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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2014

iRAP - an integrated RNA-seq Analysis Pipeline

RNA-sequencing (RNA-Seq) has become the technology of choice for whole-transcriptome profiling. However, processing the millions of sequence reads generated requires considerable bioinformatics skills and computational resources. At each step of the processing pipeline many tools are available, each with specific advantages and disadvantages. While using a specific combination of tools might be desirable, integrating the different tools can be time consuming, often due to specificities in the formats of input/output files required by the different programs. Here we present iRAP, an integrated RNA-seq analysis pipeline that allows the user to select and apply their preferred combination of existing tools for mapping reads, quantifying expression, testing for differential expression. iRAP also includes multiple tools for gene set enrichment analysis and generates web browsable reports of the results obtained in the different stages of the pipeline. Depending upon the application, iRAP can be used to quantify expression at the gene, exon or transcript level. iRAP is aimed at a broad group of users with basic bioinformatics training and requires little experience with the command line. Despite this, it also provides more advanced users with the ability to customise the options used by their chosen tools.

Bioinformatics↗

bioRxiv2014

RNA-Rocket: An RNA-Seq Analysis Resource for Infectious Disease Research

MotivationRNA-Seq is a method for profiling transcription using high-throughput sequencing and is an important component of many research projects that wish to study transcript isoforms, condition specific expression, and transcriptional structure. The methods, tools, and technologies employed to perform RNA-Seq analysis continue to change, creating a bioinformatics challenge for researchers who wish to exploit these data. Resources that bring together genomic data, analysis tools, educational material, and computational infrastructure can minimize the overhead required of life science researchers.\n\nResultsRNA-Rocket is a free service that provides access to RNA-Seq and ChIP-Seq analysis tools for studying infectious diseases. The site makes available thousands of pre-indexed genomes, their annotations, and the ability to stream results to the bioinformatics resources VectorBase, EuPathDB, and PATRIC. The site also provides a combination of experimental data and metadata, examples of pre-computed analysis, step-by-step guides, and a user interface designed to enable both novice and experienced users of RNA-Seq data.\n\nAvailability: RNA-Rocket can be found at rnaseq.pathogenportal.org Source code for this project can be found at github.com/cidvbi/PathogenPortal

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bioRxiv2015

The Impact of High-Performance Computing Best Practice Applied to Next-Generation Sequencing Workflows

High Performance Computing (HPC) Best Practice offers opportunities to implement lessons learned in areas such as computational chemistry and physics in genomics workflows, specifically Next-Generation Sequencing (NGS) workflows. In this study we will briefly describe how distributed-memory parallelism can be an important enhancement to the performance and resource utilization of NGS workflows. We will illustrate this point by showing results on the parallelization of the Inchworm module of the Trinity RNA-Seq pipeline for de novo transcriptome assembly. We show that these types of applications can scale to thousands of cores. Time scaling as well as memory scaling will be discussed at length using two RNA-Seq datasets, targeting the Mus musculus (mouse) and the Axolotl (Mexican salamander). Details about the efficient MPI communication and the impact on performance will also be shown. We hope to demonstrate that this type of parallelization approach can be extended to most types of bioinformatics workflows, with substantial benefits. The efficient, distributed-memory parallel implementation eliminates memory bottlenecks and dramatically accelerates NGS analysis. We further include a summary of programming paradigms available to the bioinformatics community, such as C++/MPI.

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bioRxiv2015

Bandage: interactive visualisation of de novo genome assemblies

SummaryWhile de novo assembly graphs contain assembled contigs (nodes), the connections between those contigs (edges) are difficult for users to access. Bandage (a Bioinformatics Application for Navigating De novo Assembly Graphs Easily) is a tool for visualising assembly graphs with connections. Users can zoom in to specific areas of the graph and interact with it by moving nodes, adding labels, changing colours and extracting sequences. BLAST searches can be performed within the Bandage GUI and the hits are displayed as highlights in the graph. By displaying connections between contigs, Bandage presents new possibilities for analysing de novo assemblies that are not possible through investigation of contigs alone.\n\nAvailability and implementationSource code and binaries are freely available at https://github.com/rrwick/Bandage. Bandage is implemented in C++ and supported on Linux, OS X and Windows.\n\nContactrrwick@gmail.com\n\nSupplementary informationA full feature list and screenshots are available at Bioinformatics online and http://rrwick.github.io/Bandage.

