deformation is the degree and cause of localization of shear at depth beneath a fault. Geologic observations of exhumed shear zones indicate that while the motion is no longer planar, it can still be localized near the down-dip extension of the fault; however, the degree of localization is uncertain. We employ simple analytic and numerical models to investigate the structural form of distributed shear beneath a strike-slip fault, and the relative importance of the physical mechanisms that have the potential to localize a shear zone. For a purely depth dependent viscosity, = 0 exp (– z / z 0 ), we find that a shear zone develops with a half-width $\delta _w\sim \sqrt{z_0}$ for small z 0 at the base of the layer, where lengths are non-dimensionalized by the layer thickness ( d km). Including a non-linear stress–strain-rate relation ( $\dot{\epsilon }\propto \sigma ^n$ ) scales w by $1/\sqrt{n}$ , comparable to deformation length scales in thin viscous sheet calculations. We find that the primary control on the shear-zone width is the depth dependence of viscosity that arises from the temperature dependence of viscosity and the increase in temperature with depth. As this relationship is exponential, scaling relations give a dimensional half-width that scales approximately as $$ \tilde{\delta }_w\approx T_{\frac{1}{2}}\sqrt{\frac{Rd}{nQ\beta }}\text{ km}, $$ where $T_{\frac{1}{2}}$ (K) is the temperature at the midpoint of the layer, R (J mol –1 K –1 ) the gas constant, Q (J mol –1 ) the activation energy and β (K km –1 ) the geothermal gradient. This relation predicts the numerical results for the parameter range consistent with continental rheologies to within 2–5 per cent and shear-zone half-widths from 2 to 6 km. The inclusion of shear-stress heating reduces w by only an additional 5–25 per cent, depending on the initial width of the shear zone. While the width of the shear zone may not decrease significantly, local temperature increases from shear-stress heating range from 50 to 300 °C resulting in a reduction in viscosities beneath the fault of several orders of magnitude and a concomitant reduction in the stresses needed to drive the motion.

Description: Protein sequences predicted from metagenomic datasets are annotated by identifying their homologs via sequence comparisons with reference or curated proteins. However, a majority of metagenomic protein sequences are partial-length, arising as a result of identifying genes on sequencing reads or on assembled nucleotide contigs, which themselves are often very fragmented. The fragmented nature of metagenomic protein predictions adversely impacts homology detection and, therefore, the quality of the overall annotation of the dataset. Here we present a novel algorithm called GRASP that accurately identifies the homologs of a given reference protein sequence from a database consisting of partial-length metagenomic proteins. Our homology detection strategy is guided by the reference sequence, and involves the simultaneous search and assembly of overlapping database sequences. GRASP was compared to three commonly used protein sequence search programs (BLASTP, PSI-BLAST and FASTM). Our evaluations using several simulated and real datasets show that GRASP has a significantly higher sensitivity than these programs while maintaining a very high specificity. GRASP can be a very useful program for detecting and quantifying taxonomic and protein family abundances in metagenomic datasets. GRASP is implemented in GNU C++, and is freely available at http://sourceforge.net/projects/grasp-release .

Description: Genetic screens of an unprecedented scale have recently been made possible by the availability of high-complexity libraries of synthetic oligonucleotides designed to mediate either gene knockdown or gene knockout, coupled with next-generation sequencing. However, several sources of random noise and statistical biases complicate the interpretation of the resulting high-throughput data. We developed HiTSelect, a comprehensive analysis pipeline for rigorously selecting screen hits and identifying functionally relevant genes and pathways by addressing off-target effects, controlling for variance in both gene silencing efficiency and sequencing depth of coverage and integrating relevant metadata. We document the superior performance of HiTSelect using data from both genome-wide RNAi and CRISPR/Cas9 screens. HiTSelect is implemented as an open-source package, with a user-friendly interface for data visualization and pathway exploration. Binary executables are available at http://sourceforge.net/projects/hitselect/ , and the source code is available at https://github.com/diazlab/HiTSelect .

