SEARCH · Search bioRxiv

Results for “Bioinformatics”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,549 records · Page 86Linked to original sources

bioRxiv2015

Genomic variant calling: Flexible tools and a diagnostic data set

1The accurate identification of low-frequency variants in tumors remains an unsolved problem. To support characterization of the issues in a realistic setting, we have developed software tools and a reference dataset for diagnosing variant calling pipelines. The dataset contains millions of variants at frequencies ranging from 0.05 to 1.0. To generate the dataset, we performed whole-genome sequencing of a mixture of two Corriel cell lines, NA19240 and NA12878, the mothers of YRI (Y) and CEU (C) HapMap trios, respectively. The cells were mixed in three different proportions, 10Y/90C, 50Y/50C and 90Y/10C, in an effort to simulate the heterogeneity found in tumor samples. We sequenced three biological replicates for each mixture, yielding approximately 1.4 billion reads per mixture for an average of 64X coverage. Using the published genotypes as our reference, we evaluate the performance of a general variant calling algorithm, constructed as a demonstration of our flexible toolset, and make comparisons to a standard GATK pipeline. We estimate the overall FDR to be 0.028 and the FNR (when coverage exceeds 20X) to be 0.019 in the 50Y/50C mixture. Interestingly, even with these relatively well studied individuals, we predict over 475,000 new variants, validating in well-behaved coding regions at a rate of 0.97, that were not included in the published genotypes.

Bioinformatics↗

bioRxiv2015

Haplotype synthesis analysis in public reference data reveals functional variants underlying known genome-wide associated susceptibility loci

The functional mechanisms underlying disease association identified by Genome-wide Association Studies remain unknown for susceptibility loci located outside gene coding regions. In addition to the regulation of gene expression, synthesis of effects from multiple surrounding functional variants has been suggested as an explanation of hard-to-interpret associations.\n\nHere, we define filter criteria based on linkage disequilibrium measures and allele frequencies which reflect expected properties of synthesizing variant sets. For eligible candidate sets we search for those haplotypes that are highly correlated with the risk alleles of a genome-wide associated variant.\n\nWe applied our methods to 1,000 Genomes reference data and confirmed Crohns Disease and Type 2 Diabetes susceptibility loci. Of these, a proportion of 32% allowed explanation by three-variant-haplotypes carrying at least two functional variants, as compared to a proportion of 16% for random variants (P = 2.92 {middle dot} 10-6). More importantly, we detected examples of known loci whose association can fully be explained by surrounding missense variants: three missense variants from MUC19 synthesize rs11564258 (L0C105369736/MUC19, intron; Crohns Disease). Next, rs2797685 (PER3, intron; Crohns Disease) is synthesized by a 57 kilobase haplotype defined by five missense variants from PER3 and three missense variants from UTS2. Finally, the association of rs7178572 (HMG20A, intron; Type 2 Diabetes) can be explained by the synthesis of eight haplotypes, each carrying at least one missense variant in either PEAK1, TBC1D2B, CHRNA5 or ADAMTS7.\n\nIn summary, application of our new methods highlights the potential of synthesis analysis to guide functional follow-up investigation of findings from association studies.

Bioinformatics↗

bioRxiv2015

Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next generation sequencing (NGS) data, and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional - omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent works on \"selective advantage\" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications as it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations and progression model inference. We demonstrate PiCnIcs ability to reproduce much of the current knowledge on colorectal cancer progression, as well as to suggest novel experimentally verifiable hypotheses.\n\nSO_SCPLOWTATEMENTC_SCPLOW O_SCPLOWOFC_SCPLOW SO_SCPLOWIGNIFICANCEC_SCPLOW: A causality based new machine learning Pipeline for Cancer Inference (PicNic) is introduced to infer the underlying somatic evolution of ensembles of tumors from next generation sequencing data. PicNic combines techniques for sample stratification, driver selection and identification of fitness-equivalent exclusive alterations to exploit a novel algorithm based on Suppes probabilistic causation. The accuracy and translational significance of the results are studied in details, with an application to colorectal cancer. PicNic pipeline has been made publicly accessible for reproducibility, interoperability and for future enhancements.