Bioinformatics↗

bioRxiv2015

WU-CRISPR: characteristics of functional guide RNAs for the CRISPR/Cas9 system

The CRISPR/Cas9 system has been rapidly adopted for genome editing. However, one major issue with this system is the lack of robust bioinformatics tools for design of single guide RNA (sgRNA), which determines the efficacy and specificity of genome editing. To address this pressing need, we analyze CRISPR RNA-seq data and identify many novel features that are characteristic of highly potent sgRNAs. These features are used to develop a bioinformatics tool for genome-wide design of sgRNAs with improved efficiency. These sgRNAs as well as the design tool are freely accessible via a web server, WU-CRISPR (http://crispr.wustl.edu).

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bioRxiv2015

Are all global alignment algorithms and implementations correct?

Abstract.Pairwise sequence alignment is perhaps the most fundamental bioinformatics operation. An optimal global alignment algorithm was described in 1970 by Needleman and Wunsch. In 1982 Gotoh presented an improved algorithm with lower time complexity. Gotohs algorithm is frequently cited (1447 citations, Google Scholar, May 2015), taught and, most importantly, used as well as implemented. While implementing the algorithm, we discovered two mathematical mistakes in Gotohs paper that induce sub-optimal sequence alignments. First, there are minor indexing mistakes in the dynamic programming algorithm which become apparent immediately when implementing the procedure. Hence, we report on these for the sake of completeness. Second, there is a more profound problem with the dynamic programming matrix initialization. This initialization issue can easily be missed and find its way into actual implementations. This error is also present in standard text books. Namely, the widely used books by Gusfield and Waterman. To obtain an initial estimate of the extent to which this error has been propagated, we scrutinized freely available undergraduate lecture slides. We found that 8 out of 31 lecture slides contained the mistake, while 16 out of 31 simply omit parts of the initialization, thus giving an incomplete description of the algorithm. Finally, by inspecting ten source codes and running respective tests, we found that five implementations were incorrect. Note that, not all bugs we identified are due to the mistake in Gotohs paper. Three implementations rely on additional constraints that limit generality. Thus, only two out of ten yield correct results. We show that the error introduced by Gotoh is straightforward to resolve and provide a correct open-source reference implementation. We do believe though, that raising the awareness about these errors is critical, since the impact of incorrect pairwise sequence alignments that typically represent one of the very first stages in any bioinformatics data analysis pipeline can have a detrimental impact on downstream analyses such as multiple sequence alignment, orthology assignment, phylogenetic analyses, divergence time estimates, etc.

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bioRxiv2016

SAMSA: A comprehensive metatranscriptome analysis pipeline

BackgroundAlthough metatranscriptomics--the study of diverse microbial population activity based on RNA-seq data--is rapidly growing in popularity, there are limited options for biologists to analyze this type of data. Current approaches for processing metatranscriptomes rely on restricted databases and a dedicated computing cluster, or metagenome-based approaches that have not been fully evaluated for processing metatranscriptomic datasets. We created a new bioinformatics pipeline, SAMSA, designed specifically for metatranscriptome dataset analysis, which runs either inhouse or in conjunction with Metagenome-RAST (MG-RAST) servers. Designed for use by researchers with relatively little bioinformatics experience, SAMSA offers a breakdown of metatranscriptome activity by organism or transcript function, and is fully open source. We next used this new tool to evaluate best practices for sequencing stool metatranscriptomes.\n\nResultsWorking with the MG-RAST annotation server, we constructed the Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) software package, a complete pipeline for the analysis of gut microbiome data. In creating this package, we determined optimal parameters in data collection and processing. SAMSA can summarize and evaluate raw annotation results, identifying abundant species and significant functional differences between metatranscriptomes.\n\nUsing pilot data and simulated subsets, we determined experimental requirements for fecal gut metatranscriptomes. Sequences need to be either long reads (longer than 100bp) or paired-end reads that can be joined. Each sample nees 40-50 million raw sequences which can be expected to yield the 5-10 million annotated reads necessary for accurate abundance measures. We also demonstrated that ribosomal RNA depletion does not equally deplete ribosomes from all species within a sample, and remaining rRNA sequences should be discarded. Using publicly available metatranscriptome data in which rRNA was not depleted, we were able to demonstrate that organism activity can be measured using mRNA counts. We were also able to detect significant differences between control and experimental groups in both organism activity and functional activity.\n\nConclusionsBy making this new pipeline publicly available, we have created a powerful new tool for metatranscriptomics research, offering a new method for greater insight into the activity of diverse microbial communities. We further recommend that stool metatranscriptomes be ribodepleted and sequenced in a 100bp paired end format with a minimum of 40 million reads per sample.