Description: Integrative analyses of epigenetic data promise a deeper understanding of the epigenome. Epidaurus is a bioinformatics tool used to effectively reveal inter-dataset relevance and differences through data aggregation, integration and visualization. In this study, we demonstrated the utility of Epidaurus in validating hypotheses and generating novel biological insights. In particular, we described the use of Epidaurus to (i) integrate epigenetic data from prostate cancer cell lines to validate the activation function of EZH2 in castration-resistant prostate cancer and to (ii) study the mechanism of androgen receptor ( AR ) binding deregulation induced by the knockdown of FOXA1 . We found that EZH2 's noncanonical activation function was reaffirmed by its association with active histone markers and the lack of association with repressive markers. More importantly, we revealed that the binding of AR was selectively reprogramed to promoter regions, leading to the up-regulation of hundreds of cancer-associated genes including EGFR . The prebuilt epigenetic dataset from commonly used cell lines (LNCaP, VCaP, LNCaP-Abl, MCF7, GM12878, K562, HeLa-S3, A549, HePG2) makes Epidaurus a useful online resource for epigenetic research. As standalone software, Epidaurus is specifically designed to process user customized datasets with both efficiency and convenience.

Description: RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ~94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ~83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred .

Description: Identifying conserved and divergent response patterns in gene networks is becoming increasingly important. A common approach is integrating expression information with gene association networks in order to find groups of connected genes that are activated or repressed. In many cases, researchers are also interested in comparisons across species (or conditions). Finding an active sub-network is a hard problem and applying it across species requires further considerations (e.g. orthology information, expression data and networks from different sources). To address these challenges we devised ModuleBlast, which uses both expression and network topology to search for highly relevant sub-networks. We have applied ModuleBlast to expression and interaction data from mouse, macaque and human to study immune response and aging. The immune response analysis identified several relevant modules, consistent with recent findings on apoptosis and NFB activation following infection. Temporal analysis of these data revealed cascades of modules that are dynamically activated within and across species. We have experimentally validated some of the novel hypotheses resulting from the analysis of the ModuleBlast results leading to new insights into the mechanisms used by a key mammalian aging protein.

Description: Here we used discriminative training methods to uncover the chromatin, transcription factor (TF) binding and sequence features of enhancers underlying gene expression in individual cardiac cells. We used machine learning with TF motifs and ChIP data for a core set of cardiogenic TFs and histone modifications to classify Drosophila cell-type-specific cardiac enhancer activity. We show that the classifier models can be used to predict cardiac cell subtype cis -regulatory activities. Associating the predicted enhancers with an expression atlas of cardiac genes further uncovered clusters of genes with transcription and function limited to individual cardiac cell subtypes. Further, the cell-specific enhancer models revealed chromatin, TF binding and sequence features that distinguish enhancer activities in distinct subsets of heart cells. Collectively, our results show that computational modeling combined with empirical testing provides a powerful platform to uncover the enhancers, TF motifs and gene expression profiles which characterize individual cardiac cell fates.

Description: The equation that relates pressure, temperature and volume and is described by parameters that are function of temperature at 1 bar (hereafter called thermal equation of state, TEOS), has practical computational advantages for petrological and geophysical applications over the equation that considers explicitly a thermal pressure. Some considerations that justify the use of the TEOS are discussed here. (1) The assumption that the parameters are function of temperature is perhaps better understood by looking at the Helmholtz energy function that is implicitly assumed in the case of an equation of state (EOS) derived from interatomic potentials. A test case shows that the Helmholtz energy related to the Vinet EOS and the Helmholtz energy from the Debye model are very similar. (2) The TEOS should be able to reproduce thermal expansion (α), isothermal bulk modulus ( K T ) and heat capacity ( C p and C v ) at high P , T computed from a lattice vibration model. The generalized Rydberg EOS applied to MgO is able to fit reasonably well the properties computed using Jacobs’ lattice dynamics formulation ( T range = 300–3000 K, P range = 1 bar–1500 kbar). (3) It is shown that in the case of MgO, the TEOS can be used quite successfully for extrapolation that goes beyond the P , T range of the measured/given data. Some physical constraints need to be applied to the derivation of the volume, bulk modulus and derivative of the bulk modulus with pressure at 1 bar. (4) The pressure dependence of the reference parameters in the TEOS that was inferred several decades ago is only apparent. A numerical computation demonstrates that the combined pressure effect in the terms defining the partial derivative of the reference V and K (and K') over temperature cancels out, making the reference parameters independent of pressure at any condition.

