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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 199 records · Page 11Linked to original sources
bioRxiv2015

Bio4j: a high-performance cloud-enabled graph-based data platform

BackgroundNext Generation Sequencing and other high-throughput technologies have brought a revolution to the bioinformatics landscape, by offering sheer amounts of data about previously unaccessible domains in a cheap and scalable way. However, fast, reproducible, and cost-effective data analysis at such scale remains elusive. A key need for achieving it is being able to access and query the vast amount of publicly available data, specially so in the case of knowledge-intensive, semantically rich data: incredibly valuable information about proteins and their functions, genes, pathways, or all sort of biological knowledge encoded in ontologies remains scattered, semantically and physically fragmented.\n\nMethods and ResultsGuided by this, we have designed and developed Bio4j. It aims to offer a platform for the integration of semantically rich biological data using typed graph models. We have modeled and integrated most publicly available data linked with proteins into a set of interdependent graphs. Data querying is possible through a data model aware Domain Specific Language implemented in Java, letting the user write typed graph traversals over the integrated data. A ready to use cloud-based data distribution, based on the Titan graph database engine is provided; generic data import code can also be used for in-house deployment.\n\nConclusionBio4j represents a unique resource for the current Bioinformatician, providing at once a solution for several key problems: data integration; expressive, high performance data access; and a cost-effective scalable cloud deployment model.

Bioinformatics↗
bioRxiv2015

GenOO: A Modern Perl Framework for High Throughput Sequencing analysis

BackgroundHigh throughput sequencing (HTS) has become one of the primary experimental tools used to extract genomic information from biological samples. Bioinformatics tools are continuously being developed for the analysis of HTS data. Beyond some well-defined core analyses, such as quality control or genomic alignment, the consistent development of custom tools and the representation of sequencing data in organized computational structures and entities remains a challenging effort for bioinformaticians.\n\nResultsIn this work, we present GenOO [jee-noo], an open-source; object-oriented (OO) Perl framework specifically developed for the design and implementation of HTS analysis tools. GenOO models biological entities such as genes and transcripts as Perl objects, and includes relevant modules, attributes and methods that allow for the manipulation of high throughput sequencing data. GenOO integrates these elements in a simple and transparent way which allows for the creation of complex analysis pipelines minimizing the overhead for the researcher. GenOO has been designed with flexibility in mind, and has an easily extendable modular structure with minimal requirements for external tools and libraries. As an example of the frameworks capabilities and usability, we present a short and simple walkthrough of a custom use case in HTS analysis.\n\nConclusionsGenOO is a tool of high software quality which can be efficiently used for advanced HTS analyses. It has been used to develop several custom analysis tools, leading to a number of published works. Using GenOO as a core development module can greatly benefit users, by reducing the overhead and complexity of managing HTS data and biological entities at hand.

Bioinformatics↗
bioRxiv2015

Roary: Rapid large-scale prokaryote pan genome analysis

SummaryA typical prokaryote population sequencing study can now consist of hundreds or thousands of isolates. Interrogating these datasets can provide detailed insights into the genetic structure of of prokaryotic genomes. We introduce Roary, a tool that rapidly builds large-scale pan genomes, identifying the core and dispensable accessory genes. Roary makes construction of the pan genome of thousands of prokaryote samples possible on a standard desktop without compromising on the accuracy of results. Using a single CPU Roary can produce a pan genome consisting of 1000 isolates in 4.5 hours using 13 GB of RAM, with further speedups possible using multiple processors.\n\nAvailability and implementationRoary is implemented in Perl and is freely available under an open source GPLv3 license from http://sanger-pathogens.github.io/Roary\n\nContactroary@sanger.ac.uk\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics↗
bioRxiv2015

