bam.iobio: a web-based, real-time, sequence alignment file inspector

Miller, Chase; Qiao, Yi; DiSera, Tonya; D'Astous, Brian; Marth, Gábor

doi:10.1038/nmeth.3174

Cited by 41 publications

(36 citation statements)

References 2 publications

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“…Data analysis. RNA-seq data were analyzed with Taxonomer, a kmerbased, rapid, interactive metagenomic sequence analysis tool accessed through a web interface on the iobio framework at http://taxonomer .iobio.io (19;Flygare et al,submitted). Taxonomer classifies each read to the highest taxonomic rank possible given a comprehensive sequence database ( Fig.…”

Section: Samplesmentioning

confidence: 99%

Unbiased Detection of Respiratory Viruses by Use of RNA Sequencing-Based Metagenomics: a Systematic Comparison to a Commercial PCR Panel

et al. 2016

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e Current infectious disease molecular tests are largely pathogen specific, requiring test selection based on the patient's symptoms. For many syndromes caused by a large number of viral, bacterial, or fungal pathogens, such as respiratory tract infections, this necessitates large panels of tests and has limited yield. In contrast, next-generation sequencing-based metagenomics can be used for unbiased detection of any expected or unexpected pathogen. However, barriers for its diagnostic implementation include incomplete understanding of analytical performance and complexity of sequence data analysis. We compared detection of known respiratory virus-positive (n ‫؍‬ 42) and unselected (n ‫؍‬ 67) pediatric nasopharyngeal swabs using an RNA sequencing (RNA-seq)-based metagenomics approach and Taxonomer, an ultrarapid, interactive, web-based metagenomics data analysis tool, with an FDA-cleared respiratory virus panel (RVP; GenMark eSensor). Untargeted metagenomics detected 86% of known respiratory virus infections, and additional PCR testing confirmed RVP results for only 2 (33%) of the discordant samples. In unselected samples, untargeted metagenomics had excellent agreement with the RVP (93%). In addition, untargeted metagenomics detected an additional 12 viruses that were either not targeted by the RVP or missed due to highly divergent genome sequences. Normalized viral read counts for untargeted metagenomics correlated with viral burden determined by quantitative PCR and showed high intrarun and interrun reproducibility. Partial or full-length viral genome sequences were generated in 86% of RNA-seq-positive samples, allowing assessment of antiviral resistance, strain-level typing, and phylogenetic relatedness. Overall, untargeted metagenomics had high agreement with a sensitive RVP, detected viruses not targeted by the RVP, and yielded epidemiologically and clinically valuable sequence information. L aboratory diagnosis of infectious diseases has historically taken a syndrome-based approach. Culture of appropriate specimens on a combination of relevant media or cell lines enables detection of certain common bacterial, viral, and fungal pathogens. However, culture requires experienced personnel, requires several days to weeks to yield a definitive answer, depends on viability and appropriate culture conditions, and has limited sensitivity. Molecular tests have superior turnaround times, sensitivity, and taxonomic resolution. However, only targeted pathogens can be detected, and differentiation of clinically or epidemiologically relevant strains or genotypes is limited. Moreover, molecular tests need to be updated when new species or strains are recognized to ensure that newly identified genetic variants can be detected.In contrast, next-generation sequencing-based metagenomic testing combines and extends many advantages of molecular tests and culture-based methods. Host-and pathogen-derived nucleic acids are sequenced without a priori knowledge of expected pathogens, allowing simultaneous detection of a virtually ...

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Section: Samplesmentioning

confidence: 99%

Unbiased Detection of Respiratory Viruses by Use of RNA Sequencing-Based Metagenomics: a Systematic Comparison to a Commercial PCR Panel

et al. 2016

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“…Decision support tools enable more flexible, interactive analyses and generally provide easier means to analyse variant data in the context of external resources such as ExAC, OMIM and ClinVar, which greatly empower clinical decision-making. Academic examples include iobio 14 and Variation Viewer 105 . Commercial tools increasingly have an important role in this domain, partly because of the complexity and cost of developing such software.…”

Section: Current Challenges and Emerging Solutionsmentioning

confidence: 99%

Settling the score: variant prioritization and Mendelian disease

2017

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When investigating Mendelian disease using exome or genome sequencing, distinguishing disease-causing genetic variants from the multitude of candidate variants is a complex, multidimensional task. Many prioritization tools and online interpretation resources exist, and professional organizations have offered clinical guidelines for review and return of prioritization results. In this Review, we describe the strengths and weaknesses of widely used computational approaches, explain their roles in the diagnostic and discovery process and discuss how they can inform (and misinform) expert reviewers. We place variant prioritization in the wider context of gene prioritization, burden testing and genotype–phenotype association, and we discuss opportunities and challenges introduced by whole-genome sequencing.