Description: The equation that relates pressure, temperature and volume and is described by parameters that are function of temperature at 1 bar (hereafter called thermal equation of state, TEOS), has practical computational advantages for petrological and geophysical applications over the equation that considers explicitly a thermal pressure. Some considerations that justify the use of the TEOS are discussed here. (1) The assumption that the parameters are function of temperature is perhaps better understood by looking at the Helmholtz energy function that is implicitly assumed in the case of an equation of state (EOS) derived from interatomic potentials. A test case shows that the Helmholtz energy related to the Vinet EOS and the Helmholtz energy from the Debye model are very similar. (2) The TEOS should be able to reproduce thermal expansion (α), isothermal bulk modulus ( K T ) and heat capacity ( C p and C v ) at high P , T computed from a lattice vibration model. The generalized Rydberg EOS applied to MgO is able to fit reasonably well the properties computed using Jacobs’ lattice dynamics formulation ( T range = 300–3000 K, P range = 1 bar–1500 kbar). (3) It is shown that in the case of MgO, the TEOS can be used quite successfully for extrapolation that goes beyond the P , T range of the measured/given data. Some physical constraints need to be applied to the derivation of the volume, bulk modulus and derivative of the bulk modulus with pressure at 1 bar. (4) The pressure dependence of the reference parameters in the TEOS that was inferred several decades ago is only apparent. A numerical computation demonstrates that the combined pressure effect in the terms defining the partial derivative of the reference V and K (and K') over temperature cancels out, making the reference parameters independent of pressure at any condition.

Description: A key aspect of RNA secondary structure prediction is the identification of novel functional elements. This is a challenging task because these elements typically are embedded in longer transcripts where the borders between the element and flanking regions have to be defined. The flanking sequences impact the folding of the functional elements both at the level of computational analyses and when the element is extracted as a transcript for experimental analysis. Here, we analyze how different flanking region lengths impact folding into a constrained structure by computing probabilities of folding for different sizes of flanking regions. Our method, RNAcop (RNA context optimization by probability), is tested on known and de novo predicted structures. In vitro experiments support the computational analysis and suggest that for a number of structures, choosing proper lengths of flanking regions is critical. RNAcop is available as web server and stand-alone software via http://rth.dk/resources/rnacop .

Description: A knowledge of subsurface temperatures in sedimentary basins, fault zones, volcanic environments and polar ice sheets is of interest for a wide variety of geophysical applications. However, the process of drilling deep boreholes in these environments to provide access for temperature and other measurements invariably disturbs the temperature field around a newly created borehole. Although this disturbance dissipates over time, most temperature measurements are made while the temperature field is still disturbed. Thus, the measurements must be ‘corrected’ for the drilling-disturbance effect if the undisturbed temperature field is to be determined. This paper provides compact analytical solutions for the thermal drilling disturbance based on 1-D (radial) and 2-D (radial and depth) Green's functions (GFs) in cylindrical coordinates. Solutions are developed for three types of boundary conditions (BCs) at the borehole wall: (1) prescribed temperature, (2) prescribed heat flux and (3) a prescribed convective condition. The BC at the borehole wall is allowed to vary both with depth and time. Inclusion of the depth dimension in the 2-D solution allows vertical heat-transfer effects to be quantified in situations where they are potentially important, that is, near the earth's surface, at the bottom of a well and when considering finite-drilling rates. The 2-D solution also includes a radial- and time-dependent BC at the earth's surface to assess the impact of drilling-related infrastructure (drilling pads, mud pits, permanent shelters) on the subsurface temperature field. Latent-heat effects due to the melting and subsequent refreezing of interstitial ice while drilling a borehole through ice-rich permafrost can be included in the GF solution as a moving-plane heat source (or sink) located at the solid–liquid interface. Synthetic examples are provided illustrating the 1-D and 2-D GF solutions. The flexibility of the approach allows the investigation of thermal drilling effects in rock or ice for a wide variety of drilling technologies. Numerical values for the required radial GFs G R are available through the Advanced Cooperative Arctic Data and Information Service at doi:10.5065/D64F1NS6.

