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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2014

Genome-wide comparative analysis reveals human- mouse regulatory landscape and evolution

BackgroundBecause species-specific gene expression is driven by species-specific regulation, understanding the relationship between sequence and function of the regulatory regions in different species will help elucidate how differences among species arise. Despite active experimental and computational research, the relationships among sequence, conservation, and function are still poorly understood.\n\nResultsWe compared transcription factor occupied segments (TFos) for 116 human and 35 mouse TFs in 546 human and 125 mouse cell types and tissues from the Human and the Mouse ENCODE projects. We based the map between human and mouse TFos on a one-to-one nucleotide cross-species mapper, bnMapper, that utilizes whole genome alignments (WGA).\n\nOur analysis shows that TFos are under evolutionary constraint, but a substantial portion (25.1% of mouse and 25.85% of human on average) of the TFos does not have a homologous sequence on the other species; this portion varies among cell types and TFs. Furthermore, 47.67% and 57.01% of the homologous TFos sequence shows binding activity on the other species for human and mouse respectively. However, 79.87% and 69.22% is repurposed such that it binds the same TF in different cells or different TFs in the same cells. Remarkably, within the set of TFos not showing conservation of occupancy, the corresponding genome regions in the other species are preferred locations of novel TFos. These events suggest that a substantial amount of functional regulatory sequences is exapted from other biochemically active genomic material.\n\nDespite substantial repurposing of TFos, we did not find substantial changes in their predicted target genes, suggesting that CRMs buffer evolutionary events allowing little or no change in the TF - target gene associations. Thus, the small portion of TFos with strictly conserved occupancy underestimates the degree of conservation of regulatory interactions.\n\nConclusionWe mapped regulatory sequences from an extensive number of TFs and cell types between human and mouse. A comparative analysis of this correspondence unveiled the extent of the shared regulatory sequence across TFs and cell types under study. Importantly, a large part of the shared regulatory sequence repurposed on the other species. This sequence, fueled by turnover events, provides a strong case for exaptation in regulatory elements.

Bioinformatics↗
bioRxiv2014

A pedagogical walkthrough of computational modeling and simulation of Wnt signaling pathway using static causal models in Matlab

AbstractA tutorial introduction to computational modeling of Wnt signaling pathway in a human colorectal cancer dataset using static Bayesian network models is provided. The walkthrough might aid bio-logists/informaticians in understanding the design of computational experiments that is interleaved with exposition of the Matlab code and causal models from Bayesian Network toolbox. This is done in order to ease the understanding of beginner students and researchers in transition to computational signaling biology, who intend to work in the field of modeling of signaling pathways. The manuscript expounds the computational flow of the contents in advance article1 via code development and takes the reader in a step by step process of how * the collection and the transformation of the available biological information from literature is done, * the integration of the heterogeneous data and prior biological knowledge in the network is achieved, * conditional probability tables for nodes in biologically inspired tables are estimated, * the simulation study is designed, * the hypothesis regarding a biological phenomena is transformed into computational framework, and * results and inferences drawn using d-connectivity/separability are reported. The manuscript finally ends with a programming assignment to help the readers get hands on experience of a perturbation project. Matlab code with dataset is made available under GNU GPL v3 license at google code project on https://code.google.com/p/static-bn-for-wnt-signaling-pathway\n\nInsight, Innovation and IntegrationSimulation study involving computational experiments dealing with Wnt signaling pathways abound in literature but often lack a pedagogical perspective that might ease the understanding of beginner students and researchers in transition who intend to work on modeling of the pathway. This paucity might happen due to restrictive policies which enforce an unwanted embargo on the sharing of important scientific knowledge. The manuscript elucidates embedding of prior biological knowledge, integration of heterogeneous information, transformation of biological hypothesis into computational framework and design of experiments in a simple manner interleaved with aspects of Bayesian Network toolbox and Matlab code so as to help readers get a feel of a project related to modeling of the pathway.

Bioinformatics↗
bioRxiv2014

Tools and Methods from the Anopheles 16 Genome Project

The dramatic reduction in sequencing costs has resulted in many initiatives to sequence certain organisms and populations. These initiatives aim to not only sequence and assemble genomes but also to perform a more broader analysis of the population structure. As part of the Anopheline Genome Consortium, which has a vested interest in studying anpopheline mosquitoes, we developed novel methods and tools to further the communities goals. We provide a brief description of these methods and tools as well as assess the contributions that each offers to the broader study of comparative genomics.

