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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2015

Distributed Bayesian Networks Reconstruction on the Whole Genome Scale

BackgroundBayesian networks are directed acyclic graphical models widely used to represent the probabilistic relationships between random variables. They have been applied in various biological contexts, including gene regulatory networks and protein-protein interactions inference. Generally, learning Bayesian networks from experimental data is NP-hard, leading to widespread use of heuristic search methods giving suboptimal results. However, in cases when the acyclicity of the graph can be externally ensured, it is possible to find the optimal network in polynomial time. While our previously developed tool BNFinder implements polynomial time algorithm, reconstructing networks with the large amount of experimental data still leads to computations on single CPU growing exceedingly.\n\nResultsIn the present paper we propose parallelized algorithm designed for multi-core and distributed systems and its implementation in the improved version of BNFinder - tool for learning optimal Bayesian networks. The new algorithm has been tested on different simulated and experimental datasets showing that it has much better efficiency of parallelization than the previous version. BNFinder gives comparable results in terms of accuracy with respect to current state-of-the-art inference methods, giving significant advantage in cases when external information such as regulators list or prior edge probability can be introduced.\n\nConclusionsWe show that the new method can be used to reconstruct networks in the size range of thousands of genes making it practically applicable to whole genome datasets of prokaryotic systems and large components of eukaryotic genomes. Our benchmarking results on realistic datasets indicate that the tool should be useful to wide audience of researchers interested in discovering dependencies in their large-scale transcriptomic datasets.

Bioinformatics↗
bioRxiv2015

Large-Scale Search of Transcriptomic Read Sets with Sequence Bloom Trees

Enormous databases of short-read RNA-seq sequencing experiments such as the NIH Sequence Read Archive (SRA) are now available. However, these collections remain difficult to use due to the inability to search for a particular expressed sequence. A natural question is which of these experiments contain sequences that indicate the expression of a particular sequence such as a gene isoform, lncRNA, or uORF. However, at present this is a computationally demanding question at the scale of these databases.\n\nWe introduce an indexing scheme, the Sequence Bloom Tree (SBT), to support sequence-based querying of terabase-scale collections of thousands of short-read sequencing experiments. We apply SBT to the problem of finding conditions under which query transcripts are expressed. Our experiments are conducted on a set of 2652 publicly available RNA-seq experiments contained in the NIH for the breast, blood, and brain tissues, comprising 5 terabytes of sequence. SBTs of this size can be queried for a 1000 nt sequence in 19 minutes using less than 300 MB of RAM, over 100 times faster than standard usage of SRA-BLAST and 119 times faster than STAR. SBTs allow for fast identification of experiments with expressed novel isoforms, even if these isoforms were unknown at the time the SBT was built. We also provide some theoretical guidance about appropriate parameter selection in SBT and propose a sampling-based scheme for potentially scaling SBT to even larger collections of files. While SBT can handle any set of reads, we demonstrate the effectiveness of SBT by searching a large collection of blood, brain, and breast RNA-seq files for all 214,293 known human transcripts to identify tissue-specific transcripts.\n\nThe implementation used in the experiments below is in C++ and is available as open source at http://www.cs.cmu.edu/[~]ckingsf/software/bloomtree.

Bioinformatics↗
bioRxiv2015

SplAdder: Identification, quantification and testing of alternative splicing events from RNA-Seq data

Motivation: Understanding the occurrence and regulation of alternative splicing (AS) is a key task towards explaining the regulatory processes that shape the complex transcriptomes of higher eukaryotes. With the advent of high-throughput sequencing of RNA (RNA-Seq), the diversity of AS transcripts could be measured at an unprecedented depth. Although the catalog of known AS events has grown ever since, novel transcripts are commonly observed when working with less well annotated organisms, in the context of disease, or within large populations. Whereas an identification of complete transcripts is technically challenging and computationally expensive, focusing on single splicing events as a proxy for transcriptome characteristics is fruitful and sufficient for a wide range of analyses.\n\nResults: We present SplAdder, an alternative splicing toolbox, that takes RNA-Seq alignments and an annotation file as input to i) augment the annotation based on RNA-Seq evidence, ii) identify alternative splicing events present in the augmented annotation graph, iii) quantify and confirm these events based on the RNA-Seq data, and iv) test for significant quantitative differences between samples. Thereby, our main focus lies on performance, accuracy and usability.\n\nAvailability: Source code and documentation are available for download at http://github.com/ratschlab/spladder. Example data, introductory information and a small tutorial are accessible via http://bioweb.me/spladder.\n\nContact: andre.kahles@ratschlab.org, gunnar.ratsch@ratschlab.org

