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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 271 records · Page 15Linked to original sources

bioRxiv2016

DiffTAD: Detecting Differential contact frequency in Topologically Associating Domains Hi-C experiments between conditions

MotivationIn recent years, the interest in analyzing chromosome conformation by Hi-C and related techniques has grown. It has been shown that contact frequency matrices obtained by these methods correlate with other methods of measurement of activity such as transcriptomics and histone modification assays. This brings a question of testing for differential contact frequency between experiments to the field.\n\nResultsIn this work, we provide a freely available software that implements two statistical methods for testing the significance of differential contact frequency in topological domains between two experiments. One method follows an empirical, permutation based approach to computing p-values, while the other is a parametric test based on the Poisson-Binomial distribution.\n\nAvailabilityThe software is freely available on the GNU General Public License at https://bitbucket.org/rzaborowski/differential-analysis\n\nContact[r.zaborowski|bartek]@mimuw.edu.pl\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2016

Computational studies of P-glycoprotein polymorphisms in antiepileptic drug resistance mechanisms

The treatment of epilepsy using antiepileptogenic drugs is complicated by drug re-sistance, resulting in treatment failure in more than one-third of cases. Human P-glycoprotein (hPGP; MDR1) is a known epileptogenic mediator. Given that experimental investigations have suggested a role for pharmacogenetics in this treatment failure, it would be of interest to study hPGP polymorphisms that might contribute to the emergence of drug resistance. Changes in protein functional activity could result from point mutations as well as altered abundance. Bioinformatics approaches were used to assess and rank the functional impact of 20 missense MDR1 polymorphisms and the top five were selected. The structures of the wildtype and mutant hPGP were modelled based on the mouse PGP structure. Docking studies of the wildtype and mutant hPGP with four standard anti-epileptic drugs were carried out. Our results revealed that the drug binding site with respect to the wildtype protein was uniform. However the mutant hPGP proteins displayed a repertoire of binding sites with stronger binding affinities towards the drug. Our studies indicated that specific polymorphisms in MDR1 could drive conformational changes of PGP structure, facilitating altered contacts with drug-substrates and resulting in drug extrusion. This suggests that MDR1 polymorphisms could play an active role in modifying drug bioavailability, leading to pharmacoresistance in antiepileptic chemotherapy.

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bioRxiv2016

McClintock: An integrated pipeline for detecting transposable element insertions in whole genome shotgun sequencing data.

BackgroundTransposable element (TE) insertions are among the most challenging type of variants to detect in genomic data because of their repetitive nature and complex mechanisms of replication. Nevertheless, the recent availability of large resequencing datasets has spurred the development of many new methods to detect TE insertions in whole genome shotgun sequences. These methods generate output in diverse formats and have a large number of software and data dependencies, making their comparative evaluation challenging for potential users.\n\nResultsHere we develop an integrated bioinformatics pipeline for the detection of TE insertions in whole genome shotgun data, called McClintock (https://github.com/bergmanlab/mcclintock), that automatically runs and generates standardized output for multiple TE detection methods. We demonstrate the utility of the McClintock system by performing comparative evaluation of six TE detection methods using simulated and real genome data from the model microbal eukaryote, Saccharomyces cerevisiae. We find substantial variation among McClintock component methods in their ability to detect non-reference insertions in the yeast genome, but show that non-reference TEs at nearly all biologically-realistic locations can be detected in simulated data by combining multiple methods that use split-read and read-pair evidence. In general, our results reveal that split-read methods detect fewer non-reference TE insertions than read-pair methods, but generally have much higher positional accuracy. Analysis of a large sample of real yeast genomes reveals that most, but not all, McClintock component methods can recover known aspects of TE biology in yeast such as the transpositional activity status of families, tRNA gene target preferences, and target site duplication structure, albeit with varying levels of positional accuracy.\n\nConclusionsOur results suggest that no single TE detection method currently provides comprehensive detection of non-reference TEs, even in the context of a simplified model eukaryotic genome like S. cerevisiae. In spite of these limitations, the McClintock system provides a framework for testing, developing and integrating results from multiple TE detection methods to achieve this ultimate aim, as well as useful guidance for yeast researchers to select appropriate TE detection tools.