Bioinformatics↗
bioRxiv2014

Interactive analysis and quality assessment of single-cell copy-number variations

We present an open-source visual-analytics web platform, Ginkgo (http://qb.cshl.edu/ginkgo), for the interactive analysis and quality assessment of single-cell copy-number alterations. Ginkgo automatically constructs copy-number profiles of individual cells from mapped reads, as well as constructing phylogenetic trees of related cells. We validate Ginkgo by reproducing the results of five major studies and examine the data characteristics of three commonly used single-cell amplification techniques to conclude DOP-PCR to be the most consistent for CNV analysis.

Bioinformatics↗
bioRxiv2014

ENVIRONMENTS and EOL: identification of Environment Ontology terms in text and the annotation of the Encyclopedia of Life

SummaryThe association of organisms to their environments is a key issue in exploring biodiversity patterns. This knowledge has traditionally been scattered, but textual descriptions of taxa and their habitats are now being consolidated in centralized resources. However, structured annotations are needed to facilitate large-scale analyses. Therefore, we developed ENVIRONMENTS, a fast dictionary-based tagger capable of identifying Environment Ontology (ENVO) terms in text. We evaluate the accuracy of the tagger on a new manually curated corpus of 600 Encyclopedia Of Life (EOL) species pages. We use the tagger to associate taxa with environments by tagging EOL text content monthly, and integrate the results into the EOL to disseminate them to a broad audience of users.\n\nAvailability and implementationThe software and the corpus are available under the open-source BSD and the CC-BY-NC-SA 3.0 licenses, respectively, at http://environments.hcmr.gr\n\nContactpafilis@hcmr.gr; lars.juhl.jensen@cpr.ku.dk

Bioinformatics↗
bioRxiv2014

Circumstantial Evidence? Comparison of Statistical Learning Methods using Functional Annotations for Prioritizing Risk Variants

Although technology has triumphed in facilitating routine genome re-sequencing, new challenges have been created for the data analyst. Genome scale surveys of human disease variation generate volumes of data that far exceed capabilities for laboratory characterization, and importantly also create a substantial burden of type I error. By incorporating a variety of functional annotations as predictors, such as regulatory and protein coding elements, statistical learning has been widely investigated as a mechanism for the prioritization of genetic variants that are more likely to be associated with complex disease. These methods offer a hope of identification of sufficiently large numbers of truly associated variants, to make cost-effective the large-scale functional characterization necessary to progress genome scale experiments. We compared the results from three published prioritization procedures which use different statistical learning algorithms and different predictors with regard to the quantity, type and coding of the functional annotations. In this paper we also explore different combinations of algorithm and annotation set. We train the models in 60% of the data and reserve the remainder for testing the accuracy. As an application, we tested which methodology performed the best for prioritizing sub-genome-wide-significant variants (5x10-8<p<1x10-6) using data from the first and second rounds of a large schizophrenia meta-analysis by the Psychiatric Genomics Consortium. Results suggest that all methods have considerable (and similar) predictive accuracies (AUCs 0.64-0.71). However, predictive accuracy results obtained from the test set do not always reflect results obtained from the application to the schizophrenia meta-analysis. In conclusion, a variety of algorithms and annotations seem to have a similar potential to effectively enrich true risk variants in genome scale datasets, however none offer more than incremental improvement in prediction. We discuss how methods might be evolved towards the step change in the risk variant prediction required to address the impending bottleneck of the new generation of genome re-sequencing studies.

Bioinformatics↗
bioRxiv2014

A robust statistical framework for reconstructing genomes from metagenomic data

We present software that reconstructs genomes from shotgun metagenomic sequences using a reference-independent approach. This method permits the identification of OTUs in large complex communities where many species are unknown. Binning reduces the complexity of a metagenomic dataset enabling many downstream analyses previously unavailable. In this study we developed MetaBAT, a robust statistical framework that integrates probabilistic distances of genome abundance with sequence composition for automatic binning. Applying MetaBAT to a human gut microbiome dataset identified 173 highly specific genomes bins including many representing previously unidentified species.

Bioinformatics↗
bioRxiv2014

Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants, and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks--graphs with multiple node and edge types--for accomplishing both tasks. First we constructed a network with 18 node types--genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections--and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as fundamental mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data integration across multiple domains.\n\nAuthor SummaryFor complex human diseases, identifying the genes harboring susceptibility variants has taken on medical importance. Disease-associated genes provide clues for elucidating disease etiology, predicting disease risk, and highlighting therapeutic targets. Here, we develop a method to predict whether a given gene and disease are associated. To capture the multitude of biological entities underlying pathogenesis, we constructed a heterogeneous network, containing multiple node and edge types. We built on a technique developed for social network analysis, which embraces disparate sources of data to make predictions from heterogeneous networks. Using the compendium of associations from genome-wide studies, we learned the influential mechanisms underlying pathogenesis. Our findings provide a novel perspective about the existence of pervasive pleiotropy across complex diseases. Furthermore, we suggest transcriptional signatures of perturbations are an underutilized resource amongst prioritization approaches. For multiple sclerosis, we demonstrated our ability to prioritize future studies and discover novel susceptibility genes. Researchers can use these predictions to increase the statistical power of their studies, to suggest the causal genes from a set of candidates, or to generate evidence-based experimental hypothesis.

