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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

Rapid identification of microbial pathogens and antimicrobial resistance from bloodstream infections using long-read sequencing

The gold standard for bloodstream infection (BSI) diagnostics involves culturing positive blood cultures (BC) using phenotypic methods for organism identification and antimicrobial resistance (AMR) testing, which can take up to five days. However, it is crucial to optimize antimicrobial therapy as soon as possible to reduce morbidity and mortality. We present a novel laboratory and bioinformatic workflow to rapidly identify bacterial and fungal organisms and AMR determinants from positive BCs using Oxford Nanopore Technologies long-read sequencing. Using a robust clinical sample size (n=307), after a BC has flagged positive, our average turnaround time from DNA extraction to determination of species identity was 4.4 h for a multiplex run of 12 BCs, and 3.7 h for a single sample run. We demonstrated that our pipeline taxonomic species identification results agreed with conventional MALDI-TOF identification for almost all positive BCs (97.7%, 300/307). Most species were accurately identified within the first hour of sequencing (93.7 %, 281/300). We explored AMR detection for clinically relevant antimicrobials and observed that assembly-based tools had higher agreement to conventional AST (81.2% after 1 h of sequencing, 89.6% after 5 h of sequencing) than read-based tools. Finally, we developed a publicly available analysis pipeline (venae) that generates a clinician-friendly HTML report, is quick to run, and can dynamically update as more sequencing data is acquired. This study demonstrates how applying rapid, real-time genomics to BSI diagnostics can support clinical decision-making and improve patient outcomes by reducing turnaround times. IMPACT STATMENTEarly pathogen detection and administration of appropriate antimicrobial therapy for BSIs has major impacts on patient survival; early administration of effective antimicrobials reduces mortality, morbidity, length of hospital stay, and development of antimicrobial resistance. Rapid real-time genomics has high potential to improve clinical decision-making and patient outcomes by reducing turnaround times (TATs) while providing high-resolution data for organism identification, AMR determination and pathogen typing. Here, we present a laboratory and bioinformatic workflow that accurately identifies species and AMR determinants in positive blood cultures within several hours, which is quicker than conventional methods which can take days. This workflow is a step forward on the path towards point-of-care diagnostics and applying real-time genomics to characterize microbial infections in clinical settings. DATA SUMMARYIllumina sequencing data for matching pure isolates were deposited in National Centre for Biotechnology Information Sequence Read Archive (NCBI SRA) BioProject PRJNA1380445. Bioinformatic analysis pipeline is available here: https://github.com/phac-nml/venae.

genomics↗

bioRxiv2026

Student-Driven Microbiome Exploration: A Low-Cost 16S rRNA Sequencing Curriculum for Undergraduate Biology Education

Microbiome science is increasingly important in modern biology education because microbial communities influence human health, ecosystems, and environmental processes. However, undergraduate microbiome instruction is often limited by the high cost and technical complexity of sequencing-based workflows, restricting opportunities for authentic student-driven research. To address this challenge, we developed a low-cost, inquiry-based curriculum that enables undergraduate students to conduct complete microbiome studies using 16S rRNA gene sequencing. The module integrates project design, environmental sample collection, microbial cell processing, PCR amplification, sequencing, and bioinformatic analysis using open-source tools such as QIIME 2. Cost-reduction strategies included centrifugation-based cell collection and a surfactant-assisted direct PCR workflow that eliminated the need for commercial DNA extraction kits. Students designed independent research projects investigating microbial communities in local environments, including campus water sources and gym equipment surfaces. Assessment data from post-course surveys, knowledge checks, and student research products demonstrated strong learning gains in microbiome concepts, molecular biology techniques, scientific communication, and computational analysis. Students reported high confidence in PCR, experimental design, and microbiome interpretation, while also identifying bioinformatics as the most challenging yet rewarding component of the curriculum. All participants expressed increased interest in future research in microbiology or bioinformatics. Overall, this curriculum provides an accessible, scalable framework for integrating next-generation sequencing into undergraduate education while promoting inquiry-driven learning, student ownership, and engagement in authentic scientific research.

microbiology↗

bioRxiv2026

Long read and short read whole genome sequencing are equivalent for genomic characterisation of bacteriophage: considerations for high throughput analysis

