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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 865 records · Page 48Linked to original sources

bioRxiv2014

Graph-based data integration predicts long-range regulatory interactions across the human genome

Transcriptional regulation of gene expression is one of the main processes that affect cell diversification from a single set of genes. Regulatory proteins often interact with DNA regions located distally from the transcription start sites (TSS) of the genes. We developed a computational method that combines open chromatin and gene expression information for a large number of cell types to identify these distal regulatory elements. Our method builds correlation graphs for publicly available DNase-seq and exon array datasets with matching samples and uses graph-based methods to filter findings supported by multiple datasets and remove indirect interactions. The resulting set of interactions was validated with both anecdotal information of known long-range interactions and unbiased experimental data deduced from Hi-C and CAGE experiments. Our results provide a novel set of high-confidence candidate open chromatin regions involved in gene regulation, often located several Mb away from the TSS of their target gene.

Bioinformatics↗

bioRxiv2014

Accounting for sources of bias and uncertainty in copy number-based statistical deconvolution of heterogeneous tumour samples

Deconvolving heterogeneous tumour samples to identify constituent cell populations with differing copy number profiles using whole genome sequencing data is a challenging problem. Copy number calling algorithms have differential detection rates for different sizes and classes of copy number alterations. This paper describes how uncertainty in classification and differential detection rates can introduce biases in measures of clonal diversity. A simulation strategy is introduced that allows differential detection rates to be adjusted for and this process is shown to minimise bias.

Bioinformatics↗

bioRxiv2014

Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

Bioinformatics↗

bioRxiv2014

Sequence co-evolution gives 3D contacts and structures of protein complexes

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequence databases, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

Bioinformatics↗

bioRxiv2014

Application of Global Transcriptome Data in Gene Ontology Classification and Construction of a Gene Ontology Interaction Network

Gene Ontology (GO) classification of statistically significant over/under expressed genes is a commonly used to interpret transcriptomics data in functional genomic analysis. In this approach, all significant genes contribute equally to the final GO classification regardless of their actual expression levels. However, the original level of gene expression can significantly affect protein production and consequently GO term enrichment, and genes with low expression levels can participate in the final GO enrichment through cumulative effects. In addition, GO terms have regulatory relationships that allow the construction of a regulatory network that incorporates gene expression levels to better study biological mechanisms. In this report, we have used gene expression levels in bacteria to determine GO term enrichments. This approach provided the opportunity to enrich GO terms across the entire transcriptome (instead of a subset of differentially expressed genes). In the second part, we show a dynamically developed enriched interaction network between Biological Process GO terms for any gene samples. This type of network presents regulatory relationships between GO terms and their genes. We then demonstrate the efficiency of these methods using public data from two important bacterial pathogens as models. We also explain how these methods help us understand potential pathogenesis mechanisms employed by these bacteria.

Bioinformatics↗

bioRxiv2014

diCal-IBD: demography-aware inference of identity-by-descent tracts in unrelated individuals

Summary: We present a tool, diCal-IBD, for detecting identity-by-descent (IBD) tracts between pairs of genomic sequences. Our method builds on a recent demographic inference method based on the coalescent with recombination, and is able to incorporate demographic information as a prior. Simulation study shows that diCal-IBD has significantly higher recall and precision than that of existing IBD detection methods, while retaining reasonable accuracy for IBD tracts as small as 0.1 cM.\n\nAvailability: http://sourceforge.net/p/dical-ibd\n\nContact: yss@eecs.berkeley.edu

Bioinformatics↗

bioRxiv2014

qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots

SummaryGenome-wide association studies (GWAS) have identified thousands of human trait-associated single nucleotide polymorphisms. Here, I describe a freely available R package for visualizing GWAS results using Q-Q and manhattan plots. The qqman package enables the flexible creation of manhattan plots, both genome-wide and for single chromosomes, with optional highlighting of SNPs of interest.\n\nAvailabilityqqman is released under the GNU General Public License, and is freely available on the Comprehensive R Archive Network (http://cran.r-project.org/package=qqman). The source code is available on GitHub (https://github.com/stephenturner/qqman).\n\nContactturner@virginia.edu

