ALBERT

Description: Recent releases of genome three-dimensional (3D) structures have the potential to transform our understanding of genomes. Nonetheless, the storage technology and visualization tools need to evolve to offer to the scientific community fast and convenient access to these data. We introduce simultaneously a database system to store and query 3D genomic data ( 3DBG ), and a 3D genome browser to visualize and explore 3D genome structures ( 3DGB ). We benchmark 3DBG against state-of-the-art systems and demonstrate that it is faster than previous solutions, and importantly gracefully scales with the size of data. We also illustrate the usefulness of our 3D genome Web browser to explore human genome structures. The 3D genome browser is available at http://3dgb.cs.mcgill.ca/ .

Description: Identification of transcription units (TUs) encoded in a bacterial genome is essential to elucidation of transcriptional regulation of the organism. To gain a detailed understanding of the dynamically composed TU structures, we have used four strand-specific RNA-seq (ssRNA-seq) datasets collected under two experimental conditions to derive the genomic TU organization of Clostridium thermocellum using a machine-learning approach. Our method accurately predicted the genomic boundaries of individual TUs based on two sets of parameters measuring the RNA-seq expression patterns across the genome: expression-level continuity and variance. A total of 2590 distinct TUs are predicted based on the four RNA-seq datasets. Among the predicted TUs, 44% have multiple genes. We assessed our prediction method on an independent set of RNA-seq data with longer reads. The evaluation confirmed the high quality of the predicted TUs. Functional enrichment analyses on a selected subset of the predicted TUs revealed interesting biology. To demonstrate the generality of the prediction method, we have also applied the method to RNA-seq data collected on Escherichia coli and achieved high prediction accuracies. The TU prediction program named SeqTU is publicly available at https://code.google.com/p/seqtu/ . We expect that the predicted TUs can serve as the baseline information for studying transcriptional and post-transcriptional regulation in C. thermocellum and other bacteria.

Description: Detecting genetic variation is one of the main applications of high-throughput sequencing, but is still challenging wherever aligning short reads poses ambiguities. Current state-of-the-art variant calling approaches avoid such regions, arguing that it is necessary to sacrifice detection sensitivity to limit false discovery. We developed a method that links candidate variant positions within repetitive genomic regions into clusters. The technique relies on a resource, a thesaurus of genetic variation, that enumerates genomic regions with similar sequence. The resource is computationally intensive to generate, but once compiled can be applied efficiently to annotate and prioritize variants in repetitive regions. We show that thesaurus annotation can reduce the rate of false variant calls due to mappability by up to three orders of magnitude. We apply the technique to whole genome datasets and establish that called variants in low mappability regions annotated using the thesaurus can be experimentally validated. We then extend the analysis to a large panel of exomes to show that the annotation technique opens possibilities to study variation in hereto hidden and under-studied parts of the genome.

Description: Inversion polymorphisms have important phenotypic and evolutionary consequences in humans. Two different methodologies have been used to infer inversions from SNP dense data, enabling the use of large cohorts for their study. One approach relies on the differences in linkage disequilibrium across breakpoints; the other one captures the internal haplotype groups that tag the inversion status of chromosomes. In this article, we assessed the convergence of the two methods in the detection of 20 human inversions that have been reported in the literature. The methods converged in four inversions including inv-8p23, for which we studied its association with low-BMI in American children. Using a novel haplotype tagging method with control on inversion ancestry, we computed the frequency of inv-8p23 in two American cohorts and observed inversion haplotype admixture. Accounting for haplotype ancestry, we found that the European inverted allele in children carries a recessive risk of underweight, validated in an independent Spanish cohort (combined: OR= 2.00, P = 0.001). While the footprints of inversions on SNP data are complex, we show that systematic analyses, such as convergence of different methods and controlling for ancestry, can reveal the contribution of inversions to the ancestral composition of populations and to the heritability of human disease.

Description: The Metabolic Models Reconstruction Using Genome-Scale Information ( merlin ) tool is a user-friendly Java application that aids the reconstruction of genome-scale metabolic models for any organism that has its genome sequenced. It performs the major steps of the reconstruction process, including the functional genomic annotation of the whole genome and subsequent construction of the portfolio of reactions. Moreover, merlin includes tools for the identification and annotation of genes encoding transport proteins, generating the transport reactions for those carriers. It also performs the compartmentalisation of the model, predicting the organelle localisation of the proteins encoded in the genome and thus the localisation of the metabolites involved in the reactions promoted by such enzymes. The gene-proteins-reactions (GPR) associations are automatically generated and included in the model. Finally, merlin expedites the transition from genomic data to draft metabolic models reconstructions exported in the SBML standard format, allowing the user to have a preliminary view of the biochemical network, which can be manually curated within the environment provided by merlin .