Improved Algorithms for Finding Edit Distance Based Motifs

Motif search is an important step in extracting meaningful patterns from biological data. Since the general problem of motif search is intractable, there is a pressing need to develop efficient exact and approximation algorithms to solve this problem. We design novel algorithms for solving the Edit-distance-based Motif Search (EMS) problem: given two integers l, d and n biological strings, find all strings of length l that appear in each input strings with at most d substitutions, insertions and deletions. These algorithms have been evaluated on several challenging instances. Our algorithm solves a moderately hard instance (11, 3) in a couple of minutes and the next difficult instance (14, 3) in a couple of hours whereas the best previously known algorithm, EMS1, solves (11, 3) in a few hours and does not solve (13, 4) even after 3 days. This significant improvement is due to a novel and provably efficient neighborhood generation technique introduced in this paper. This efficient approach can be used in other edit distance based applications in Bioinformatics, such as k-spectrum based sequence error correction algorithms. We also use a trie based data structure to efficiently store the candidate motifs in the neighbourhood and to output the motifs in a sorted order.

Bioinformatics↗
bioRxiv2015

Pyvolve: a flexible Python module for simulating sequences along phylogenies

We introduce Pyvolve, a flexible Python module for simulating genetic data along a phylogeny according to continuous-time Markov models of sequence evolution. Pyvolve is easily incorporated into Python bioinformatics pipelines, and it can simulate sequences according most standard models of nucleotide, amino-acid, and codon sequence evolution. All model parameters are fully customizable. Users can additionally specify custom evolutionary models, with custom rate matrices and/or states to evolve. This flexibility makes Pyvolve a convenient framework not only for simulating sequences under a wide variety of conditions, but also for developing and testing new evolutionary models. Moreover, Pyvolve includes several novel sequence simulation features, including a new rate matrix scaling algorithm and branch-length perturbations. Pyvolve is an open-source project freely available, along with a detailed user-manual and example scripts, under a FreeBSD license from http://github.com/sjspielman/pyvolve.

Bioinformatics↗
bioRxiv2015

Cpipe: a shared variant detection pipeline designed for diagnostic settings

The benefits of implementing high throughput sequencing in the clinic are quickly becoming apparent. However, few freely available bioinformatics pipelines have been built from the ground up with clinical genomics in mind. Here we present Cpipe, a pipeline designed specifically for clinical genetic disease diagnostics. Cpipe was developed by the Melbourne Genomics Health Alliance, an Australian initiative to promote common approaches to genomics across healthcare institutions. As such, Cpipe has been designed to provide fast, effective and reproducible analysis, while also being highly flexible and customisable to meet the individual needs of diverse clinical settings. Cpipe is being shared with the clinical sequencing community as an open source project and is available at http://cpipeline.org.

Bioinformatics↗
bioRxiv2015

SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis

SummarySPARTA is a reference-based bacterial RNA-seq analysis workflow application for single-end Illumina reads. SPARTA is turnkey software that simplifies the process of analyzing RNA-seq data sets, making bacterial RNA-seq analysis a routine process that can be undertaken on a personal computer or in the classroom. The easy-to-install, complete workflow processes whole transcriptome shotgun sequencing data files by trimming reads and removing adapters, mapping reads to a reference, counting gene features, calculating differential gene expression, and, importantly, checking for potential batch effects within the data set. SPARTA outputs quality analysis reports, gene feature counts and differential gene expression tables and scatterplots. The workflow is implemented in Python for file management and sequential execution of each analysis step and is available for Mac OS X, Microsoft Windows, and Linux. To promote the use of SPARTA as a teaching platform, a web-based tutorial is available explaining how RNA-seq data are processed and analyzed by the software.\n\nAvailability and ImplementationTutorial and workflow can be found at sparta.readthedocs.org. Teaching materials are located at sparta-teaching.readthedocs.org. Source code can be downloaded at www.github.com/abramovitchMSU/, implemented in Python and supported on Mac OS X, Linux, and MS Windows.\n\nContactRobert B. Abramovitch (abramov5@msu.edu)\n\nSupplemental InformationSupplementary data are available at Bioinformatics online.