Bioinformatics↗

bioRxiv2014

A new insight for the screening of potential β-lactamase inhibitors

The {beta}-lactamase produces by Aeromonas hydrophila which enables to hydrolyze and inactivate {beta}- lactam ring of antibiotics. The homology modeling was used to generate the 3-D model of {beta}-lactamase by using known template 3-D structure. The stereochemical quality and torsion angle of 3-D model were validated. Total eleven effective drugs have been selected and targeted the active amino acid residues in {beta}-lactamase. The drugs were derivative of {beta}-lactam ring antibiotics and screening was made by docking. Out of 11 drugs, 3 drugs (Ampicillin, Astreonam and Sultamicillin) were found to be more potent on the basis of robust binding energy between protein-drug interactions. Additionally, homology of {beta}-lactamase of A. hydrophila resembled with other pathogenic bacteria that used for phylogeny analysis. These findings suggest a new insight for better understanding and useful for designing of novel potent drugs.

Bioinformatics↗

bioRxiv2014

RNA-seq gene profiling - a systematic empirical comparison

Accurately quantifying gene expression levels is a key goal of experiments using RNA-sequencing to assay the transcriptome. This typically requires aligning the short reads generated to the genome or transcriptome before quantifying expression of pre-defined sets of genes. Differences in the alignment/quantification tools can have a major effect upon the expression levels found with important consequences for biological interpretation. Here we address two main issues: do different analysis pipelines affect the gene expression levels inferred from RNA-seq data? And, how close are the expression levels inferred to the \"true\" expression levels?\n\nWe evaluate fifty gene profiling pipelines in experimental and simulated data sets with different characteristics (e.g, read length and sequencing depth). In the absence of knowledge of the ground truth in real RNAseq data sets, we used simulated data to assess the differences between the \"true\" expression and those reconstructed by the analysis pipelines. Even though this approach does not take into account all known biases present in RNAseq data, it still allows to estimate the accuracy of the gene expression values inferred by different analysis pipelines. The results show that i) overall there is a high correlation between the expression levels inferred by the best pipelines and the true quantification values; ii) the error in the estimated gene expression values can vary considerably across genes; and iii) a small set of genes have expression estimates with consistently high error (across data sets and methods). Finally, although the mapping software is important, the quantification method makes a greater difference to the results.

Bioinformatics↗

bioRxiv2014

SraTailor: GUI software for visualizing high-throughput sequence read archives

Raw high-throughput sequence data are deposited in public databases as SRAs (Sequence Read Archives) and are publically available to every researcher. However, in order to graphically visualize the sequence data of interest, the corresponding SRAs must be downloaded and converted into BigWig format through complicated command-line processing. This task requires users to possess skill with script languages and sequence data processing, a requirement that prevents a wide range of biologists from exploiting SRAs. To address these challenges, we developed SraTailor, a GUI (Graphical User Interface) software package that automatically converts an SRA into a BigWig-formatted file. Simplicity of use is one of the most notable features of SraTailor: entering an accession number of an SRA and clicking the mouse are the only steps required in order to obtain BigWig-formatted files and to graphically visualize the extents of reads at given loci. SraTailor is also able to make peak calls and files of other formats, and the software also accepts various command-line-like options. Therefore, this software makes SRAs fully exploitable by a wide range of biologists. SraTailor is freely available at http://www.dev.med.kyushu-u.ac.jp/sra_tailor/.

Bioinformatics↗

bioRxiv2014

Automation and Evaluation of the SOWH Test with SOWHAT

The Swofford-Olsen-Waddell-Hillis (SOWH) test is a method to evaluate incongruent phylogenetic topologies. It is used, for example, when an investigator wishes to know if the maximum likelihood tree recovered in their analysis is significantly different than an alternative phylogenetic hypothesis. The SOWH test compares the observed difference in likelihood between the topologies to a null distribution of differences in likelihood generated by parametric resampling. The SOWH test is a well-established and important phylogenetic method, but it can be difficult to implement and its sensitivity to various factors is not well understood. We wrote SOWHAT, a program that automates the SOWH test. In test analyses, we find that variation in parameter estimation as well as the use of a more complex model of parameter estimation have little impact on results, but that results can be inconsistent when an insufficient number of replicates are used to estimate the null distribution. We provide methods of analyzing the sampling as well as a simple stopping criteria for sufficient bootstrap replicates, which increase the overall reliability of the approach. Applications of the SOWH test should include explicit evaluations of sampling adequacy. SOWHAT is available for download from https://github.com/josephryan/SOWHAT.