Bioinformatics↗
bioRxiv2015

MMR: A Tool for Read Multi-Mapper Resolution

MotivationMapping high throughput sequencing data to a reference genome is an essential step for most analysis pipelines aiming at the computational analysis of genome and transcriptome sequencing data. Breaking ties between equally well mapping locations poses a severe problem not only during the alignment phase, but also has significant impact on the results of downstream analyses. We present the multimapper resolution (MMR) tool that infers optimal mapping locations from the coverage density of other mapped reads.\n\nResultsFiltering alignments with MMR can significantly improve the performance of downstream analyses like transcript quantitation and differential testing. We illustrate that the accuracy (Spear-man correlation) of transcript quantification increases by 17% when using reads of length 51. In addition, MMR decreases the alignment file sizes by more than 50% and this leads to a reduced running time of the quantification tool. Our efficient implementation of the MMR algorithm is easily applicable as a post-processing step to existing alignment files in BAM format. Its complexity scales linearly with the number of alignments and requires no further inputs.\n\nSupplementary MaterialSource code and documentation are available for download at github.com/ratschlab/mmr. Supplementary text and figures, comprehensive testing results and further information can be found at bioweb.me/mmr.\n\nContactakahles@cbio.mskcc.org and raetsch@cbio.mskcc.org

Bioinformatics↗
bioRxiv2015

RiboDiff: Detecting Changes of Translation Efficiency from Ribosome Footprints

MotivationDeep sequencing based ribosome footprint profiling can provide novel insights into the regulatory mechanisms of protein translation. However, the observed ribosome profile is fundamentally confounded by transcriptional activity. In order to decipher principles of translation regulation, tools that can reliably detect changes in translation efficiency in case-control studies are needed.\n\nResultsWe present a statistical framework and analysis tool, RiboDiff, to detect genes with changes in translation efficiency across experimental treatments. RiboDiff uses generalized linear models to estimate the over-dispersion of RNA-Seq and ribosome profiling measurements separately, and performs a statistical test for differential translation efficiency using both mRNA abundance and ribosome occupancy.\n\nAvailabilityRiboDiff webpage http://bioweb.me/ribodiff. Source code including scripts for preprocessing the FASTQ data are available at http://github.com/ratschlab/ribodiff.\n\nContactzhongy@cbio.mskcc.org and Gunnar.Ratsch@ratschlab.org.

Bioinformatics↗
bioRxiv2015

Index-based map-to-sequence alignment in large eukaryotic genomes

Resolution of complex repeat structures and rearrangements in the assembly and analysis of large eukaryotic genomes is often aided by a combination of high-throughput sequencing and mapping technologies (e.g. optical restriction mapping). In particular, mapping technologies can generate sparse maps of large DNA fragments (150 kbp-2 Mbp) and thus provide a unique source of information for disambiguating complex rearrangements in cancer genomes. Despite their utility, combining high-throughput sequencing and mapping technologies has been challenging due to the lack of efficient and freely available software for robustly aligning maps to sequences. Here we introduce two new map-to-sequence alignment algorithms that efficiently and accurately align high-throughput mapping datasets to large, eukaryotic genomes while accounting for high error rates. In order to do so, these methods (OPTIMA for glocal and OPTIMA-Overlap for overlap alignment) exploit the ability to create efficient data structures that index continuous-valued mapping data while accounting for errors. We also introduce an approach for evaluating the significance of alignments that avoids expensive permutation-based tests while being agnostic to technology-dependent error rates. Our benchmarking results suggest that OPTIMA and OPTIMA-Overlap outperform state-of-the-art approaches in sensitivity (1.6-2x improvement) while simultaneously being more efficient (170-200%) and precise in their alignments (99% precision). These advantages are independent of the quality of the data, suggesting that our indexing approach and statistical evaluation are robust and provide improved sensitivity while guaranteeing high precision.