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bioRxiv2016

Metagenomic Binning through Multi-resolution Genomic Binary Patterns

Bioinformatics methods typically use textual representations of genetic information, represented computationally as strings or sub-strings of the characters A, T, G and C. Image processing methods offer a rich source of alternative descriptors as they are designed to work in the presence of noisy data without the need for exact matching. We introduce a method, multi-resolution local binary patterns (MLBP) from image processing to extract local texture changes from nucleotide sequence data. We apply this feature space to the alignment-free binning of metagenomic data. The effectiveness of MLBP is demonstrated using both simulated and real human gut microbial communities. The intuition behind our method is the MLBP feature vectors permit sequence comparisons without the need for explicit pairwise matching. Sequence reads or contigs can then be represented as vectors and their texture compared efficiently using state-of-the-art machine learning algorithms to perform dimensionality reduction to capture eigengenome information and perform clustering (here using RSVD and BH-tSNE). We demonstrate this approach outperforms existing methods based on k-mer frequency. The image processing method, MLBP, thus offers a viable alternative feature space to textual representations of sequence data. The source code for our Multi-resolution Genomic Binary Patterns method can be found at https://github.com/skouchaki/MrGBP.

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bioRxiv2016

tensorBF: an R package for Bayesian tensor factorization

With recent advancements in measurement technologies, many multi-way and tensor datasets have started to emerge. Exploiting the natural tensor structure in the data has been shown to be advantageous for both explorative and predictive studies in several application areas of bioinformatics and computational biology. Therefore, there has subsequently arisen a need for robust and flexible tools for effectively analyzing tensor data sets. We present the R package tensorBF, which is the first R package providing Bayesian factorization of a tensor. Our package implements a generative model that automatically identifies the number of factors needed to explain the tensor, overcoming a key limitation of traditional tensor factorizations. We also recommend best practices when using tensor factorizations for both, explorative and predictive analysis with an example application on drug response dataset. The package also implements tools related to the normalization of data, informative noise priors and visualization. Availability: The package is available at https://cran.r-project.org/package=tensorBF.

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bioRxiv2017

Detection and quantification of viral RNA in human tumors using open source pipeline: viGEN

An estimated 17% of cancers worldwide are associated with infectious causes. The extent and biological significance of viral presence/infection in actual tumor samples is generally unknown but could be measured using human transcriptome (RNA-seq) data from tumor samples.\n\nWe present an open source bioinformatics pipeline viGEN, which combines existing well-known and novel RNA-seq tools for not only the detection and quantification of viral RNA, but also variants in the viral transcripts.\n\nThe pipeline includes 4 major modules: The first module allows to align and filter out human RNA sequences; the second module maps and count (remaining un-aligned) reads against reference genomes of all known and sequenced human viruses; the third module quantifies read counts at the individual viral genes level thus allowing for downstream differential expression analysis of viral genes between experimental and controls groups. The fourth module calls variants in these viruses. To the best of our knowledge, there are no publicly available pipelines or packages that would provide this type of complete analysis in one open source package.\n\nIn this paper, we applied the viGEN pipeline to two case studies. We first demonstrate the working of our pipeline on a large public dataset, the TCGA cervical cancer cohort. We also performed additional in-depth analyses on a small focused study of TCGA liver cancer patients. In this cohort, we perform viral-gene quantification, viral-variant extraction and survival analysis. This allowed us to find differentially expressed viral-transcripts and viral-variants between the groups of patients, and connect them to clinical outcome.\n\nFrom our analyses, we show that we were able to successfully detect the human papilloma virus among the TCGA cervical cancer patients. We compared the viGEN pipeline with two metagenomics tools and demonstrate similar sensitivity/specificity. We were also able to quantify viral-transcripts and extract viral-variants using the liver cancer dataset. The results presented corresponded with published literature in terms of rate of detection, viral gene expression patterns and impact of several known variants of HBV genome. Results also show novel information about distinct patterns of expression and co-expression in Hepatitis B and the Human Endogenous Retrovirus (HERV) K113 viruses.\n\nThis pipeline is generalizable, and can be used to provide novel biological insights into the significance of viral and other microbial infections in complex diseases, tumorigeneses and cancer immunology. The source code, with example data and tutorial is available at: https://github.com/ICBI/viGEN/.