Bioinformatics↗

bioRxiv2014

Inference of phenotype-defining functional modules of protein families for microbial plant biomass degraders

BackgroundEfficient industrial processes for converting plant lignocellulosic materials into biofuels are a key challenge in global efforts to use alternative energy sources to fossil fuels. Novel cellulolytic enzymes have been discovered from microbial genomes and metagenomes of microbial communities. However, the identification of relevant genes without known homologs, and elucidation of the lignocellulolytic pathways and protein complexes for different microorganisms remain a challenge.\n\nResultsWe describe a new computational method for the targeted discovery of functional modules of plant biomass-degrading protein families based on their co-occurrence patterns across genomes and metagenome datasets, and the strength of association of these modules with the genomes of known degraders. From more than 6.4 million family annotations for 2884 microbial genomes and 332 taxonomic bins from 18 metagenomes, we identified five functional modules that are distinctive for plant biomass degraders, which we call plant biomass degradation modules (PDMs). These modules incorporated protein families involved in the degradation of cellulose, hemicelluloses and pectins, structural components of the cellulosome and additional families with potential functions in plant biomass degradation. The PDMs could be linked to 81 gene clusters in genomes of known lignocellulose degraders, including previously described clusters of lignocellulolytic genes. On average, 70% of the families of each PDM mapped to gene clusters in known degraders, which served as an additional confirmation of their functional relationships. The presence of a PDM in a genome or taxonomic metagenome bin allowed us to predict an organisms ability for plant biomass degradation accurately. For 15 draft genomes of a cow rumen metagenome, we validated by cross-linking with confirmed cellulolytic enzymes that the PDMs identified plant biomass degraders within a complex microbial community.\n\nConclusionsFunctional modules of protein families that realize different aspects of plant cell wall degradation can be inferred from co-occurrence patterns across (meta)genomes with a probabilistic topic model. The PDMs represent a new resource of protein families and candidate genes implicated in microbial plant biomass degradation. They can be used to predict the ability to degrade plant biomass for a genome or taxonomic bin. The method would also be suitable for characterizing other microbial phenotypes.

Bioinformatics↗

bioRxiv2014

Powerful tests for multi-marker association analysis using ensemble learning

Multi-marker approaches are currently gaining a lot of interest in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene and pathway based association tests are increasingly being viewed as useful complements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not consider pairwise and higher-order interactions between genetic variants. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of utilizing only a linear or logistic regression model, we propose the use of ensembles of diverse machine learning algorithms for constructing such association tests. As the true mathematical relationship between a phenotype and any group of genetic and clinical variables is unknown in advance and may be complex, such a strategy gives us a general and flexible framework to approximate this relationship across different sets of SNPs. We show how phenotype prediction obtained from ensemble learning algorithms can be used for constructing tests for the joint association of multiple variants. We first apply our method to simulated datasets to demonstrate its power and correctness. Then, we apply our method to previously studied asthma-related genes in two independent asthma cohorts to conduct association tests.

Bioinformatics↗

bioRxiv2014

Lighter: fast and memory-efficient error correction without counting

Lighter is a fast, memory-efficient tool for correcting sequencing errors. Lighter avoids counting k-mers. Instead, it uses a pair of Bloom filters, one holding a sample of the input k-mers and the other holding k-mers likely to be correct. As long as the sampling fraction is adjusted in inverse proportion to the depth of sequencing, Bloom filter size can be held constant while maintaining near-constant accuracy. Lighter is parallelized, uses no secondary storage, and is both faster and more memory-efficient than competing approaches while achieving comparable accuracy.

Bioinformatics↗

bioRxiv2014

Inferring restrictions in the temporal order of mutations during tumor progression: effects of passengers, evolutionary models, and sampling