Bioinformatics↗
bioRxiv2014

Triticeae resources in Ensembl Plants

Recent developments in DNA sequencing have enabled the large and complex genomes of many crop species to be determined for the first time, even those previously intractable due to their polyploid nature. Indeed, over the course of the last two years, the genome sequences of several commercially important cereals, notably barley and bread wheat, have become available, as well as those of related wild species. While still incomplete, comparison to other, more completely assembled species suggests that coverage of genic regions is likely to be high.\n\nEnsembl Plants (http://plants.ensembl.org) is an integrative resource organising, analysing and visualising genome-scale information for important crop and model plants. Available data includes reference genome sequence, variant loci, gene models and functional annotation. For variant loci, individual and population genotypes, linkage information and, where available, phenotypic information, are shown. Comparative analyses are performed on DNA and protein sequence alignments. The resulting genome alignments and gene trees, representing the implied evolutionary history the gene family, are made available for visualisation and analysis. Driven by the use case of bread wheat, specific extensions to the analysis pipelines and web interface have recently been developed to support polyploid genomes.\n\nData in Ensembl Plants is accessible through a genome browser incorporating various specialist interfaces for different data types, and through a variety of additional methods for programmatic access and data mining. These interfaces are consistent with those offered through the Ensembl interface for the genomes of non-plant species, including those of plant pathogens, pests and pollinators, facilitating the study of the plant in its environment.

Bioinformatics↗
bioRxiv2014

A novel computational approach for genome-wide prediction of small RNAs in bacteria

Small regulatory RNAs (sRNAs) are the most abundant post-transcriptional regulators in bacteria. They serve ubiquitous roles that control nearly every aspects of bacterial physiology. Identification of important features from sRNAs sequences will guide the computational prediction of new sRNA sequences for a better understanding of the pervasive sRNA-mediated regulation in bacteria. In this study, we have performed systematic analyses of many sequence and structural features that are possibly related to sRNA properties and identified a subset of significant features that effectively discriminate sRNAs sequences from random sequences. we then used a neural network model that integrated these subfeatures on unlabeled testing datasets, and it had achieved a 92.2% recall and 89.8% specificity. Finally, we applied this prediction model for genome-wide identification of sRNAs-encoded genes using a sliding-window approach. We recovered multiple known sRNAs and hundreds of predicted new sRNAs. These candidate novel sRNAs deserve extensive study to better understand the sRNA-mediated regulatory network in bacteria.

Bioinformatics↗
bioRxiv2014

Cell-Line Annotation on Europe PubMed Central

A cell line is a cell culture developed from a single cell and therefore consisting of cells with a uniform genetic make-up. A cell line has an important role as a research resource such as organisms, antibodies, constructs, knockdown reagents, etc. Unique identification of cell lines in the biomedical literature is important for the reproducibility of science. As data citation, resource citation is also important for resource re-use.\n\nIn this paper, we mention the challenges of identifying cell lines and describe a system for cell line annotation with perluminary results.

Bioinformatics↗
bioRxiv2014

Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates

Quantification of RNA transcripts with RNA-Seq is inaccurate due to positional fragment bias, which is not represented appropriately by current statistical models of RNA-Seq data. This article introduces the Mix2(rd. \"mixquare\") model, which uses a mixture of probability distributions to model the transcript specific positional fragment bias. The parameters of the Mix2 model can be efficiently trained with the Expectation Maximization (EM) algorithm resulting in simultaneous estimates of the transcript abundances and transcript specific positional biases. Experiments are conducted on synthetic data and the Universal Human Reference (UHR) and Brain (HBR) sample from the Microarray quality control (MAQC) data set. Comparing the correlation between qPCR and FPKM values to state-of-the-art methods Cufflinks and PennSeq we obtain an increase in R2 value from 0.44 to 0.6 and from 0.34 to 0.54. In the detection of differential expression between UHR and HBR the true positive rate increases from 0.44 to 0.71 at a false positive rate of 0.1. Finally, the Mix2 model is used to investigate biases present in the MAQC data. This reveals 5 dominant biases which deviate from the common assumption of a uniform fragment distribution. The Mix2 software is available at http://www.lexogen.com/fileadmin/uploads/bioinfo/mix2model.tgz.