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bioRxiv2017

Uniform Resolution of Compact Identifiers for Biomedical Data

Most biomedical data repositories issue locally-unique accessions numbers, but do not provide globally unique, machine-resolvable, persistent identifiers for their datasets, as required by publishers wishing to implement data citation in accordance with widely accepted principles. Local accessions may however be prefixed with a namespace identifier, providing global uniqueness. Such \"compact identifiers\" have been widely used in biomedical informatics to support global resource identification with local identifier assignment.\n\nWe report here on our project to provide robust support for machine-resolvable, persistent compact identifiers in biomedical data citation, by harmonizing the Identifiers.org and N2T.net (Name-To-Thing) meta-resolvers and extending their capabilities. Identifiers.org services hosted at the European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), and N2T.net services hosted at the California Digital Library (CDL), can now resolve any given identifier from over 600 source databases to its original source on the Web, using a common registry of prefix-based redirection rules.\n\nWe believe these services will be of significant help to publishers and others implementing persistent, machine-resolvable citation of research data.

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bioRxiv2017

Accessible, curated metagenomic data through ExperimentHub

We present curatedMetagenomicData, a Bioconductor and command-line interface to thousands of metagenomic profiles from the Human Microbiome Project and other publicly available datasets, and ExperimentHub, a platform for convenient cloud-based distribution of data to the R desktop. The resource provides standardized per-participant metadata linked to bacterial, fungal, archaeal, and viral taxonomic abundances, as well as quantitative metabolic functional profiles. The datasets can be immediately analyzed in R or other software with a minimum of bioinformatic expertise and no preprocessing of data. We demonstrate identification of taxonomic/functional correlations, an investigation of gut \"enterotypes\", and a comparison of the accuracy of disease classification from different data types. These documented analyses can be reproduced efficiently on a laptop, without the barriers of working with large-scale, raw sequencing data. The building and expansion of curatedMetagenomicData is based entirely on open source software and pipelines, to facilitate the addition of new microbiome datasets and methods.

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bioRxiv2017

Deep Recurrent Neural Network for Protein Function Prediction from Sequence

As high-throughput biological sequencing becomes faster and cheaper, the need to extract useful information from sequencing becomes ever more paramount, often limited by low-throughput experimental characterizations. For proteins, accurate prediction of their functions directly from their primary amino-acid sequences has been a long standing challenge. Here, machine learning using artificial recurrent neural networks (RNN) was applied towards classification of protein function directly from primary sequence without sequence alignment, heuristic scoring or feature engineering. The RNN models containing long-short-term-memory (LSTM) units trained on public, annotated datasets from UniProt achieved high performance for in-class prediction of four important protein functions tested, particularly compared to other machine learning algorithms using sequence-derived protein features. RNN models were used also for out-of-class predictions of phylogenetically distinct protein families with similar functions, including proteins of the CRISPR-associated nuclease, ferritin-like iron storage and cytochrome P450 families. Applying the trained RNN models on the partially unannotated UniRef100 database predicted not only candidates validated by existing annotations but also currently unannotated sequences. Some RNN predictions for the ferritin-like iron sequestering function were experimentally validated, even though their sequences differ significantly from known, characterized proteins and from each other and cannot be easily predicted using popular bioinformatics methods. As sequencing and experimental characterization data increases rapidly, the machine-learning approach based on RNN could be useful for discovery and prediction of homologues for a wide range of protein functions.

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bioRxiv2017

Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics

MotivationIn Bayesian phylogenetic inference, marginal likelihoods are estimated using either the path-sampling or stepping-stone-sampling algorithms. Both algorithms are computationally demanding because they require a series of power posterior Markov chain Monte Carlo (MCMC) simulations. Here we introduce a general parallelization strategy that distributes the power posterior MCMC simulations and the likelihood computations over available CPUs. Our parallelization strategy can easily be applied to any statistical model despite our primary focus on molecular substitution models in this study.\n\nResultsUsing two phylogenetic example datasets, we demonstrate that the runtime of the marginal likelihood estimation can be reduced significantly even if only two CPUs are available (an average performance increase of 1.96x). The performance increase is nearly linear with the number of available CPUs. We record a performance increase of 11.4x for cluster nodes with 16 CPUs, representing a substantial reduction to the runtime of marginal likelihood estimations. Hence, our parallelization strategy enables the estimation of marginal likelihoods to complete in a feasible amount of time which previously needed days, weeks or even months.\n\nAvailabilityThe methods described here are implemented in our open-source software RevBayes which is available from http://www.RevBayes.com.\n\nContactSebastian.Hoehna@gmail.com\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2017