Bioinformatics↗
bioRxiv2015

Un-complicating protein complex prediction.

Identification of protein complexes from proteomic experiments is crucial to understand not only their function but also the principles of cellular organization. Advances in experimental techniques have enabled the construction of large-scale protein-protein interaction networks, and computational methods have been developed to analyze high-throughput data. In most cases several parameters are introduced that have to be trained before application. But how do we select the parameter values when there are no training data available? How many data do we need to properly train a method. How is the performance of a method affected when we incorrectly select the parameter values? The above questions, although important to determine the applicability of a method, are most of the time over-looked. We highlight the importance of such an analysis by investigating how limited knowledge, in the form of incomplete training data, affects the performance of parametric protein-complex prediction algorithms. Furthermore, we develop a simple non-parametric method that does not rely on the existence of training data and we compare it with the parametric alternatives. Using datasets from yeast and fly we demonstrate that parametric methods trained with limited data provide sub-optimal predictions, while our non-parametric method performs better or is on par with the parametric alternatives. Overall, our analysis questions, at least for the specific problem, whether parametric methods provide significantly better results than non-parametric ones to justify the additional effort for applying them.

Bioinformatics↗
bioRxiv2015

Whole Genome Regulatory Variant Evaluation for Transcription Factor Binding

The majority of disease-associated variants identified in genome-wide association studies (GWAS) reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of GWAS studies. We present GERV (Generative Evaluation of Regulatory Variants), a novel computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence determinants of transcription factor binding than a motif-based approach alone, including both a transcription factors canonical motif as well as associated co-factor motifs. We show that GERV outperforms existing methods in predicting SNPs associated with allele-specific binding. GERV correctly predicts a validated causal variant among linked SNPs, and prioritizes the variants previously reported to modulate the binding of FOXA1 in breast cancer cell lines. Thus, GERV provides a powerful approach for functionally an-notating and prioritizing causal variants for experimental follow-up analysis. The implementation of GERV and related data are available at http://gerv.csail.mit.edu/

Bioinformatics↗
bioRxiv2015

Isoform-level Ribosome Occupancy Estimation Guided by Transcript Abundance with Ribomap

Ribosome profiling is a recently developed high-throughput sequencing technique that captures approximately 30 bp long ribosome-protected mRNA fragments during translation. Because of alternative splicing and repetitive sequences, a ribosome-protected read may map to many places in the transcriptome, leading to discarded or arbitrary mappings when standard approaches are used. We present a technique and software that addresses this problem by assigning reads to potential origins proportional to estimated transcript abundance. This yields a more accurate estimate of ribosome profiles compared with a na{iota}ve mapping. Ribomap is available as open source at http://www.cs.cmu.edu/[~]ckingsf/software/ribomap.

Bioinformatics↗
bioRxiv2015

An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types

Identification of altered pathways that are clinically relevant across human cancers is a key challenge in cancer genomics. We developed a network-based algorithm to integrate somatic mutation data with gene networks and pathways, in order to identify pathways altered by somatic mutations across cancers. We applied our approach to The Cancer Genome Atlas (TCGA) dataset of somatic mutations in 4,790 cancer patients with 19 different types of malignancies. Our analysis identified cancer-type-specific altered pathways enriched with known cancer-relevant genes and drug targets. Consensus clustering using gene expression datasets that included 4,870 patients from TCGA and multiple independent cohorts confirmed that the altered pathways could be used to stratify patients into subgroups with significantly different clinical outcomes. Of particular significance, certain patient subpopulations with poor prognosis were identified because they had specific altered pathways for which there are available targeted therapies. These findings could be used to tailor and intensify therapy in these patients, for whom current therapy is suboptimal.