Bioinformatics↗
bioRxiv2014

FlexAID: Revisiting docking on non native-complex structures

Small-molecule protein docking is an essential tool in drug design and to understand molecular recognition. In the present work we introduce FlexAID, a small-molecule docking algorithm that accounts for target side-chain flexibility and utilizes a soft scoring function, i.e. one that is not highly dependent on specific geometric criteria, based on surface complementarity. The pairwise energy parameters were derived from a large dataset of true positive poses and negative decoys from the PDBbind dataset through an iterative process using Monte Carlo simulations. The prediction of binding poses is tested using the independent Astex dataset while performance in virtual screening is evaluated using a subset of the DUD dataset. We compare FlexAID to AutoDock Vina, FlexX, and rDock in an extensive number of scenarios to understand the strengths and limitations of the different programs as well as to reported results for Glide, GOLD and DOCK6 where applicable. The most relevant among these scenarios is that of docking on flexible non native-complex structures where as is the case in reality, the target conformation in the bound form is not known a priori. We demonstrate that FlexAID, unlike other programs, is robust against increasing structural variability. FlexAID obtains equivalent sampling success as GOLD and performs better than AutoDock Vina or FlexX in all scenarios against non native-complex structures. FlexAID is better than rDock when there is at least one critical side-chain movement required upon ligand binding. In virtual screening, FlexAID rescored results are comparable to those of AutoDock Vina and rDock. The higher accuracy in flexible targets where critical movements are required, intuitive PyMOL-integrated graphical user interface and free source code as well as pre-compiled executables for Windows, Linux and Mac OS make FlexAID a welcome addition to the arsenal of existing small-molecule protein docking methods.\n\nAUTHOR SUMMARYProtein ligand interactions are essential to understand biological processes such as enzymatic reactions, signalling pathways as well as in the development of new medicines. Docking algorithms permit to predict the structure of a ligand protein complex at the atomic level. Several docking algorithms were developed over the years with a tendency towards utilizing very specific and detailed (i.e., hard) descriptions of molecular interactions. In this work we present a new docking algorithm called FlexAID that utilizes a very general and superficial (i.e., soft) description of interactions based on atomic surface areas in contact. We demonstrate that FlexAID can achieve better accuracy in predicting the structure of ligand protein complexes than existing accessible widely used or state-of-the-art methods in real scenarios when using flexible targets harbouring structural differences with respect to the final protein structure present in the ligand protein complex. FlexAID and its PyMOL-integrated graphical user interface are free, easy to use and available for Windows, Linux and Mac OS.

Bioinformatics↗
bioRxiv2014

SubClonal Hierarchy Inference from Somatic Mutations: automatic reconstruction of cancer evolutionary trees from multi-region next generation sequencing

Recent improvements in next-generation sequencing of tumor samples and the ability to identify somatic mutations at low allelic fractions have opened the way for new approaches to model the evolution of individual cancers. The power and utility of these models is increased when tumor samples from multiple sites are sequenced. Temporal ordering of the samples may provide insight into the etiology of both primary and metastatic lesions and rationalizations for tumor recurrence and therapeutic failures. Additional insights may be provided by temporal ordering of evolving subclones - cellular subpopulations with unique mutational profiles. Current methods for subclone hierarchy inference tightly couple the problem of temporal ordering with that of estimating the fraction of cancer cells harboring each mutation. We present a new framework that includes a rigorous statistical hypothesis test and a collection of tools that make it possible to decouple these problems, which we believe will enable substantial progress in the field of subclone hierarchy inference. The methods presented here can be flexibly combined with methods developed by others addressing either of these problems. We provide tools to interpret hypothesis test results, which inform phylogenetic tree construction, and we introduce the first genetic algorithm designed for this purpose. The utility of our framework is systematically demonstrated in simulations. For most tested combinations of tumor purity, sequencing coverage, and tree complexity, good power ([&ge;] 0.8) can be achieved and Type 1 error is well controlled when at least three tumor samples are available from a patient. Using data from three published multi-region tumor sequencing studies of (murine) small cell lung cancer, acute myeloid leukemia, and chronic lymphocytic leukemia, in which the authors reconstructed subclonal phylogenetic trees by manual expert curation, we show how different configurations of our tools can identify either a single tree in agreement with the authors, or a small set of trees, which include the authors preferred tree. Our results have implications for improved modeling of tumor evolution and the importance of multi-region tumor sequencing.\n\nAuthor SummaryCancer is a genetic disease, driven by DNA mutations. Each tumor is composed of millions of cells with differing genetic profiles that compete with each other for resources in a process similar to Darwinian evolution. We describe a computational framework to model tumor evolution on the cellular level, using next-generation sequencing. The framework is the first to apply a rigorous statistical hypothesis test designed to inform a new search algorithm. Both the test and the algorithm are based on evolutionary principles. The utility of the framework is shown in computer simulations and by automated reconstruction of the cellular evolution underlying murine small cell lung cancers, acute myeloid leukemias and chronic lymophocytic leukemias, from three recent published studies.