BackgroundAntimicrobial resistance (AMR) is a global health crisis, necessitating alternative antibacterial strategies. Bacteriophages (phages) offer a promising solution, and their use as a therapeutic agent relies on stringent bioinformatic characterisation using whole genome sequencing (WGS) technologies. However, phages are highly diverse, with no clear consensus on best practices concerning phage DNA extraction or sequencing platform. Efficient and repeatable DNA extraction, sequencing, and bioinformatics processes are critical for safety assessments but remain poorly defined. Additionally, the impact of sequencing platform choice and DNA extraction methods on downstream genomic analyses is not well understood. MethodsWe evaluated multiple DNA extraction, library preparation, and sequencing approaches using a diverse collection of Pseudomonas phages from the PhageWA biobank. Column-based and precipitation-based DNA extraction methods were compared for DNA yield and recovery efficiency. Genome sequencing was performed using short-read (Illumina) and long-read (Oxford Nanopore Technologies) platforms, incorporating multiple library preparation kits and Nanopore basecalling models. Assemblies were assessed for completeness, quality, and sequence concordance using standardised bioinformatics pipelines, with hybrid Illumina-Nanopore assemblies used as references for comparison. ResultsDNA extraction efficiency varied substantially between protocols, with the Puregene precipitation-based method yielding significantly higher DNA recovery than column-based approaches when normalised to phage titre. Illumina sequencing consistently generated complete genome assemblies, although assembly fragmentation was observed for several jumbo phages when using the SeqWell ExpressPlex 2.0 library preparation method. For Nanopore sequencing, ligation-based native barcoding libraries produced longer reads than rapid barcoding libraries, while selection of the Dorado v5.0.0 basecalling model significantly improved read quality. Genome assembly success was dependent on phage genus; native Nanopore sequencing failed to assemble several Pbunavirus genomes, likely due to modified DNA bases, but an amplification-based library preparation successfully resolved these genomes. Across successfully assembled samples, Illumina and Nanopore platforms produced highly concordant genomes with comparable completeness scores, and hybrid polishing identified only minor sequence differences. ConclusionsDNA extraction methodology, sequencing chemistry, and basecalling model selection significantly influence phage WGS outcomes. Precipitation-based DNA extraction improved DNA recovery, while both Illumina and Nanopore sequencing generated high-quality phage genomes suitable for therapeutic characterisation. Nanopore sequencing provided assemblies comparable to Illumina with minimal benefit from hybrid polishing, supporting its routine use for phage genomics. These findings provide practical guidance for phage genome characterisation workflows and contribute to the development of standardised, regulatory-grade approaches for therapeutic phage assessment.

microbiology↗

bioRxiv2026

Multi-agentic system for primer design in qPCR and LAMP diagnostics tests

Primer design is a fundamental component of molecular diagnostics in both quantitative polymerase chain reaction qPCR and loop-mediated isothermal amplification LAMP assays. However, assay design is often performed manually as nucleotide databases, sequence alignment tools and resources are found at different places on the Internet. In this study, an AI-orchestrated bioinformatics workflow was developed to automate the end-to-end qPCR and LAMP primers and probes. The workflow was implemented using LangGraph, LangChain and Biopython, where a series of specialized agents were coordinated to execute sequential bioinformatics tasks with minimal human intervention. Target sequences were then retrieved based on the user request from the National Center for Biotechnology Information nucleotide database and the requested sequence records were then subjected to multiple sequence alignment for the identification of conserved genomic regions. The multi-agentic primer design system can be used for assay development for applications in infectious disease diagnostics, outbreak surveillance and environmental monitoring. This study also demonstrates how multi-agentic systems can be combined with established bioinformatics methods to automate qPCR and LAMP assay design.

molecular biology↗

bioRxiv2026

PGG Enhances Olaparib Efficacy via cGAS-STING Signaling and Sensitizes Triple-Negative Breast Cancer to PD- L1 Blockade