Description: Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx .

Description: Despite the biological importance of non-coding RNA, their structural characterization remains challenging. Making use of the rapidly growing sequence databases, we analyze nucleotide coevolution across homologous sequences via Direct-Coupling Analysis to detect nucleotide-nucleotide contacts. For a representative set of riboswitches, we show that the results of Direct-Coupling Analysis in combination with a generalized Nussinov algorithm systematically improve the results of RNA secondary structure prediction beyond traditional covariance approaches based on mutual information. Even more importantly, we show that the results of Direct-Coupling Analysis are enriched in tertiary structure contacts. By integrating these predictions into molecular modeling tools, systematically improved tertiary structure predictions can be obtained, as compared to using secondary structure information alone.

Description: Sequence variations in regulatory DNA regions are known to cause functionally important consequences for gene expression. DNA sequence variations may have an essential role in determining phenotypes and may be linked to disease; however, their identification through analysis of massive genome-wide sequencing data is a great challenge. In this work, a new computational pipeline, a Bayesian method for protein–DNA interaction with binding affinity ranking (BayesPI-BAR), is proposed for quantifying the effect of sequence variations on protein binding. BayesPI-BAR uses biophysical modeling of protein–DNA interactions to predict single nucleotide polymorphisms (SNPs) that cause significant changes in the binding affinity of a regulatory region for transcription factors (TFs). The method includes two new parameters (TF chemical potentials or protein concentrations and direct TF binding targets) that are neglected by previous methods. The new method is verified on 67 known human regulatory SNPs, of which 47 (70%) have predicted true TFs ranked in the top 10. Importantly, the performance of BayesPI-BAR, which uses principal component analysis to integrate multiple predictions from various TF chemical potentials, is found to be better than that of existing programs, such as sTRAP and is-rSNP, when evaluated on the same SNPs. BayesPI-BAR is a publicly available tool and is able to carry out parallelized computation, which helps to investigate a large number of TFs or SNPs and to detect disease-associated regulatory sequence variations in the sea of genome-wide noncoding regions.

Description: The rapid discovery of potential driver mutations through large-scale mutational analyses of human cancers generates a need to characterize their cellular phenotypes. Among the techniques for genome editing, recombinant adeno-associated virus (rAAV)-mediated gene targeting is suited for knock-in of single nucleotide substitutions and to a lesser degree for gene knock-outs. However, the generation of gene targeting constructs and the targeting process is time-consuming and labor-intense. To facilitate rAAV-mediated gene targeting, we developed the first software and complementary automation-friendly vector tools to generate optimized targeting constructs for editing human protein encoding genes. By computational approaches, rAAV constructs for editing ~71% of bases in protein-coding exons were designed. Similarly, ~81% of genes were predicted to be targetable by rAAV-mediated knock-out. A Gateway-based cloning system for facile generation of rAAV constructs suitable for robotic automation was developed and used in successful generation of targeting constructs. Together, these tools enable automated rAAV targeting construct design, generation as well as enrichment and expansion of targeted cells with desired integrations.

Description: Degenerate codon (DC) libraries efficiently address the experimental library-size limitations of directed evolution by focusing diversity toward the positions and toward the amino acids (AAs) that are most likely to generate hits; however, manually constructing DC libraries is challenging, error prone and time consuming. This paper provides a dynamic programming solution to the task of finding the best DCs while keeping the size of the library beneath some given limit, improving on the existing integer-linear programming formulation. It then extends the algorithm to consider multiple DCs at each position, a heretofore unsolved problem, while adhering to a constraint on the number of primers needed to synthesize the library. In the two library-design problems examined here, the use of multiple DCs produces libraries that very nearly cover the set of desired AAs while still staying within the experimental size limits. Surprisingly, the algorithm is able to find near-perfect libraries where the ratio of amino-acid sequences to nucleic-acid sequences approaches 1; it effectively side-steps the degeneracy of the genetic code. Our algorithm is freely available through our web server and solves most design problems in about a second.