Bootstrap Distillation: Non-parametric Internal Validation of GWAS Results by Subgroup Resampling

Genome-wide Association Studies are carried out on a large number of genetic variants in a large number of people, allowing the detection of small genetic effects that are associated with a trait. Natural variation of genotypes within populations means that any particular sample from the population may not represent the true genotype frequencies within that population. This may lead to the observation of marker-disease associations when no such association exists.\n\nA bootstrap population sub-sampling technique can reduce the influence of allele frequency variation in producing false-positive results for particular samplings of the population. In order to utilise bioinformatics in the service of a serious disease, this sub-sampling method has been applied to the Type 1 Diabetes dataset from the Wellcome Trust Case Control Consortium in order to evaluate its effectiveness.\n\nWhile previous literature on Type 1 Diabetes has identified some DNA variants that are associated with the disease, these variants are not informative for distinguishing between disease cases and controls using genetic information alone (AUC=0.7284). Population sub-sampling filtered out noise from genome-wide association data, and increased the chance of finding useful associative signals. Subsequent filtering based on marker linkage and testing of marker sets of different sizes produced a 5-SNP signature set of markers for Type 1 Diabetes. The group-specific markers used in this set, primarily from the HLA region on chromosome 6, are considerably more informative than previously known associated variants for predicting T1D phenotype from genetic data (AUC=0.8395). Given this predictive quality, the signature set may be useful alone as a screening test, and would be particularly useful in combination with other clinical cofactors for Type 1 Diabetes risk.

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bioRxiv2017

ATLAS: Analysis Tools for Low-depth and Ancient Samples

SummaryPost-mortem damage (PMD) obstructs the proper analysis of ancient DNA samples and can currently only be addressed by removing or down-weighting potentially damaged data. Here we present ATLAS, a suite of methods to accurately genotype and estimate genetic diversity from ancient samples, while accounting for PMD. It works directly from raw BAM files and enables the building of complete and customized pipelines for the analysis of ancient and other low-depth samples in a very user-friendly way. Based on simulations we show that, in the presence of PMD, a dedicated pipeline of ATLAS calls genotypes more accurately than the state-of-the-art pipeline of GATK combined with mapDamage 2.0.\n\nAvailabilityATLAS is an open-source C++ program freely available at https://bitbucket.org/phaentu/atlas.\n\nContactDaniel.Wegmann@unifr.ch\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2017

VaPoR: a high-speed validation approach for structural variation using long-read sequencing technology.

AbstractSummaryAlthough there are numerous algorithms that have been developed to identify structural variation (SVs) in genomic sequences, there is a dearth of approaches that can be used to evaluate their results. The emergence of new sequencing technologies that generate longer sequence reads can, in theory, provide direct evidence for all types of SVs regardless of the length of region through which it spans. However, current efforts to use these data in this manner require the use of large computational resources to assemble these sequences as well as manual inspection of each region. Here, we present VaPoR, a highly efficient algorithm that autonomously validates large SV sets using long read sequencing data. We assess of the performance of VaPoR on both simulated and real SVs with regards to various features including accuracy and sensitivity of breakpoint evaluation and report a high fidelity rate.\n\nAvailabilityhttps://github.com/mills-lab/VaPoR\n\nContactremills@umich.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2017

CRISPRAnalyzeR: Interactive analysis, annotation and documentation of pooled CRISPR screens