Bioinformatics↗

bioRxiv2015

Rawcopy: Improved copy number analysis with Affymetrix arrays

Rawcopy is an R package for processing of Affymetrix CytoScan HD, CytoScan 750k and SNP 6.0 microarray raw intensities (CEL files). It uses data from a large number of reference samples to produce log ratio for total copy number analysis and B-allele frequency for allele-specific copy number and heterozygosity analysis. Rawcopy achieves higher signal-to-noise ratio than commonly used free and proprietary alternatives, leading to improved identification of copy number alterations. In addition, Rawcopy visualises each microarray sample for assessment of technical quality, patient identity and genome-wide absolute copy number states.

Bioinformatics↗

bioRxiv2015

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data

MotivationWe introduce TRONCO (TRanslational ONCOlogy), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract population-level models describing the trends of accumulation of alterations in a cohort of cross-sectional samples, e.g., retrieved from publicly available databases, and individual-level models that reveal the clonal evolutionary history in single cancer patients, when multiple samples, e.g., multiple biopsies or single-cell sequencing data, are available. The resulting models can provide key hints in uncovering the evolutionary trajectories of cancer, especially for precision medicine or personalized therapy.\n\nAvailabilityTRONCO is released under the GPL license, it is hosted in the Software section at http://bimib.disco.unimib.it/ and archived also at bioconductor.org.\n\nContacttronco@disco.unimib.it

Bioinformatics↗

bioRxiv2015

Design of the TRONCO BioConductor Package for TRanslational ONCOlogy

Models of cancer progression provide insights on the order of accumulation of genetic alterations during cancer development. Algorithms to infer such models from the currently available mutational profiles collected from different cancer patiens (cross-sectional data) have been defined in the literature since late 90s. These algorithms differ in the way they extract a graphical model of the events modelling the progression, e.g., somatic mutations or copy-number alterations.\n\nTRONCO is an R package for TRanslational ONcology which provides a serie of functions to assist the user in the analysis of cross-sectional genomic data and, in particular, it implements algorithms that aim to model cancer progression by means of the notion of selective advantage. These algorithms are proved to outperform the current state-of-the-art in the inference of cancer progression models. TRONCO also provides functionalities to load input cross-sectional data, set up the execution of the algorithms, assess the statistical confidence in the results and visualize the models.\n\nAvailability. Freely available at http://www.bioconductor.org/ under GPL license; project hosted at http://bimib.disco.unimib.it/ and https://github.com/BIMIB-DISCo/TRONCO.\n\nContact. tronco@disco.unimib.it

Bioinformatics↗

bioRxiv2015

One Codex: A Sensitive and Accurate Data Platform for Genomic Microbial Identification

High-throughput sequencing (HTS) is increasingly being used for broad applications of microbial characterization, such as microbial ecology, clinical diagnosis, and outbreak epidemiology. However, the analytical task of comparing short sequence reads against the known diversity of microbial life has proved to be computationally challenging. The One Codex data platform was created with the dual goals of analyzing microbial data against the largest possible collection of microbial reference genomes, as well as presenting those results in a format that is consumable by applied end-users. One Codex identifies microbial sequences using a \"k-mer based\" taxonomic classification algorithm through a web-based data platform, using a reference database that currently includes approximately 40,000 bacterial, viral, fungal, and protozoan genomes. In order to evaluate whether this classification method and associated database provided quantitatively different performance for microbial identification, we created a large and diverse evaluation dataset containing 50 million reads from 10,639 genomes, as well as sequences from six organisms novel species not be included in the reference databases of any of the tested classifiers. Quantitative evaluation of several published microbial detection methods shows that One Codex has the highest degree of sensitivity and specificity (AUC = 0.97, compared to 0.82-0.88 for other methods), both when detecting well-characterized species as well as newly sequenced, \"taxonomically novel\" organisms.