Bioinformatics↗
bioRxiv2015

Automated and accurate estimation of gene family abundance from shotgun metagenomes

Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohns disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of disease.\n\nAuthor SummaryMicrobial communities perform a wide variety of functions, from marine photosynthesis to aiding digestion in the human gut. Shotgun \"metagenomic\" sequencing can be used to sample millions of short DNA sequences from such communities directly, without needing to first culture its constituents in the laboratory. Using these data, researchers can survey which functions are encoded by mapping these short sequences to known protein families and pathways. Several tools for this annotation already exist. But, annotation is a multi-step process that includes identification of genes in a metagenome and determination of the type of protein each gene encodes. We currently know little about how different choices of parameters during annotation influences the final results. In this work, we systematically test how several key decisions affect the accuracy and speed of annotation, and based on these results, develop new software for annotation, which we named ShotMAP. We then use ShotMAP to functionally characterize marine communities and gut communities in a clinical cohort of inflammatory bowel disease. We find several functions are differentially represented in the gut microbiome of Crohns disease patients, which could be candidates for biomarkers and could also offer insight into the pathophysiology of Crohns. ShotMAP is freely available (https://github.com/sharpton/shotmap).

Bioinformatics↗
bioRxiv2015

Accelerating Protein Structure Prediction using Particle Swarm Optimization on GPU

Protein tertiary structure prediction (PSP) is one of the most challenging problems in bioinformatics. Different methods have been introduced to solve this problem so far, but PSP is computationally intensive and belongs to the NP-hard class. One of the best solutions to accelerate PSP is the use of a massively parallel processing architecture, such graphical processing unit (GPU), which is used to parallelize computational algorithms. In this paper, we have proposed a parallel architecture to accelerate PSP. A bio-inspired method, particle swarm optimization (PSO) has been used as the optimization method to solve PSP. We have also performed a comprehensive study on implementing different topologies of PSO on GPU to consider the acceleration rate. Our solution belongs to ab-initio category which is based on the dihedral angles and calculates the energy-levels to predict the tertiary structure. Indeed, we have studied the search space of a protein to find the best pair of angles that gives the minimum free energy. A profile-level knowledge-based force field based on PSI-BLAST multiple sequence alignment has been applied as a fitness function to calculate the energy values. Different topologies and variations of PSO are considered here and the experimental results show that the speedup gain using GPU is about 34 times faster than CPU implementation of the algorithm with an acceptable precision. The energy values of predicted structures confirm the robustness of the algorithm.

Bioinformatics↗
bioRxiv2015

Comparing Variant Call Files for Performance Benchmarking of Next-Generation Sequencing Variant Calling Pipelines

SummaryTo evaluate and compare the performance of variant calling methods and their confidence scores, comparisons between a test call set and a \"gold standard\" need to be carried out. Unfortunately, these comparisons are not straightforward with the current Variant Call Files (VCF), which are the standard output of most variant calling algorithms for high-throughput sequencing data. Comparisons of VCFs are often confounded by the different representations of indels, MNPs, and combinations thereof with SNVs in complex regions of the genome, resulting in misleading results. A variant caller is inherently a classification method designed to score putative variants with confidence scores that could permit controlling the rate of false positives (FP) or false negatives (FN) for a given application. Receiver operator curves (ROC) and the area under the ROC (AUC) are efficient metrics to evaluate a test call set versus a gold standard. However, in the case of VCF data this also requires a special accounting to deal with discrepant representations. We developed a novel algorithm for comparing variant call sets that deals with complex call representation discrepancies and through a dynamic programing method that minimizes false positives and negatives globally across the entire call sets for accurate performance evaluation of VCFs.\n\nAvailabilityRTG Tools is implemented as a multithreaded Java application and source code is available under BSD license at: https://github.com/RealTimeGenomics/rtg-tools\n\nContactlen@realtimegenomics.com\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics↗
bioRxiv2015

Correcting bias from stochastic insert size in read pair data — applications to structural variation detection and genome assembly