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“…Tools that depend on HTSlib are typically run from the command-line restricting their accessibility to users who have a specific set of computational skills. To improve accessibility, several tools attempt to provide a better user experience in the form of a web-interface and directly process HTS files in the web-browser [8,9]. This necessitates the use of JavaScript, the only language supported by all major browsers.…”

Section: Introductionmentioning

confidence: 99%

HTSlib-js: Developer toolkit for a client-side JavaScript interface for HTSlib

Park

Childs

Schlesner

et al. 2018

Preprint

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BackgroundAn increasing number of bioinformatics tools are developed in JavaScript to provide an interactive visual interface to users. However, there are no tools yet that enable fast client-side analysis of large, high-throughput sequencing file formats, such as BAM and VCF files. ResultsWe present HTSlib-js, an asm.js-based JavaScript wrapper for HTSlib, the de facto standard for processing BAM and VCF files. HTSlib-js exploits recent technological advances in web browser engines that dramatically increase the performance of browser-based tools to enable swift processing of files in the aforementioned formats.HTSlib-js enables quick development of JavaScript-based applications that include processing of aligned sequence reads and variant calling data. ConclusionsHTSlib-js constitutes a toolkit for developers to easily write fast, accessible, browserbased applications that place an emphasis on data visualization, interactivity and privacy. Real-world examples demonstrate the capabilities of HTSlib-js and serve as guide for own developments. Keywordshigh-throughput sequencing, JavaScript, browser-based applications, interactive data visualization and analysis (Fig. 1C); a basic pileup tool, a somatic variant quality control tool and a tool for identifying and visualising double stranded breaks called Digenome-seq [12]. The pileup tool can process BAM/CRAM files to produce a basic pileup similar to that of the samtools command line tool. The quality control tool can process VCF files and produces plots to assess the quality of somatic variants. The third example is the complete working browser-based Digenome-seq program [13]. As described in the previous section, the examples are automatically built by CMake (via Emscripten for JavaScript targets, or normal CMake for native binaries).Due to limitations imposed by the browser environment, HTSlib-js only supports reading from, but not writing to the hard-drive. Developers wishing to save data to the hard-drive will first need to store results in memory and then prepare a downloadable file using the HTML5 Blob API. ResultsUsing Emscripten, we produced a fully functional HTSlib wrapper that can be used to create JavaScript-based programs capable of quickly processing SAM, BAM, VCF and BCF files. Examples demonstrate the analysis of SAM and BAM files (pileup example), and VCF and BCF files (quality control example) in browser-based tools including the retrieval of CIGAR strings (Digenome-seq). We compared the performance of asm.js binaries in a web browser to native binaries using a 1.3 GiB BAM file. In our speed benchmark, the compiled asm.js binaries showed a performance relatively close to the native binary. There was only a 5x difference in the pileup example and 2x difference in Digenome seq (Table 1), which will be acceptable for many applications. Hence HTSlib-js overcomes a major drawback of earlier browser-based tools.-6 -Due to the browser environment HTSlib-js based tools are likely to be strong in the following aspects: 1) Visualization. By nature, the web...

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bam.iobio: a web-based, real-time, sequence alignment file inspector

Cited by 41 publications

References 2 publications

Unbiased Detection of Respiratory Viruses by Use of RNA Sequencing-Based Metagenomics: a Systematic Comparison to a Commercial PCR Panel

Unbiased Detection of Respiratory Viruses by Use of RNA Sequencing-Based Metagenomics: a Systematic Comparison to a Commercial PCR Panel

Settling the score: variant prioritization and Mendelian disease

HTSlib-js: Developer toolkit for a client-side JavaScript interface for HTSlib

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