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bioRxiv2016

Estimating the functional impact of INDELs in transcription factor binding sites: a genome-wide landscape

BackgroundVariants in transcription factor binding sites (TFBSs) may have important regulatory effects, as they have the potential to alter transcription factor (TF) binding affinities and thereby affecting gene expression. With recent advances in sequencing technologies the number of variants identified in TFBSs has increased, hence understanding their role is of significant interest when interpreting next generation sequencing data. Current methods have two major limitations: they are limited to predicting the functional impact of single nucleotide variants (SNVs) and often rely on additional experimental data, laborious and expensive to acquire. We propose a purely bioinformatic method that addresses these two limitations while providing comparable results.\n\nResultsOur method uses position weight matrices and a sliding window approach, in order to account for the sequence context of variants, and scores the consequences of both SNVs and INDELs in TFBSs. We tested the accuracy of our method in two different ways. Firstly, we compared it to a recent method based on DNase I hypersensitive sites sequencing (DHS-seq) data designed to predict the effects of SNVs: we found a significant correlation of our score both with their DHS-seq data and their prediction model. Secondly, we called INDELs on publicly available DHS-seq data from ENCODE, and found our score to represent well the experimental data. We concluded that our method is reliable and we used it to describe the landscape of variation in TFBSs in the human genome, by scoring all variants in the 1000 Genomes Project Phase 3. Surprisingly, we found that most insertions have neutral effects on binding sites, while deletions, as expected, were found to have the most severe TFBS-scores. We identified four categories of variants based on their TFBS-scores and tested them for enrichment of variants classified as pathogenic, benign and protective in ClinVar: we found that the variants with the most negative TFBS-scores have the most significant enrichment for pathogenic variants.\n\nConclusionsOur method addresses key shortcomings of currently available bioinformatic tools in predicting the effects of INDELs in TFBSs, and provides an unprecedented window into the genome-wide landscape of INDELs, their predicted influences on TF binding, and potential relevance for human diseases. We thus offer an additional tool to help prioritising non-coding variants in sequencing studies.

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bioRxiv2016

Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data

High throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biological samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bias otherwise clearly seen at the level of individual genes. We created a mathematical model of cumulative suppression of data variation that predicts the ideal parameters and the optimal size of a molecular pathway. We compared the abilities to aggregate experimental molecular data for the five alternative methods, also evaluated by their capacity to retain meaningful features of biological samples. The bioinformatic method OncoFinder showed optimal performance in both tests and should be very useful for future cross-platform data analyses.

Bioinformatics↗

bioRxiv2016

Overlapping long sequence reads: Current innovations and challenges in developing sensitive, specific and scalable algorithms

Identifying overlaps between error-prone long reads, specifically those from Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PB), is essential for certain downstream applications, including error correction and de novo assembly. Though akin to the read-to-reference alignment problem, read-to-read overlap detection is a distinct problem that can benefit from specialized algorithms that perform efficiently and robustly on high error rate long reads. Here, we review the current state-of-the-art read-to-read overlap tools for error-prone long reads, including BLASR, DALIGNER, MHAP, GraphMap, and Minimap. These specialized bioinformatics tools differ not just in their algorithmic designs and methodology, but also in their robustness of performance on a variety of datasets, time and memory efficiency, and scalability. We highlight the algorithmic features of these tools, as well as their potential issues and biases when utilizing any particular method. We benchmarked these tools, tracking their resource needs and computational performance, and assessed the specificity and precision of each. The concepts surveyed may apply to future sequencing technologies, as scalability is becoming more relevant with increased sequencing throughput.\n\nContactcjustin@bcgsc.ca; ibirol@bcgsc.ca\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2016

A computational method for detection of structural variants using Deviant Reads and read pair Orientation: DevRO