Bioinformatics↗
bioRxiv2015

Phylogenetic tree inference from local gene content

BackgroundComplete genome sequences provide many new characters suitable for studying phylogenetic relationships. The limitations of the single sequence-based phylogenetic reconstruction prompted the efforts to build trees based on genome-wide properties, such as the fraction of shared orthologous genes or conservation of adjoining gene pairs. Gene content-based phylogenies, however, have their own biases: most notably, differential losses and horizontal transfers of genes interfere with phylogenetic signal, each in their own way, and special measures need to be taken to eliminate these types of noise.\n\nResultsWe expand the repertoire of genome-wide traits available for phylogeny building, by developing a practical approach for measuring local gene conservation in two genomes. We counted the number of orthologous genes shared by chromosomal neighborhoods (\"bins\"), and built the phylogeny of 63 prokaryotic genomes on this basis. The tree correctly resolved all well-established clades, and also suggested the monophily of firmicutes, which tend to be split in other genome-based trees.\n\nConclusionsOur measure of local gene order conservation extracts strong phylogenetic signal. This new measure appears to be substantially resistant to the observed instances of gene loss and horizontal transfer, two evolutionary forces which can cause systematic biases in the genome-based phylogenies.

Bioinformatics↗
bioRxiv2015

MEGSA: A powerful and flexible framework for analyzing mutual exclusivity of tumor mutations

The central challenge in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when patients carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, pathway-guided searches or for expanding established small MEGS. We applied our method to the whole exome sequencing data for fourteen cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a novel MEGS with five genes (FLT3, IDH2, NRAS, KIT and TP53) and a MEGS (NPM1, TP53 and RUX1)s whose mutation status was strongly associated with survival (P=6.7x10-4). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes (ARID1A, AKT1, MED23 and TBL1XR1), providing support for their role as cancer drivers.

Bioinformatics↗
bioRxiv2015

Choosing subsamples for sequencing studies by minimizing the average distance to the closest leaf

AO_SCPCAPBSTRACTC_SCPCAPImputation of genotypes in a study sample can make use of sequenced or densely genotyped external reference panels consisting of individuals that are not from the study sample. It can also employ internal reference panels, incorporating a subset of individuals from the study sample itself. Internal panels offer an advantage over external panels, as they can reduce imputation errors arising from genetic dissimilarity between a population of interest and a second, distinct population from which the external reference panel has been constructed. As the cost of next-generation sequencing decreases, internal reference panel selection is becoming increasingly feasible. However, it is not clear how best to select individuals to include in such panels. We introduce a new method for selecting an internal reference panel--minimizing the average distance to the closest leaf (ADCL)--and compare its performance relative to an earlier algorithm: maximizing phylogenetic diversity (PD). Employing both simulated data and sequences from the 1000 Genomes Project, we show that ADCL provides a significant improvement in imputation accuracy, especially for imputation of sites with low-frequency alleles. This improvement in imputation accuracy is robust to changes in reference panel size, marker density, and length of the imputation target region.

Bioinformatics↗
bioRxiv2015

An evaluation of the accuracy and speed of metagenome analysis tools

Metagenome studies are becoming increasingly widespread, yielding important insights into microbial communities covering diverse environments from terrestrial and aquatic ecosystems to human skin and gut. With the advent of high-throughput sequencing platforms, the use of large scale shotgun sequencing approaches is now commonplace. However, a thorough independent benchmark comparing state-of-the-art metagenome analysis tools is lacking. Here we present a benchmark where the most widely used tools are tested on complex, realistic data sets. Our results clearly show that the most widely used tools are not necessarily the most accurate, that the most accurate tool is not necessarily the most time consuming, and that there is a high degree of variability between available tools. These findings are important as the conclusions of any metagenomics study are affected by errors in the predicted community composition and functional capacity. Data sets and results are freely available from http://www.ucbioinformatics.org/metabenchmark.html