Description: For eukaryotic cells, the biological processes involving regulatory DNA elements play an important role in cell cycle. Understanding 3D spatial arrangements of chromosomes and revealing long-range chromatin interactions are critical to decipher these biological processes. In recent years, chromosome conformation capture (3C) related techniques have been developed to measure the interaction frequencies between long-range genome loci, which have provided a great opportunity to decode the 3D organization of the genome. In this paper, we develop a new Bayesian framework to derive the 3D architecture of a chromosome from 3C-based data. By modeling each chromosome as a polymer chain, we define the conformational energy based on our current knowledge on polymer physics and use it as prior information in the Bayesian framework. We also propose an expectation-maximization (EM) based algorithm to estimate the unknown parameters of the Bayesian model and infer an ensemble of chromatin structures based on interaction frequency data. We have validated our Bayesian inference approach through cross-validation and verified the computed chromatin conformations using the geometric constraints derived from fluorescence in situ hybridization (FISH) experiments. We have further confirmed the inferred chromatin structures using the known genetic interactions derived from other studies in the literature. Our test results have indicated that our Bayesian framework can compute an accurate ensemble of 3D chromatin conformations that best interpret the distance constraints derived from 3C-based data and also agree with other sources of geometric constraints derived from experimental evidence in the previous studies. The source code of our approach can be found in https://github.com/wangsy11/InfMod3DGen .

Description: Characterization of cell type specific regulatory networks and elements is a major challenge in genomics, and emerging strategies frequently employ high-throughput genome-wide assays of transcription factor (TF) to DNA binding, histone modifications or chromatin state. However, these experiments remain too difficult/expensive for many laboratories to apply comprehensively to their system of interest. Here, we explore the potential of elucidating regulatory systems in varied cell types using computational techniques that rely on only data of gene expression, low-resolution chromatin accessibility, and TF–DNA binding specificities (‘motifs’). We show that static computational motif scans overlaid with chromatin accessibility data reasonably approximate experimentally measured TF–DNA binding. We demonstrate that predicted binding profiles and expression patterns of hundreds of TFs are sufficient to identify major regulators of ~200 spatiotemporal expression domains in the Drosophila embryo. We are then able to learn reliable statistical models of enhancer activity for over 70 expression domains and apply those models to annotate domain specific enhancers genome-wide. Throughout this work, we apply our motif and accessibility based approach to comprehensively characterize the regulatory network of fruitfly embryonic development and show that the accuracy of our computational method compares favorably to approaches that rely on data from many experimental assays.

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: 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: Much of the inter-individual variation in gene expression is triggered via perturbations of signaling networks by DNA variants. We present a novel probabilistic approach for identifying the particular pathways by which DNA variants perturb the signaling network. Our procedure, called PINE, relies on a systematic integration of established biological knowledge of signaling networks with data on transcriptional responses to various experimental conditions. Unlike previous approaches, PINE provides statistical aspects that are critical for prioritizing hypotheses for followup experiments. Using simulated data, we show that higher accuracy is attained with PINE than with existing methods. We used PINE to analyze transcriptional responses of immune dendritic cells to several pathogenic stimulations. PINE identified statistically significant genetic perturbations in the pathogen-sensing signaling network, suggesting previously uncharacterized regulatory mechanisms for functional DNA variants.

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: Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to detect biologically meaningful changes. Similarly, retaining these samples in the analysis may not reveal any statistically significant changes due to the higher noise level. A compromise is to use all available data, but to down-weight the observations from more variable samples. We describe a statistical approach that facilitates this by modelling heterogeneity at both the sample and observational levels as part of the differential expression analysis. At the sample level this is achieved by fitting a log-linear variance model that includes common sample-specific or group-specific parameters that are shared between genes. The estimated sample variance factors are then converted to weights and combined with observational level weights obtained from the mean–variance relationship of the log-counts-per-million using ‘voom’. A comprehensive analysis involving both simulations and experimental RNA-sequencing data demonstrates that this strategy leads to a universally more powerful analysis and fewer false discoveries when compared to conventional approaches. This methodology has wide application and is implemented in the open-source ‘limma’ package.