Triple-negative breast cancer (TNBC) is a highly aggressive malignancy and options for targeted therapy are limited. Although PARP inhibitors (PARPi) like olaparib (Ola) leverage synthetic lethality, their clinical utility is severely impeded by drug resistance and low frequency of BRCA mutations. In this study, 1,2,3,4,6-penta-O-galloyl-{beta}-D-glucose (PGG), a natural polyphenolic compound, can induce a state of homologous recombination deficiency (HRD) in TNBC cells by inhibiting the PALB2-BRCA2 interaction. PGG potentiated the inhibitory effects of Ola on the in vitro proliferation, clonogenic potential, migration, and invasion of TNBC cells. Mechanistically, the dual therapy triggered severe oxidative stress and mitochondrial collapse, which accelerated the classical apoptosis cascade and promoted the emission of immunogenic cell death (ICD)-associated DAMPs. The accumulated cytosolic double-stranded DNA activated the innate immune cGAS-STING pathway, thereby promoting robust intra-tumoral infiltration of CD8+ T cells. Furthermore, since cGAS-STING hyperactivation drives compensatory PD-L1 upregulation on TNBC cells, incorporating anti-PD-L1 antibody into the combination regimen further reversed the immunosuppressive barrier, achieving near-complete tumor eradication and maximum infiltration of cytotoxic T cells in vivo. Bioinformatics analyses further suggested a potential network of PGG targets associated with extracellular matrix organization, offering a bioinformatics-derived hypothesis for its theoretical involvement in modulating stromal tension and vascular homeostasis. Collectively, this study establishes PGG as a multi-functional therapeutic agent integrating intracellular synthetic lethality and innate immune sensing, while providing a preliminary bioinformatic rationale for prospective microenvironmental investigation. This study also shows PGG as a multi-functional therapeutic agent that integrates intracellular synthetic lethality, innate immune sensing, and microenvironmental remodeling to convert immunologically "cold" TNBC into "hot" tumors.

biochemistry↗

bioRxiv2014

Improving Protein Docking with Constraint Programming and Coevolution Data

BackgroundConstraint programming (CP) is usually seen as a rigid approach, focusing on crisp, precise, distinctions between what is allowed as a solution and what is not. At first sight, this makes it seem inadequate for bioinformatics applications that rely mostly on statistical parameters and optimization. The prediction of protein interactions, or protein docking, is one such application. And this apparent problem with CP is particularly evident when constraints are provided by noisy data, as it is the case when using the statistical analysis of Multiple Sequence Alignments (MSA) to extract coevolution information. The goal of this paper is to show that this first impression is misleading and that CP is a useful technique for improving protein docking even with data as vague and noisy as the coevolution indicators that can be inferred from MSA.\n\nResultsHere we focus on the study of two protein complexes. In one case we used a simplified estimator of interaction propensity to infer a set of five candidate residues for the interface and used that set to constrain the docking models. Even with this simplified approach and considering only the interface of one of the partners, there is a visible focusing of the models around the correct configuration. Considering a set of 400 models with the best geometric contacts, this constraint increases the number of models close to the target (RMSD {inverted exclamation}5[A]) from 2 to 5 and decreases the RMSD of all retained models from 26[A] to 17.5[A]. For the other example we used a more standard estimate of coevolving residues, from the Co-Evolution Analysis using Protein Sequences (CAPS) software. Using a group of three residues identified from the sequence alignment as potentially co-evolving to constrain the search, the number of complexes similar to the target among the 50 highest scoring docking models increased from 3 in the unconstrained docking to 30 in the constrained docking.\n\nConclusionsAlthough only a proof-of-concept application, our results show that, with suitably designed constraints, CP allows us to integrate coevolution data, which can be inferred from databases of protein sequences, even though the data is noisy and often \"fuzzy\", with no well-defined discontinuities. This also shows, more generally, that CP in bioinformatics needs not be limited to the more crisp cases of finite domains and explicit rules but can also be applied to a broader range of problems that depend on statistical measurements and continuous data.

Bioinformatics↗

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

Bioinformatics↗

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.

Bioinformatics↗

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).

Bioinformatics↗

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.

Bioinformatics↗

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.

Bioinformatics↗

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.

Bioinformatics↗

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.

bioinformatics↗

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.

bioinformatics↗

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.

bioinformatics↗