Bioinformatics↗
bioRxiv2015

CIDANE: Comprehensive isoform discovery and abundance estimation

We present CIDANE, a novel framework for genome-based transcript reconstruction and quantification from RNA-seq reads. CIDANE assembles transcripts with significantly higher sensitivity and precision than existing tools, while competing in speed with the fastest methods. In addition to reconstructing transcripts ab initio, the algorithm also allows to make use of the growing annotation of known splice sites, transcription start and end sites, or full-length transcripts, which are available for most model organisms. CIDANE supports the integrated analysis of RNA-seq and additional gene-boundary data and recovers splice junctions that are invisible to other methods. CIDANE is available at http://ccb.jhu.edu/software/cidane/.

Bioinformatics↗
bioRxiv2015

Interrogating conserved elements of diseases using Boolean combinations of orthologous phenotypes

Conserved genetic programs often predate the homologous structures and phenotypes to which they give rise; eyes, for example, have evolved several dozen times, but their development seems to involve a common set of conserved genes. Recently, the concept of orthologous phenotypes (or phenologs) offered a quantitative way to describe this property. Phenologs are phenotypes or diseases from separate species who share an unexpectedly large set of their associated gene orthologs. It has been shown that the phenotype pairs which make up a phenolog are mutually predictive in terms of the genes involved. Recently, we demonstrated the ranking of gene-phenotype association predictions using multiple phenologs from an array of species. In this work, we demonstrate a computational method which provides a more targeted view of the conserved pathways which give rise to diseases. Our approach involves the generation of synthetic pseudophenotypes made up of Boolean combinations (union, intersection, and difference) of the gene sets for phenotypes from our database. We search for diseases that overlap significantly with these Boolean phenotypes, and find a number of highly predictive combinations. While set unions produce less specific predictions (as expected), intersection and difference-based combinations appear to offer insights into extremely specific aspects of target diseases. For example, breast cancer is predicted by zebrafish methylmercury response minus metal ion response, with predictions MT-COI, JUN, SOD2, GADD45B, and BAX all involved in the pro-apoptotic response to reactive oxygen species, thought to be a key player in cancer. We also demonstrate predictions from Arabidopsis Boolean phenotypes for increased brown adipose tissue in mouse (salt stress responses intersection with sucrose stimulus response); and for human myopathy (red light response minus water deprivation response). We demonstrate the ranking of predictions for human holoprosencephaly from the set intersections between each pair of a variety of closely-related zebrafish phenotypes. Our results suggest that Boolean phenolog combinations may provide a more informed insight into the conserved pathways underlying diseases than either regular phenologs or the naive Bayes approach.

Bioinformatics↗
bioRxiv2015

Bayesian Modeling of Epigenetic Variation in Multiple Human Cell Types

With massive amount of sequencing data generated for many epigenetic features in a variety of cell and tissue types, the chief challenges are to build effective and quantitative models explaining how the dynamics in multiple epigenomes lead to differential gene expression and diverse phenotypes. We developed a unified Bayesian framework for jointly annotating multiple epigenomes and detecting differential regulation among multiple tissues and cell types over regions of varying sizes. Our method, called IDEAS (integrative and discriminative epigenome annotation system), achieves superior power and accuracy over existing methods by modeling both position and cell type specific regulatory activities. Using 84 ENCODE epigenetic data sets in 6 cell types, we identified epigenetic variation of different sizes that are strongly associated with differential gene expression. The detected regions are significantly enriched in genetic variants associated with complex phenotypes. Our results yielded much stronger enrichment scores than achievable by existing approaches, and the enriched phenotypes are highly relevant to the corresponding cell types. IDEAS is a powerful statistical tool for integrative annotation of regulatory elements and detection of multivariate epigenetic variation in many tissues and cell types, which could be of important utility in elucidating the interplay between genetic variants, gene regulation and diseases.\n\nThe states generated by IDEAS can be visualized or downloaded from the \"Regulation\" section of http://main.genome-browser.bx.psu.edu/

Bioinformatics↗