Description: Most mammalian genes have mRNA variants due to alternative promoter usage, alternative splicing, and alternative cleavage and polyadenylation. Expression of alternative RNA isoforms has been found to be associated with tumorigenesis, proliferation and differentiation. Detection of condition-associated transcription variation requires association methods. Traditional association methods such as Pearson chi-square test and Fisher Exact test are single test methods and do not work on count data with replicates. Although the Cochran Mantel Haenszel (CMH) approach can handle replicated count data, our simulations showed that multiple CMH tests still had very low power. To identify condition-associated variation of transcription, we here proposed a ranking analysis of chi-squares (RAX2) for large-scale association analysis. RAX2 is a nonparametric method and has accurate and conservative estimation of FDR profile. Simulations demonstrated that RAX2 performs well in finding condition-associated transcription variants. We applied RAX2 to primary T-cell transcriptomic data and identified 1610 (16.3%) tags associated in transcription with immune stimulation at FDR 〈 0.05. Most of these tags also had differential expression. Analysis of two and three tags within genes revealed that under immune stimulation short RNA isoforms were preferably used.

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: To understand how transposon landscapes (TLs) vary across animal genomes, we describe a new method called the Transposon Insertion and Depletion AnaLyzer (TIDAL) and a database of 〉300 TLs in Drosophila melanogaster (TIDAL-Fly). Our analysis reveals pervasive TL diversity across cell lines and fly strains, even for identically named sub-strains from different laboratories such as the ISO1 strain used for the reference genome sequence. On average, 〉500 novel insertions exist in every lab strain, inbred strains of the Drosophila Genetic Reference Panel (DGRP), and fly isolates in the Drosophila Genome Nexus (DGN). A minority (〈25%) of transposon families comprise the majority (〉70%) of TL diversity across fly strains. A sharp contrast between insertion and depletion patterns indicates that many transposons are unique to the ISO1 reference genome sequence. Although TL diversity from fly strains reaches asymptotic limits with increasing sequencing depth, rampant TL diversity causes unsaturated detection of TLs in pools of flies. Finally, we show novel transposon insertions negatively correlate with Piwi-interacting RNA (piRNA) levels for most transposon families, except for the highly-abundant roo retrotransposon. Our study provides a useful resource for Drosophila geneticists to understand how transposons create extensive genomic diversity in fly cell lines and strains.

Description: Next-generation sequencing (NGS) approaches rapidly produce millions to billions of short reads, which allow pathogen detection and discovery in human clinical, animal and environmental samples. A major limitation of sequence homology-based identification for highly divergent microorganisms is the short length of reads generated by most highly parallel sequencing technologies. Short reads require a high level of sequence similarities to annotated genes to confidently predict gene function or homology. Such recognition of highly divergent homologues can be improved by reference-free ( de novo ) assembly of short overlapping sequence reads into larger contigs. We describe an ensemble strategy that integrates the sequential use of various de Bruijn graph and overlap-layout-consensus assemblers with a novel partitioned sub-assembly approach. We also proposed new quality metrics that are suitable for evaluating metagenome de novo assembly. We demonstrate that this new ensemble strategy tested using in silico spike-in, clinical and environmental NGS datasets achieved significantly better contigs than current approaches.

Description: Distinguishing between promoter-like sequences in bacteria that belong to true or abortive promoters, or to those that do not initiate transcription at all, is one of the important challenges in transcriptomics. To address this problem, we have studied the genome-reduced bacterium Mycoplasma pneumoniae , for which the RNAs associated with transcriptional start sites have been recently experimentally identified. We determined the contribution to transcription events of different genomic features: the –10, extended –10 and –35 boxes, the UP element, the bases surrounding the –10 box and the nearest-neighbor free energy of the promoter region. Using a random forest classifier and the aforementioned features transformed into scores, we could distinguish between true, abortive promoters and non-promoters with good –10 box sequences. The methods used in this characterization of promoters can be extended to other bacteria and have important applications for promoter design in bacterial genome engineering.

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: MicroRNAs (miRNAs) are involved in the regulation of gene expression at a post-transcriptional level. As such, monitoring miRNA expression has been increasingly used to assess their role in regulatory mechanisms of biological processes. In large scale studies, once miRNAs of interest have been identified, the target genes they regulate are often inferred using algorithms or databases. A pathway analysis is then often performed in order to generate hypotheses about the relevant biological functions controlled by the miRNA signature. Here we show that the method widely used in scientific literature to identify these pathways is biased and leads to inaccurate results. In addition to describing the bias and its origin we present an alternative strategy to identify potential biological functions specifically impacted by a miRNA signature. More generally, our study exemplifies the crucial need of relevant negative controls when developing, and using, bioinformatics methods.