Insert size distributions from paired read protocols are used for inference in bioinformatic applications such as genome assembly and structural variation detection. However, many of the models that are being used are subject to bias. This bias arises when we assume that all insert sizes within a distribution are equally likely to be observed, when in fact, size matters. These systematic errors exist in popular software even when the assumptions made about data are true. We have previously shown that bias occurs for scaffolders in genome assembly. Here, we generalize the theory and demonstrate that it is applicable in other contexts. We provide examples of bias in state-of the-art software and improve them using our model. One key application of our theory is structural variation detection using read pairs. We show that an incorrect null-hypothesis is commonly used in popular tools and can be corrected using our theory. Furthermore, we approximate the smallest size of indels that are possible to discover given an insert size distribution. Two other applications are inference of insert size distribution on \emph{de novo} genome assemblies and error correction of genome assemblies using mated reads. Our theory is implemented in a tool called GetDistr (\url{https://github.com/ksahlin/GetDistr}).

Bioinformatics↗
bioRxiv2015

Manta: Rapid detection of structural variants and indels for clinical sequencing applications

SummaryWe describe Manta, a method to discover structural variants and indels from next generation sequencing data. Manta is optimized for rapid clinical analysis, calling structural variants, medium-sized indels and large insertions on standard compute hardware in less than a tenth of the time that comparable methods require to identify only subsets of these variant types: for example NA12878 at 50x genomic coverage is analyzed in less than 20 minutes. Manta can discover and score variants based on supporting paired and split-read evidence, with scoring models optimized for germline analysis of diploid individuals and somatic analysis of tumor-normal sample pairs. Call quality is similar to or better than comparable methods, as determined by pedigree consistency of germline calls and comparison of somatic calls to COSMIC database variants. Manta consistently assembles a higher fraction of its calls to basepair resolution, allowing for improved downstream annotation and analysis of clinical significance. We provide Manta as a community resource to facilitate practical and routine structural variant analysis in clinical and research sequencing scenarios.\n\nAvailabilityManta source code and Linux binaries are available from http://github.com/sequencing/manta.\n\nContactcsaunders@illumina.com\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics↗
bioRxiv2015

Statistical Inference of a Convergent Antibody Repertoire Response to Influenza Vaccine

BackgroundVaccines dramatically affect an individuals adaptive immune system, and thus provide an excellent means to study human immunity. Upon vaccination, the B cells that express antibodies (Abs) that happen to bind the vaccine are stimulated to proliferate and undergo mutagenesis at their Ab locus. This process may alter the composition of B cell lineages within an individual, which are known collectively as the antibody repertoire (AbR). Antibodies are also highly expressed in whole blood, potentially enabling unbiased RNA sequencing technologies to query this diversity. Less is known about the diversity of AbR responses across individuals to a given vaccine and if individuals tend to yield a similar response to the same antigenic stimulus.\n\nMethodsHere we implement a bioinformatic pipeline that extracts the AbR information from a time-series RNA-seq dataset of 5 patients who were administered a seasonal trivalent influenza vaccine (TIV). We harness the detailed time-series nature of this dataset and use methods based in functional data analysis (FDA) to identify the B cell lineages that respond to the vaccine. We then design and implement rigorous statistical tests in order to ask whether or not these patients exhibit a convergent AbR response to the same TIV.\n\nResultsWe find that high-resolution time-series data can be used to help identify the Ab lineages that respond to an antigenic stimulus, and that this response can exhibit a convergent nature across patients inoculated with the same vaccine. However, correlations in AbR diversity among individuals prior to inoculation can confound inference of a convergent signal unless it is taken into account.\n\nConclusionsWe developed a framework to identify the elements of an AbR that respond to an antigen. This information could be used to understand the diversity of different immune responses in different individuals, as well as to gauge the effectiveness of the immune response to a given stimulus within an individual. We also present a framework for testing a convergent hypothesis between AbRs; a hypothesis that is more difficult to test than previously appreciated. Our discovery of a convergent signal suggests that similar epitopes do select for antibodies with similar sequence characteristics.