Bioinformatics↗

bioRxiv2015

MG7: Configurable and scalable 16S metagenomics data analysis

As part of the Cambrian explosion of omics data, metagenomics brings to the table a specific, defining trait: its social essence. The meta prefix exerts its influence, with multitudes manifesting themselves everywhere; from samples to data analysis, from actors involved to (present and future) applications. Of these dimensions, data analysis is where needs lay further from what current tools provide. Key features are, among others, scalability, reproducibility, data provenance and distribution, process identity and versioning. These are the goals guiding our work in MG7, a 16S metagenomics data analysis system. The basic principle is a new approach to data analysis, where configuration, processes, or data locations are static, type-checked and subject to the standard evolution of a well-maintained software project. Cloud computing, in its Amazon Web Services incarnation, when coupled with these ideas, produces a robust, safely configurable, scalable tool. Processes, data, machine behaviors and their dependencies are expressed using a set of libraries which bring as much as possible checking and validation to the type level, without sacrificing expressiveness. Together they form a toolkit for defining scalable cloud-based workflows composed of stateless computations, with a static reproducible specification of dependencies, behavior and wiring of all steps. The modeling of taxonomy data is done using Bio4j, where the new paradigm of graph databases allows for both a simple expression of taxonomic assignment tasks and the calculation of taxa abundance values considering the hierarchic structure of the taxonomy tree. MG7 includes a new 16S reference database, 16S-DB7, built with a flexible and sustainable update system, and the possibility of project-driven personalization.

Bioinformatics↗

bioRxiv2015

A benchmark of gene expression tissue-specificity metrics

One of the major properties of genes is their expression pattern. Notably, genes are often classified as tissue-specific or housekeeping. This property is of interest to molecular evolution as an explanatory factor of, e.g., evolutionary rate, as well as a functional feature which may in itself evolve. While many different methods of measuring tissue specificity have been proposed and used for such studies, there has been no comparison or benchmarking of these methods to our knowledge, and little justification of their use. In this study we compare nine measures of tissue-specificity. Most methods were established for ESTs and microarrays, and several were later adapted to RNA-seq. We analyze their capacity to distinguish gene categories, their robustness to the choice and number of tissues used, and their capture of evolutionary conservation signal.

Bioinformatics↗

bioRxiv2015

Sphinx: modeling transcriptional heterogeneity in single-cell RNA-Seq

The significance of single-cell transcription resides not only in the cumulative expression strength of the cell population but also in its heterogeneity. We propose a new model that improves the detection of changes in the transcriptional heterogeneity pattern of RNA-Seq data using two heterogeneity parameters: burst proportion and burst magnitude, whose changes are validated using RNA-FISH. Transcriptional co-bursting - governed by distinct mechanisms during myoblast proliferation and differentiation - is described here.

Bioinformatics↗

bioRxiv2015

PHYLUCE is a software package for the analysis of conserved genomic loci

SummaryTargeted enrichment of conserved and ultraconserved genomic elements allows universal collection of phylogenomic data from hundreds of species at multiple time scales (< 5 Ma to > 300 Ma). Prior to downstream inference, data from these types of targeted enrichment studies must undergo pre-processing to assemble contigs from sequence data; identify targeted, enriched loci from the off-target background data; align enriched contigs representing conserved loci to one another; and prepare and manipulate these alignments for subsequent phylogenomic inference. PHYLUCE is an efficient and easy-to-install software package that accomplishes these tasks across hundreds of taxa and thousands of enriched loci.\n\nAvailability and ImplementationPHYLUCE is written for Python 2.7. PHYLUCE is supported on OSX and Linux (RedHat/CentOS) operating systems. PHYLUCE source code is distributed under a BSD-style license from https://www.github.com/fairclothUlab/phyluce/. PHYLUCE is also available as a package (https://binstar.org/fairclothUlab/phyluce) for the Anaconda Python distribution that installs all dependencies, and users can request a PHYLUCE instance on iPlant Atmosphere (tag: phyluce). The software manual and a tutorial are available from http://phyluce.readthedocs.org/en/latest/ and test data are available from doi: 10.6084/m9.figshare.1284521.\n\nContactbrant@fairclothUlab.org\n\nSupplementary informationSupplementary Figure 1.\n\nO_FIG O_LINKSMALLFIG WIDTH=178 HEIGHT=200 SRC=\"FIGDIR/small/027904_figS1.gif\" ALT=\"Figure 1\">\nView larger version (40K):\norg.highwire.dtl.DTLVardef@1f6fb2aorg.highwire.dtl.DTLVardef@1e39afforg.highwire.dtl.DTLVardef@1d51cf1org.highwire.dtl.DTLVardef@5f2eda_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOSupplementary Figure 1.C_FLOATNO PHYLUCE workflow for phylogenomic analyses of data collected from conserved genomic loci using targeted enrichment.\n\nC_FIG