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: 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: In cancer research, background models for mutation rates have been extensively calibrated in coding regions, leading to the identification of many driver genes, recurrently mutated more than expected. Noncoding regions are also associated with disease; however, background models for them have not been investigated in as much detail. This is partially due to limited noncoding functional annotation. Also, great mutation heterogeneity and potential correlations between neighboring sites give rise to substantial overdispersion in mutation count, resulting in problematic background rate estimation. Here, we address these issues with a new computational framework called LARVA. It integrates variants with a comprehensive set of noncoding functional elements, modeling the mutation counts of the elements with a β-binomial distribution to handle overdispersion. LARVA, moreover, uses regional genomic features such as replication timing to better estimate local mutation rates and mutational hotspots. We demonstrate LARVA's effectiveness on 760 whole-genome tumor sequences, showing that it identifies well-known noncoding drivers, such as mutations in the TERT promoter. Furthermore, LARVA highlights several novel highly mutated regulatory sites that could potentially be noncoding drivers. We make LARVA available as a software tool and release our highly mutated annotations as an online resource ( larva.gersteinlab.org ).

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: Alu insertions have contributed to 〉11% of the human genome and ~30–35 Alu subfamilies remain actively mobile, yet the characterization of polymorphic Alu insertions from short-read data remains a challenge. We build on existing computational methods to combine Alu detection and de novo assembly of WGS data as a means to reconstruct the full sequence of insertion events from Illumina paired end reads. Comparison with published calls obtained using PacBio long-reads indicates a false discovery rate below 5%, at the cost of reduced sensitivity due to the colocation of reference and non-reference repeats. We generate a highly accurate call set of 1614 completely assembled Alu variants from 53 samples from the Human Genome Diversity Project (HGDP) panel. We utilize the reconstructed alternative insertion haplotypes to genotype 1010 fully assembled insertions, obtaining 〉99% agreement with genotypes obtained by PCR. In our assembled sequences, we find evidence of premature insertion mechanisms and observe 5' truncation in 16% of Alu Ya5 and Alu Yb8 insertions. The sites of truncation coincide with stem-loop structures and SRP9/14 binding sites in the Alu RNA, implicating L1 ORF2p pausing in the generation of 5' truncations. Additionally, we identified variable Alu J and Alu S elements that likely arose due to non-retrotransposition mechanisms.

Description: Mutual information (MI), a quantity describing the nonlinear dependence between two random variables, has been widely used to construct gene regulatory networks (GRNs). Despite its good performance, MI cannot separate the direct regulations from indirect ones among genes. Although the conditional mutual information (CMI) is able to identify the direct regulations, it generally underestimates the regulation strength, i.e. it may result in false negatives when inferring gene regulations. In this work, to overcome the problems, we propose a novel concept, namely conditional mutual inclusive information (CMI2), to describe the regulations between genes. Furthermore, with CMI2, we develop a new approach, namely CMI2NI (CMI2-based network inference), for reverse-engineering GRNs. In CMI2NI, CMI2 is used to quantify the mutual information between two genes given a third one through calculating the Kullback–Leibler divergence between the postulated distributions of including and excluding the edge between the two genes. The benchmark results on the GRNs from DREAM challenge as well as the SOS DNA repair network in Escherichia coli demonstrate the superior performance of CMI2NI. Specifically, even for gene expression data with small sample size, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the regulation strength between genes. As a case study, CMI2NI was also used to reconstruct cancer-specific GRNs using gene expression data from The Cancer Genome Atlas (TCGA). CMI2NI is freely accessible at http://www.comp-sysbio.org/cmi2ni .

Description: High-throughput sequencing of DNA coding regions has become a common way of assaying genomic variation in the study of human diseases. Copy number variation (CNV) is an important type of genomic variation, but detecting and characterizing CNV from exome sequencing is challenging due to the high level of biases and artifacts. We propose CODEX, a normalization and CNV calling procedure for whole exome sequencing data. The Poisson latent factor model in CODEX includes terms that specifically remove biases due to GC content, exon capture and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. CODEX is compared to existing methods on a population analysis of HapMap samples from the 1000 Genomes Project, and shown to be more accurate on three microarray-based validation data sets. We further evaluate performance on 222 neuroblastoma samples with matched normals and focus on a well-studied rare somatic CNV within the ATRX gene. We show that the cross-sample normalization procedure of CODEX removes more noise than normalizing the tumor against the matched normal and that the segmentation procedure performs well in detecting CNVs with nested structures.