Pooled CRISPR/Cas9 screens are a powerful and versatile tool for the systematic investigation of cellular processes in a variety of organisms. Such screens generate large amounts of data that present a new challenge to analyze and interpret. Here, we developed a web application to analyze, document and explore pooled CRISR/Cas9 screens using a unified single workflow. The end-to-end analysis pipeline features eight different hit calling strategies based on state-of-the-art methods, including DESeq2, MAGeCK, edgeR, sgRSEA, Z-Ratio, Mann-Whitney test, ScreenBEAM and BAGEL. Results can be compared with interactive visualizations and data tables. CRISPRAnalyzeR integrates meta-information from 26 external data resources, providing a wide array of options for the annotation and documentation of screens. The application was developed with user experience in mind, requiring no previous knowledge in bioinformatics. All modern operating systems are supported.\n\nAvailability and online documentation: The source code, a pre-configured docker application, sample data and a documentation can be found on our GitHub page (http://www.github.com/boutroslab/CRISPRAnalyzeR). A tutorial video can be found at http://www.crispr-analyzer.org.

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bioRxiv2017

FUMA: Functional mapping and annotation of genetic associations

A main challenge in genome-wide association studies (GWAS) is to prioritize genetic variants and identify potential causal mechanisms of human diseases. Although multiple bioinformatics resources are available for functional annotation and prioritization, a standard, integrative approach is lacking. We developed FUMA: a web-based platform to facilitate functional annotation of GWAS results, prioritization of genes and interactive visualization of annotated results by incorporating information from multiple state-of-the-art biological databases.

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bioRxiv2017

SAVE: A secure cloud-based pipeline for CRISPR pooled screen deconvolution

We present a user-friendly, cloud-based, data analysis pipeline for the deconvolution of pooled screening data. This tool, termed SAVE for Screening Analysis Visual Explorer, serves a dual purpose of extracting, clustering and analyzing raw next generation sequencing files derived from pooled screening experiments while at the same time presenting them in a user-friendly way on a secure web-based platform. Moreover, SAVE serves as a useful web-based analysis pipeline for reanalysis of pooled CRISPR screening datasets. Taken together, the framework described in this study is expected to accelerate development of web-based bioinformatics tool for handling all studies which include next generation sequencing data. SAVE is available at http://save.nrihub.org.

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bioRxiv2017

Granatum: a graphical single-cell RNA-seq analysis pipeline for genomics scientists

BackgroundSingle-cell RNA sequencing (scRNA-Seq) is an increasingly popular platform to study heterogeneity at the single-cell level.\n\nComputational methods to process scRNA-Seq have limited accessibility to bench scientists as they require significant amounts of bioinformatics skills.\n\nResultsWe have developed Granatum, a web-based scRNA-Seq analysis pipeline to make analysis more broadly accessible to researchers. Without a single line of programming code, users can click through the pipeline, setting parameters and visualizing results via the interactive graphical interface Granatum conveniently walks users through various steps of scRNA-Seq analysis. It has a comprehensive list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene filtering, geneexpression normalization, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein-networ interaction visualization, and pseudo-time cell series construction.\n\nConclusionsGranatum enables broad adoption of scRNA-Seq technology by empowering the bench scientists with an easy-to-use graphical interface for scRNA-Seq data analysis. The package is freely available for research use at http://garmiregroup.org/granatum/app

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bioRxiv2017

Evidence-based gene models for structural and functional annotations of the oil palm genome

The advent of rapid and inexpensive DNA sequencing has led to an explosion of data that must be transformed into knowledge about genome organization and function. Gene prediction is customarily the starting point for genome analysis. This paper presents a bioinformatics study of the oil palm genome, including a comparative genomics analysis, database and tools development, and mining of biological data for genes of interest. We annotated 26,087 oil palm genes integrated from two gene-prediction pipelines, Fgenesh++ and Seqping. As case studies, we conducted comprehensive investigations on intronless, resistance and fatty acid biosynthesis genes, and demonstrated that the current gene prediction set is of high quality. 3,672 intronless genes were identified in the oil palm genome, an important resource for evolutionary study. Further scrutiny of the oil palm genes revealed 210 candidate resistance genes involved in pathogen defense. Fatty acids have diverse applications ranging from food to industrial feedstock, and we identified 42 key genes involved in fatty-acid biosynthesis in oil palm mesocarp and kernel. These results provide an important resource for studies on plant genomes and a theoretical foundation for marker-assisted breeding of oil palm and related crops.

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