Bioinformatics↗

bioRxiv2015

Indel variant analysis of short-read sequencing data with Scalpel

As the second most common type of variations in the human genome, insertions and deletions (indels) have been linked to many diseases, but indels of more than a few bases are still challenging to discover from short-read sequencing data. Scalpel (http://scalpel.sourceforge.net) is open-source software for reliable indel detection based on the micro-assembly technique. To date, it has been successfully used to discover mutations in novel candidate genes for autism, and is extensively used in other large-scale studies of human diseases. This protocol gives an overview of the algorithm and describes how to use Scalpel to perform highly accurate indel calling from whole genome and exome sequencing data. We provide detailed instructions for an exemplary family-based de novo study, but we also characterize the other two supported modes of operation for single sample and somatic analysis. Indel normalization, visualization, and annotation of the mutations are also illustrated. Using a standard server, indel discovery and characterization in the exonic regions of the example sequencing data can be finished in ~6 hours after read mapping.

Bioinformatics↗

bioRxiv2015

in silico Whole Genome Sequencer & Analyzer (iWGS): a computational pipeline to guide the design and analysis of de novo genome sequencing studies

The availability of genomes across the tree of life is highly biased toward vertebrates, pathogens, human disease models, and organisms with relatively small and simple genomes. Recent progress in genomics has enabled the de novo decoding of the genome of virtually any organism, greatly expanding its potential for understanding the biology and evolution of the full spectrum of biodiversity. The increasing diversity of sequencing technologies, assays, and de novo assembly algorithms have augmented the complexity of de novo genome sequencing projects in non-model organisms. To reduce the costs and challenges in de novo genome sequencing projects and streamline their experimental design and analysis, we developed iWGS (in silico Whole Genome Sequencer and Analyzer), an automated pipeline for guiding the choice of appropriate sequencing strategy and assembly protocols. iWGS seamlessly integrates the four key steps of a de novo genome sequencing project: data generation (through simulation), data quality control, de novo assembly, and assembly evaluation and validation. The last three steps can also be applied to the analysis of real data. iWGS is designed to enable the user to have great flexibility in testing the range of experimental designs available for genome sequencing projects, and supports all major sequencing technologies and popular assembly tools. Three case studies illustrate how iWGS can guide the design of de novo genome sequencing projects and evaluate the performance of a wide variety of user-specified sequencing strategies and assembly protocols on genomes of differing architectures. iWGS, along with a detailed documentation, is freely available at https://github.com/zhouxiaofan1983/iWGS.

Bioinformatics↗

bioRxiv2015

Characterising Complex Enzyme Reaction Data

The relationship between enzyme-catalysed reactions and the Enzyme Commission (EC) number, the widely accepted classification scheme used to characterise enzyme activity, is complex and with the rapid increase in our knowledge of the reactions catalysed by enzymes needs revisiting. We present a manual and computational analysis to investigate this complexity and found that almost one-third of all known EC numbers are linked to more than one reaction in the secondary reaction databases (e.g. KEGG). Although this complexity is often resolved by defining generic, alternative and partial reactions, we have also found individual EC numbers with more than one reaction catalysing different types of bond changes. This analysis adds a new dimension to our understanding of enzyme function and might be useful for the accurate annotation of the function of enzymes and to study the changes in enzyme function during evolution.