Description: Tarim Craton is a Precambrian block situated in northwest China, just north of the Tibetan Plateau, where a large sedimentary basin with abundant hydrocarbon potential has developed. Accurate heat flow data for Tarim is vital for understanding the lithospheric evolution and hydrocarbon generation in this area; however, there were unavailable until now, due to a lack of high quality steady-state temperature logging data. Here, we report 10 new heat flow values derived from steady-state temperature logging and measured thermal conductivity data. New heat flow values range from 40.1 to 49.4 mW m –2 , with a mean of 43.1 ± 3.0 mW m –2 . In addition, radiogenic heat production from the sediments accounts for 20 per cent of the observed surface heat flow, whilst the mantle heat flow is estimated to be as low as 6–15 mW m –2 ; this indicates a dominant contribution from crustal heat, to the observed heat flow. The average heat flow and crustal temperature in the Tarim Craton are markedly lower than those in the Tibetan Plateau, whilst the calculated rheological strength of the lithosphere, beneath Tarim, is sufficiently large to resist the elevation-induced gravitational potential energy difference between Tarim and Tibet. This inherited thermal and rheological contrast, between the craton and Plateau, can be traced back to before the India–Asia collision; this accounts for the differential active deformation pattern in the Tarim Craton and adjacent areas.

Description: Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer's properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo -derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/ .

Description: Microbial natural products are an invaluable source of evolved bioactive small molecules and pharmaceutical agents. Next-generation and metagenomic sequencing indicates untapped genomic potential, yet high rediscovery rates of known metabolites increasingly frustrate conventional natural product screening programs. New methods to connect biosynthetic gene clusters to novel chemical scaffolds are therefore critical to enable the targeted discovery of genetically encoded natural products. Here, we present PRISM, a computational resource for the identification of biosynthetic gene clusters, prediction of genetically encoded nonribosomal peptides and type I and II polyketides, and bio- and cheminformatic dereplication of known natural products. PRISM implements novel algorithms which render it uniquely capable of predicting type II polyketides, deoxygenated sugars, and starter units, making it a comprehensive genome-guided chemical structure prediction engine. A library of 57 tailoring reactions is leveraged for combinatorial scaffold library generation when multiple potential substrates are consistent with biosynthetic logic. We compare the accuracy of PRISM to existing genomic analysis platforms. PRISM is an open-source, user-friendly web application available at http://magarveylab.ca/prism/ .

Description: The cMonkey integrated biclustering algorithm identifies conditionally co-regulated modules of genes (biclusters). cMonkey integrates various orthogonal pieces of information which support evidence of gene co-regulation, and optimizes biclusters to be supported simultaneously by one or more of these prior constraints. The algorithm served as the cornerstone for constructing the first global, predictive Environmental Gene Regulatory Influence Network (EGRIN) model for a free-living cell, and has now been applied to many more organisms. However, due to its computational inefficiencies, long run-time and complexity of various input data types, cMonkey was not readily usable by the wider community. To address these primary concerns, we have significantly updated the cMonkey algorithm and refactored its implementation, improving its usability and extendibility. These improvements provide a fully functioning and user-friendly platform for building co-regulated gene modules and the tools necessary for their exploration and interpretation. We show, via three separate analyses of data for E. coli, M. tuberculosis and H. sapiens , that the updated algorithm and inclusion of novel scoring functions for new data types (e.g. ChIP-seq and transcription factor over-expression [TFOE]) improve discovery of biologically informative co-regulated modules. The complete cMonkey 2 software package, including source code, is available at https://github.com/baliga-lab/cmonkey2 .