BackgroundNext generation sequencing (NGS) technology has made it possible to perform high-resolution screens for structural variants. Computational methods for detection of structural variants utilize paired-end mapping information, depth of coverage, split reads, or some combination of such data. The available methods are particularly designed to detect structural variants in single genomes or multiple genomes in a pairwise manner. The aim of this study was to develop a bioinformatics pipeline for detection of large structural variants using multiple pooled populations.\n\nResultsHere we describe the method \"DevRO\", developed to enable identification of structural variants using short insert paired-ends and long-range mate-pairs. DevRO uses paired-end mapping information from both types of libraries for identification of inversions, deletions and duplications followed by read depth information to screen for copy number variants. DevRO can detect structural variants in multiple populations without the need for pairwise comparisons. It uses a combined approach based on (i) paired-end mapping and (ii) depth of coverage that gives power to the study as compared to traditional methods that are based on either of these. DevRO is also designed to detect deletions in the reference assembly, which is an added functionality as compared to available methods.\n\nConclusionWe report a bioinformatics pipeline \"DevRO\" for detection of structural variants using paired-end mapping and depth of coverage methods tested on sequencing reads from multiple pooled rabbit populations. This method is useful when large numbers of populations have been re-sequenced as compared to traditional methods that can detect structural variants in a pairwise manner.

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bioRxiv2017

Building containerized workflows for RNA-seq data using the BioDepot-workflow-Builder (BwB)

We present the BioDepot-workflow-builder (Bwb), a software tool that allows users to create and execute reproducible bioinformatics workflows using a drag-and-drop interface. Graphical widgets represent Docker containers executing a modular task. Widgets are then graphically linked to build bioinformatics workflows that can be reproducibly deployed across different local and cloud platforms. Each widget contains a form-based user interface to facilitate parameter entry and a console to display intermediate results. Bwb provides tools for rapid customization of widgets, containers and workflows. Saved workflows can be shared using Bwbs native format or exported as shell scripts.

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bioRxiv2017

Improving the performance of minimizers andwinnowing schemes

The minimizers scheme is a method for selecting k-mers from sequences. It is used in many bioinformatics software tools to bin comparable sequences or to sample a sequence in a deterministic fashion at approximately regular intervals, in order to reduce memory consumption and processing time. Although very useful, the minimizers selection procedure has undesirable behaviors (e.g., too many k-mers are selected when processing certain sequences). Some of these problems were already known to the authors of the minimizers technique, and the natural lexicographic ordering of k-mers used by minimizers was recognized as their origin. Many software tools using minimizers employ ad hoc variations of the lexicographic order to alleviate those issues.\n\nWe provide an in-depth analysis of the effect of k-mer ordering on the performance of the minimizers technique. By using small universal hitting sets (a recently defined concept), we show how to significantly improve the performance of minimizers and avoid some of its worse behaviors. Based on these results, we encourage bioinformatics software developers to use an ordering based on a universal hitting set or, if not possible, a randomized ordering, rather than the lexicographic order. This analysis also settles negatively a conjecture (by Schleimer et al.) on the expected density of minimizers in a random sequence.\n\nThe software used for this analysis is available on GitHub: https://github.com/gmarcais/minimizers.git.\n\nContact: gmarcais@cs.cmu.edu

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bioRxiv2017

GenomicDataCommons: a Bioconductor Interface to the NCI Genomic Data Commons

The National Cancer Institute (NCI) Genomic Data Commons (Grossman et al. 2016, https://gdc.cancer.gov/) provides the cancer research community with an open and unified repository for sharing and accessing data across numerous cancer studies and projects via a high-performance data transfer and query infrastructure. The Bioconductor project (Huber et al. 2015) is an open source and open development software project built on the R statistical programming environment (R Core Team 2016). A major goal of the Bioconductor project is to facilitate the use, analysis, and comprehension of genomic data. The GenomicDataCommons Bioconductor package provides basic infrastructure for querying, accessing, and mining genomic datasets available from the GDC. We expect that Bioconductor developer and bioinformatics community will build on the GenomicDataCommons package to add higher-level functionality and expose cancer genomics data to many state-of-the-art bioinformatics methods available in Bioconductor.\n\nAvailabilityhttps://github.com/Bioconductor/GenomicDataCommons (& soon in Bioconductor).