Bioinformatics↗

bioRxiv2015

SinaPlot: an enhanced chart for simple and truthful representation of single observations over multiple classes

Recent developments in data driven science, in particular computational biology, have led scientists to integrate data from several sources, over diverse experimental procedures, or databases. This alone poses a major challenge in truthfully visualising data, especially when the amount of data points varies between classes.\n\nTo aid the presentation of datasets with differing sample size we have developed a new type of plot overcoming limitations of current standard visualization charts. Plots like bar charts, violin plots, strip charts or box-and-whiskers plots may provide visual information about mean/median, variance of the data, number of data points or density distribution of data; still, only a combination of these plots may provide all relevant information.\n\nWe have designed a new and simple plot inspired by the strip chart and the violin plot that operates by letting the normalized density of points restrict the jitter along the x-axis. The plot displays the same contour as a violin plot, but resembles a simple strip chart for small number of data points. In this way the plot conveys information of both the number of data points, the density distribution, outliers and data spread in a very simple, comprehensible and condensed format. The package for producing the plots is available for R through the CRAN network (https://cran.r-project.org/web/packages/sinaplot/index.html). In order to aid users without experience in R we also provide access to a web-server accepting excel sheets to produce the plots (http://servers.binf.ku.dk:8890/sinaplot/).

Bioinformatics↗

bioRxiv2015

Resolving Complex Structural Genomic Rearrangements using a Randomized Approach

Complex chromosomal rearrangements consist of structural genomic alterations involving multiple instances of deletions, duplications, inversions, or translocations that co-occur either on the same chromosome or represent different overlapping events on homologous chromosomes. We present SVelter, an algorithm that first identifies regions of the genome suspected to harbor a complex event and then iteratively rearranges the local genome structure, in a randomized fashion, with each structure scored against characteristics of the observed sequencing data. We show that SVelter is able to accurately reconstruct these regions when compared to well-characterized genomes that have been deep sequenced with both short and long read technologies.

Bioinformatics↗

bioRxiv2015

QuicK-mer: A rapid paralog sensitive CNV detection pipeline

QuicK-mer is a unified pipeline for estimating genome copy-number from high-throughput Illumina sequencing data. QuicK-mer utilizes the Jellyfish application to efficiently tabulate counts of predefined sets of k-mers. The program performs GC-normalization using defined control regions and reports paralog-specific estimates of copy-number suitable for downstream analysis. The package is freely available at https://github.com/KiddLab/QuicK-mer

Bioinformatics↗

bioRxiv2015

Investigating the importance of anatomical homology for cross-species phenotype comparisons using semantic similarity.

There is growing use of ontologies for the measurement of cross-species phenotype similarity. Such similarity measurements contribute to diverse applications, such as identifying genetic models for human diseases, transferring knowledge among model organisms, and studying the genetic basis of evolutionary innovations. Two organismal features, whether genes, anatomical parts, or any other inherited feature, are considered to be homologous when they are evolutionarily derived from a single feature in a common ancestor. A classic example is the homology between the paired fins of fishes and vertebrate limbs. Anatomical ontologies that model the structural relations among parts may fail to include some known anatomical homologies unless they are deliberately added as separate axioms. The consequences of neglecting known homologies for applications that rely on such ontologies has not been well studied. Here, we examine how semantic similarity is affected when external homology knowledge is included. We measure phenotypic similarity between orthologous and non-orthologous gene pairs between humans and either mouse or zebrafish, and compare the inclusion of real with faux homology axioms. Semantic similarity was preferentially increased for orthologs when using real homology axioms, but only in the more divergent of the two species comparisons (human to zebrafish, not human to mouse), and the relative increase was less than 1% to non-orthologs. By contrast, inclusion of both real and faux random homology axioms preferentially increased similarities between genes that were initially more dissimilar in the other comparisons. Biologically meaningful increases in semantic similarity were seen for a select subset of gene pairs. Overall, the effect of including homology axioms on cross-species semantic similarity was modest at the levels of divergence examined here, but our results hint that it may be greater for more distant species comparisons.

Bioinformatics↗