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: Comprehensive motif discovery under experimental conditions is critical for the global understanding of gene regulation. To generate a nearly complete list of human DNA motifs under given conditions, we employed a novel approach to de novo discover significant co-occurring DNA motifs in 349 human DNase I hypersensitive site datasets. We predicted 845 to 1325 motifs in each dataset, for a total of 2684 non-redundant motifs. These 2684 motifs contained 54.02 to 75.95% of the known motifs in seven large collections including TRANSFAC. In each dataset, we also discovered 43 663 to 2 013 288 motif modules, groups of motifs with their binding sites co-occurring in a significant number of short DNA regions. Compared with known interacting transcription factors in eight resources, the predicted motif modules on average included 84.23% of known interacting motifs. We further showed new features of the predicted motifs, such as motifs enriched in proximal regions rarely overlapped with motifs enriched in distal regions, motifs enriched in 5' distal regions were often enriched in 3' distal regions, etc. Finally, we observed that the 2684 predicted motifs classified the cell or tissue types of the datasets with an accuracy of 81.29%. The resources generated in this study are available at http://server.cs.ucf.edu/predrem/ .

Description: The prediction of novel pre-microRNA (miRNA) from genomic sequence has received considerable attention recently. However, the majority of studies have focused on the human genome. Previous studies have demonstrated that sensitivity (correctly detecting true miRNA) is sustained when human-trained methods are applied to other species, however they have failed to report the dramatic drop in specificity (the ability to correctly reject non-miRNA sequences) in non-human genomes. Considering the ratio of true miRNA sequences to pseudo-miRNA sequences is on the order of 1:1000, such low specificity prevents the application of most existing tools to non-human genomes, as the number of false positives overwhelms the true predictions. We here introduce a framework (SMIRP) for creating species-specific miRNA prediction systems, leveraging sequence conservation and phylogenetic distance information. Substantial improvements in specificity and precision are obtained for four non-human test species when our framework is applied to three different prediction systems representing two types of classifiers (support vector machine and Random Forest), based on three different feature sets, with both human-specific and taxon-wide training data. The SMIRP framework is potentially applicable to all miRNA prediction systems and we expect substantial improvement in precision and specificity, while sustaining sensitivity, independent of the machine learning technique chosen.

Description: Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool ( cancerlandscapes.org ), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets.

Description: Transposable elements (TEs) are mobile genomic DNA sequences found in most organisms. They so densely populate the genomes of many eukaryotic species that they are often the major constituents. With the rapid generation of many plant genome sequencing projects over the past few decades, there is an urgent need for improved TE annotation as a prerequisite for genome-wide studies. Analogous to the use of RNA-seq for gene annotation, we propose a new method for de novo TE annotation that uses as a guide 24 nt-siRNAs that are a part of TE silencing pathways. We use this new approach, called TASR (for Transposon Annotation using Small RNAs), for de novo annotation of TEs in Arabidopsis , rice and soybean and demonstrate that this strategy can be successfully applied for de novo TE annotation in plants. Executable PERL is available for download from: http://tasr-pipeline.sourceforge.net/

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: RNA-Seq is gradually becoming the standard tool for transcriptomic expression studies in biological research. Although considerable progress has been recorded in the development of statistical algorithms for the detection of differentially expressed genes using RNA-Seq data, the list of detected genes can differ significantly between algorithms. We present a new method (PANDORA) that combines multiple algorithms toward a summarized result, more efficiently reflecting true experimental outcomes. This is achieved through the systematic combination of several analysis algorithms, by weighting their outcomes according to their performance with realistically simulated data sets generated from real data. Results supported by the analysis of both simulated and real data from different organisms as well as correlation with PolII occupancy demonstrate that PANDORA improves the detection of differential expression. It accomplishes this by optimizing the tradeoff between standard performance measurements, such as precision and sensitivity.

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.

Description: We define a new category of candidate tumor drivers in cancer genome evolution: ‘selected expression regulators’ (SERs)—genes driving dysregulated transcriptional programs in cancer evolution. The SERs are identified from genome-wide tumor expression data with a novel method, namely SPARROW ( SPAR se selected exp R essi O n regulators identified W ith penalized regression). SPARROW uncovers a previously unknown connection between cancer expression variation and driver events, by using a novel sparse regression technique. Our results indicate that SPARROW is a powerful complementary approach to identify candidate genes containing driver events that are hard to detect from sequence data, due to a large number of passenger mutations and lack of comprehensive sequence information from a sufficiently large number of samples. SERs identified by SPARROW reveal known driver mutations in multiple human cancers, along with known cancer-associated processes and survival-associated genes, better than popular methods for inferring gene expression networks. We demonstrate that when applied to acute myeloid leukemia expression data, SPARROW identifies an apoptotic biomarker ( PYCARD ) for an investigational drug obatoclax. The PYCARD and obatoclax association is validated in 30 AML patient samples.