Description: In this study, equations are developed that predict for synthetic sedimentary rocks (clastics, carbonates and evapourates) thermal properties comprising thermal conductivity, specific heat capacity and thermal diffusivity. The rock groups are composed of mineral assemblages with variable contents of 15 major rock-forming minerals and porosities of 0–30 per cent. Petrophysical properties and their well-logging-tool-characteristic readings were assigned to these rock-forming minerals and to pore-filling fluids. Relationships are explored between each thermal property and other petrophysical properties (density, sonic interval transit time, hydrogen index, volume fraction of shale and photoelectric absorption index) using multivariate statistics. The application of these relations allows computing continuous borehole profiles for each rock thermal property. The uncertainties in the prediction of each property vary depending on the selected well-log combination. Best prediction is in the range of 2–8 per cent for the specific heat capacity, of 5–10 per cent for the thermal conductivity, and of 8–15 for the thermal diffusivity, respectively. Well-log derived thermal conductivity is validated by laboratory data measured on cores from deep boreholes of the Danish Basin, the North German Basin, and the Molasse Basin. Additional validation of thermal conductivity was performed by comparing predicted and measured temperature logs. The maximum deviation between these logs is 〈3 °C. The thermal-conductivity calculation allowed an evaluation of the depth range in which the palaeoclimatic effect on the subsurface temperature field can be observed in the North German Basin. This effect reduces the surface heat-flow density by 25 mW m –2 .

Description: It is important to know the electrokinetic properties of crustal rocks for interpreting the conductivity mechanisms and seismoelectric phenomena during earthquakes and seismoelectric well logging. In this study, electrokinetic experiments are conducted using a special core-holder by employing an AC lock-in technique. A series of experiments are conducted on 10 sandstone samples to measure the streaming potentials and streaming currents, and the experiments on each sample are done at six different salinities. The streaming potential coefficient and streaming current coefficient are calculated from the measured streaming potentials and streaming currents. The experimental results show that streaming potential coefficient and streaming current coefficient decrease as the salinity increases. The dependence of these two coefficients on permeability and pore radius are analysed and compared with previous works. At low salinities, the streaming potential coefficient and streaming current coefficient increase with the increasing permeability and pore radius. At high salinities, the streaming potential coefficient (streaming current coefficient) almost share a same value for 10 different samples. This conclusion indicates that the differences of rock parameters can only be well recognized at lower salinities, and the electrokinetic signals are invalid at high salinities, which offers a restrictive condition for using the amplitude of electrokinetic signals to estimate rock parameters. The zeta-potential have also been estimated through combined measurements of streaming potential and streaming current. The surface conductivity and its contribution to electrokinetic effects are determined from a comparison of zeta-potentials by two different methods, and then the validation of the Helmholz–Smoluchowski equation for a capillary tube is tested in rocks. We also compare our date with theoretical and experimental works, and set up an expression about the relationship between zeta potential and salinity, which fits the experimental data well.

Description: We used ab initio molecular dynamics to calculate the elastic constants of MgSiO 3 , FeSiO 3 and (Fe 0.5 Mg 0.5 )SiO 3 perovskite under lower-most mantle conditions (136 GPa, 2000–4000 K). At 0 K, V p and V s for FeSiO 3 perovskite are 12 and 18 per cent lower, respectively than those for MgSiO 3 , in agreement with previous work. The difference in velocity, however, increases with temperature for high spin (but not low spin) states and at 4000 K, V p and V s for the end-member FeSiO 3 are, respectively, 19 and 34 per cent lower than those of MgSiO 3 . We find that (dln V s /dln V p ) T,P of Fe x Mg 1- x O 3 remains below 2 for all relevant iron concentrations and lower than the value of 3 seen in most ultralow velocity zones (ULVZ). Moreover, we are unable to match simultaneously the observed density increase and shear wave velocity reduction shown by ULVZs by increasing the ferrous iron content in perovskite except for the largest density increases and the smallest shear wave decreases observed in ULVZs. Assuming that current seismic estimates of ULVZ properties are accurate, our results suggest that ferrous iron enrichment alone cannot explain ULVZs.