Bioinformatics↗
bioRxiv2015

Robust and Stable Gene Selection via Maximum-Minimum Correntropy Criterion

One of the central challenges in cancer research is identifying significant genes among thousands of others on a microarray. Since preventing outbreak and progression of cancer is the ultimate goal in bioinformatics and computational biology, detection of genes that are most involved is vital and crucial. In this article, we propose a Maximum-Minimum Correntropy Criterion (MMCC) approach for selection of biologically meaningful genes from microarray data sets which is stable, fast and robust against diverse noise and outliers and competitively accurate in comparison with other algorithms. Moreover, via an evolutionary optimization process, the optimal number of features for each data set is determined. Through broad experimental evaluation, MMCC is proved to be significantly better compared to other well-known gene selection algorithms for 25 commonly used microarray data sets. Surprisingly, high accuracy in classification by Support Vector Machine (SVM) is achieved by less than10 genes selected by MMCC in all of the cases.

Bioinformatics↗
bioRxiv2015

Combining Dependent P-values with an Empirical Adaptation of Brown’s Method

Motivation: Combining P-values from multiple statistical tests is a common exercise in bioinformatics. However, this procedure is non-trivial for dependent P-values. Here we discuss an empirical adaptation of Browns Method (an extension of Fishers Method) for combining dependent P-values which is appropriate for the correlated data sets found in high-throughput biological experiments.\n\nResults: We show that Fishers Method is biased when used on dependent sets of P-values with both simulated data and gene expression data from The Cancer Genome Atlas (TCGA). When applied on the same data sets, the Empirical Browns Method provides a better null distribution and a more conservative result.\n\nAvailability: The Empirical Browns Method is available in Python, R, and MATLAB and can be obtained from https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.1

Bioinformatics↗
bioRxiv2015

DisGeNET-RDF: harnessing the innovative power of the Semantic Web to explore the genetic basis of diseases

MotivationDisGeNET-RDF makes available knowledge on the genetic basis of human diseases in the Semantic Web (SW). Gene-disease associations (GDAs) and their provenance metadata are published as human-readable and machine-processable web resources. The information on GDAs included in DisGeNET-RDF is interlinked to other biomedical databases to support the development of bioinformatics approaches for translational research through evidence-based exploitation of a rich and fully interconnected Linked Open Data (LOD).\n\nAvailabilityhttp://rdf.disgenet.org/\n\nContactsupport@disgenet.org

Bioinformatics↗
bioRxiv2015

AGOUTI: improving genome assembly and annotation using transcriptome data

SummaryCurrent genome assemblies consist of thousands of contigs. These incomplete and fragmented assemblies lead to errors in gene identification, such that single genes spread across multiple contigs are annotated as separate gene models. We present AGOUTI (Annotated Genome Optimization Using Transcriptome Information), a tool that uses RNA-seq data to simultaneously combine contigs into scaffolds and fragmented gene models into single models. We show that AGOUTI improves both the contiguity of genome assemblies and the accuracy of gene annotation, providing updated versions of each as output.\n\nAvailabilityThe software is implemented in python and is available from github.com/svm-zhang/AGOUTI.\n\nContactsimozhan@indiana.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics↗
bioRxiv2015

A framework for collaborative computational research

Analysis of high troughput biological data often involves the use of many software packages and in-house written code. For each analysis step there are multiple options of software tools available, each with its own capabilities, limitations and assumptions on the input and output data. The development of bioinformatics pipelines involves a great deal of experimentation with different tools and parameters, considering how each would fit to the big picture and the practical implications of their use. Organizing data analysis could prove challenging. In this work we present a set of methods and tools that aim to enable the user to experiment extensively, while keeping analyses reproducible and organized. We present a framework based on simple principles that allow data analyses to be structured in a way that emphasizes reproducibility, organization and clarity, while being simple and intuitive so that adding and modifying analysis steps can be done naturally with little extra effort. The framework suppports version control of code, documentation and data, enabling collaboration between users.

Bioinformatics↗