Cancer progression is caused by the sequential accumulation of mutations, but not all orders of accumulation of mutations are equally likely. When the fixation of some mutations depends on the presence of previous ones, identifying restrictions in the order of accumulation of mutations can lead to the discovery of therapeutic targets and diagnostic markers. Using simulated data sets, I conducted a comprehensive comparison of the performance of all available methods to identify these restrictions from cross-sectional data. In contrast to previous work, I embedded restrictions within evolutionary models of tumor progression that included passengers (mutations not responsible for the development of cancer, known to be very common). This allowed me to asses the effects of having to filter out passengers, of sampling schemes, and of deviations from order restrictions. Poor choices of method, filtering, and sampling lead to large errors in all performance metrics. Having to filter passengers lead to decreased performance, especially because true restrictions were missed. Overall, the best method for identifying order restrictions were Oncogenetic Trees, a fast and easy to use method that, although unable to recover dependencies of mutations on more than one mutation, showed good performance in most scenarios, superior to Conjunctive Bayesian Networks and Progression Networks. Single cell sampling provided no advantage, but sampling in the final stages of the disease vs. sampling at different stages had severe effects. Evolutionary model and deviations from order restrictions had major, and sometimes counterintuitive, interactions with other factors that affected performance. This paper provides practical recommendations for using these methods with experimental data. Moreover, it shows that it is both possible and necessary to embed assumptions about order restrictions and the nature of driver status within evolutionary models of cancer progression to evaluate the performance of inferential approaches.

Bioinformatics↗

bioRxiv2014

A comparative study of techniques for differential expression analysis on RNA-Seq data

Recent advances in next-generation sequencing technology allow high-throughput cDNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies, in particular for detecting differentially expressed genes between groups. Many software packages have been developed for the identification of differentially expressed genes (DEGs) between treatment groups based on RNA-Seq data. However, there is a lack of consensus on how to approach an optimal study design and choice of suitable software for the analysis. In this comparative study we evaluate the performance of three of the most frequently used software tools: Cufflinks-Cuffdiff2, DESeq and edgeR. A number of important parameters of RNA-Seq technology were taken into consideration, including the number of replicates, sequencing depth, and balanced vs. unbalanced sequencing depth within and between groups. We benchmarked results relative to sets of DEGs identified through either quantitative RT-PCR or microarray. We observed that edgeR performs slightly better than DESeq and Cuffdiff2 in terms of the ability to uncover true positives. Overall, DESeq or taking the intersection of DEGs from two or more tools is recommended if the number of false positives is a major concern in the study. In other circumstances, edgeR is slightly preferable for differential expression analysis at the expense of potentially introducing more false positives.

Bioinformatics↗

bioRxiv2014

Simultaneous estimation of transcript abundances and transcript specific fragment distributions of RNA-Seq data with the Mix2 model

Quantification of RNA transcripts with RNA-Seq is inaccurate due to positional fragmentation bias, which is not represented appropriately by current statistical models of RNA-Seq data. Another, less investigated, source of error is the inaccuracy of transcript start and end annotations.\n\nThis 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 EM algorithm and are tied between similar transcripts. Transcript specific shift and scale parameters allow the Mix2 model to automatically correct inaccurate transcript start and end annotations. Experiments are conducted on synthetic data covering 7 genes of different complexity, 4 types of fragment bias and correct as well as incorrect transcript start and end annotations. Abundance estimates obtained by Cufflinks 2.2.0, PennSeq and the Mix2 model show superior performance of the Mix2 model in the vast majority of test conditions.\n\nThe Mix2 software is available at http://www.lexogen.com/fileadmin/uploads/bioinfo/mix2model.tgz, subject to the enclosed license.\n\nAdditional experimental data are available in the supplement.

Bioinformatics↗

bioRxiv2014

Identification, annotation and visualisation of extreme changes in splicing from RNA-seq experiments with SwitchSeq

In the past years, RNA sequencing has become the method of choice for the study of transcriptome composition. When working with this type of data, several tools exist to quantify differences in splicing across conditions and to address the significance of those changes. However, the number of genes predicted to undergo differential splicing is often high, and further interpretation of the results becomes a challenging task. Here we present SwitchSeq, a novel set of tools designed to help the users in the interpretation of differential splicing events that affect protein coding genes. More specifically, we provide a framework to identify switch events, i.e., cases where, for a given gene, the identity of the most abundant transcript changes across conditions. The identified events are then annotated by incorporating information from several public databases and third-party tools, and are further visualised in an intuitive manner with the independent R package tviz. All the results are displayed in a self-contained HTML document, and are also stored in txt and json format to facilitate the integration with any further downstream analysis tools. Such analysis approach can be used complementarily to Gene Ontology and pathway enrichment analysis, and can also serve as an aid in the validation of predicted changes in mRNA and protein abundance. The latest version of SwitchSeq, including installation instructions and use cases, can be found at https://github.com/mgonzalezporta/SwitchSeq. Additionally, the plot capabilities are provided as an independent R package at https://github.com/mgonzalezporta/tviz.

Bioinformatics↗