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bioRxiv2017

PinAPL-Py: a web-service for the analysis of CRISPR-Cas9 Screens

BackgroundLarge-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools have been developed for this purpose, their utility is still hindered either due to limited functionality or the requirement of bioinformatic expertise.\n\nResultsTo make sequencing data analysis of CRISPR/Cas9 screens more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which is operated as an intuitive web-service. PinAPL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflow covering sequence quality control, automated sgRNA sequence extraction, alignment, sgRNA enrichment/depletion analysis and gene ranking. The workflow is set up to use a variety of popular sgRNA libraries as well as custom libraries that can be easily uploaded. Various analysis options are offered, suitable to analyze a large variety of CRISPR/Cas9 screening experiments. Analysis output includes ranked lists of sgRNAs and genes, and publication-ready plots.\n\nConclusionsPinAPL-Py helps to advance genome-wide screening efforts by combining comprehensive functionality with user-friendly implementation. PinAPL-Py is freely accessible at http://pinapl-py.ucsd.edu with instructions, documentation and test datasets. The source code is available at https://github.com/LewisLabUCSD/PinAPL-Py

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bioRxiv2017

iDEP: An integrated web application for differential expression and pathway analysis

The analysis and interpretation of the RNA-Seq data can be time-consuming and challenging. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly web application for exploratory data analysis, differential expression, and pathway analysis. iDEP (integrated Differential Expression and Pathway analysis) seamlessly connects 63 R/Bioconductor packages, 208 annotation databases for plant and animal species, and 2 web services. The workflow can be reproduced by downloading customized R code and related files. As demonstrated by two examples, iDEP (http://ge-lab.org/idep/) democratizes access to bioinformatics resources and empowers biologists to easily gain actionable insights from transcriptomic data.

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bioRxiv2017

DiGeST: Distributed Computing for Scalable Gene and Variant Ranking with Hadoop/Spark

BackgroundThe advent of next-generation sequencing technologies has opened new avenues for clinical genomics research. In particular, as sequencing costs continue to decrease, an ever-growing number of clinical genomics institutes now rely on DNA sequencing studies at varying scales - genome, exome, mendeliome - for uncovering disease-associated variants or genes, in both rare and non-rare diseases.\n\nA common methodology for identifying such variants or genes is to rely on genetic association studies (GAS), that test whether allele or genotype frequencies differ between two groups of individuals, usually diseased subjects and healthy controls. Current bioinformatics tools for performing GAS are designed to run on standalone machines, and do not scale well with the increasing size of study designs and the search for multi-locus genetic associations. More efficient distributed and scalable data analysis solutions are needed to address this challenge.\n\nResultsWe developed a Big Data solution stack for distributing computations in genetic association studies, that address both single and multi-locus associations. The proposed stack, called DiGeST (Distributed Gene/variant Scoring Tool) is divided in two main components: a Hadoop/Spark high-performance computing back-end for efficient data storage and distributed computing, and a Web front-end providing users with a rich set of options to filter, compare and explore exome data from different sample populations. Using exome data from the 1000 Genomes Project, we show that our distributed implementation smoothly scales with computing resources. We make the resulting software stack Open-Source, and provide virtualisation scripts to run the complete environment both on standalone machine or Hadoop-based cluster.\n\nConclusionsHadoop/Spark provides a powerful and well-suited distributed computing framework for genetic association studies. Our work illustrates the flexibility, ease of use and scalability of the framework, and more generally advocates for its wider adoption in bioinformatics pipelines.

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bioRxiv2017

OligoMiner: A rapid, flexible environment for the design of genome-scale oligonucleotide in situ hybridization probes

Oligonucleotide (oligo)-based fluorescence in situ hybridization (FISH) has emerged as an important tool for the study of chromosome organization and gene expression and has been empowered by the commercial availability of highly complex pools of oligos. However, a dedicated bioinformatic design utility has yet to be created specifically for the purpose of identifying optimal oligo FISH probe sequences on the genome-wide scale. Here, we introduce OligoMiner, a rapid and robust computational pipeline for the genome-scale design of oligo FISH probes that affords the scientist exact control over the parameters of each probe. Our streamlined method uses standard bioinformatic file formats, allowing users to seamlessly integrate existing and new utilities into the pipeline as desired, and introduces a novel method for evaluating the specificity of each probe molecule that connects simulated hybridization energetics to rapidly generated sequence alignments using supervised learning. We demonstrate the scalability of our approach by performing genome-scale probe discovery in numerous model organism genomes and showcase the performance of the resulting probes with both diffraction-limited and single-molecule super-resolution imaging of chromosomal and RNA targets. We anticipate this pipeline will make the FISH probe design process much more accessible and will more broadly facilitate the design of pools of hybridization probes for a variety of applications.

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