Bioinformatics↗
bioRxiv2014

Reveel: large-scale population genotyping using low-coverage sequencing data

Population low-coverage whole-genome sequencing is rapidly emerging as a prominent approach for discovering genomic variation and genotyping a cohort. This approach combines substantially lower cost than full-coverage sequencing with whole-genome discovery of low-allele-frequency variants, to an extent that is not possible with array genotyping or exome sequencing. However, a challenging computational problem arises when attempting to discover variants and genotype the entire cohort. Variant discovery and genotyping are relatively straightforward on a single individual that has been sequenced at high coverage, because the inference decomposes into the independent genotyping of each genomic position for which a sufficient number of confidently mapped reads are available. However, in cases where low-coverage population data are given, the joint inference requires leveraging the complex linkage disequilibrium patterns in the cohort to compensate for sparse and missing data in each individual. The potentially massive computation time for such inference, as well as the missing data that confound low-frequency allele discovery, need to be overcome for this approach to become practical. Here, we present Reveel, a novel method for single nucleotide variant calling and genotyping of large cohorts that have been sequenced at low coverage. Reveel introduces a novel technique for leveraging linkage disequilibrium that deviates from previous Markov-based models. We evaluate Reveels performance through extensive simulations as well as real data from the 1000 Genomes Project, and show that it achieves higher accuracy in low-frequency allele discovery and substantially lower computation cost than previous state-of-the-art methods.

Bioinformatics↗
bioRxiv2014

Generalised empirical Bayesian methods for discovery of differential data in high-throughput biology

MotivationHigh-throughput data are now commonplace in biological research. Rapidly changing technologies and application mean that novel methods for detecting differential behaviour that account for a large P, small n setting are required at an increasing rate. The development of such methods is, in general, being done on an ad hoc basis, requiring further development cycles and a lack of standardization between analyses.\n\nResultsWe present here a generalised method for identifying differential behaviour within high-throughput biological data through empirical Bayesian methods. This approach is based on our baySeq algorithm for identification of differential expression in RNA-seq data based on a negative binomial distribution, and in paired data based on a beta-binomial distribution. Here we show how the same empirical Bayesian approach can be applied to any parametric distribution, removing the need for lengthy development of novel methods for differently distributed data. Comparisons with existing methods developed to address specific problems in high-throughput biological data show that these generic methods can achieve equivalent or better performance. A number of enhancements to the basic algorithm are also presented to increase flexibility and reduce computational costs.\n\nAvailabilityThe methods are implemented in the R baySeq (v2) package, available on Bioconductor http://www.bioconductor.org/packages/release/bioc/html/baySeq.html.\n\nContacttjh48@cam.ac.uk

Bioinformatics↗
bioRxiv2014

Alignment by numbers: sequence assembly using compressed numerical representations

MotivationDNA sequencing instruments are enabling genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and interpret sequence data. Established methods for computational sequence analysis generally use nucleotide-level resolution of sequences, and while such approaches can be very accurate, increasingly ambitious and data-intensive analyses are rendering them impractical for applications such as genome and metagenome assembly. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction methods are routinely used to reduce the computational burden of analyses. We therefore seek to address the question of whether it is possible to improve the efficiency of sequence alignment by applying dimensionality reduction methods to numerically represented nucleotide sequences.\n\nResultsTo explore the applicability of signal transformation and dimensionality reduction methods to sequence assembly, we implemented a short read aligner and evaluated its performance against simulated high diversity viral sequences alongside four existing aligners. Using our sequence transformation and feature selection approach, alignment time was reduced by up to 14-fold compared to uncompressed sequences and without reducing alignment accuracy. Despite using highly compressed sequence transformations, our implementation yielded alignments of similar overall accuracy to existing aligners, outperforming all other tools tested at high levels of sequence variation. Our approach was also applied to the de novo assembly of a simulated diverse viral population. Our results demonstrate that full sequence resolution is not a prerequisite of accurate sequence alignment and that analytical performance can be retained and even enhanced through appropriate dimensionality reduction of sequences.

Bioinformatics↗