ArticleGigaScience2016
Colib'read on galaxy: a tools suite dedicated to biological information extraction from raw NGS reads.
Article in GigaScience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Sharkmer: repurposing PCR primers for targeted genome assembly using in silico PCR.Bioinformatics (Oxford, England) · 2026Article
- A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events.PLoS genetics · 2018Article
- Colib'read on galaxy: a tools suite dedicated to biological information extraction from raw NGS reads.GigaScience · 2016Article
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith next-generation sequencing (NGS) technologies, the life sciences face a deluge of raw data. Classical analysis processes for such data often begin with an assembly step, needing large amounts of computing resources, and potentially removing or modifying parts of the biological information contained in the data. Our approach proposes to focus directly on biological questions, by considering raw unassembled NGS data, through a suite of six command-line tools.
findingsDedicated to 'whole-genome assembly-free' treatments, the Colib'read tools suite uses optimized algorithms for various analyses of NGS datasets, such as variant calling or read set comparisons. Based on the use of a de Bruijn graph and bloom filter, such analyses can be performed in a few hours, using small amounts of memory. Applications using real data demonstrate the good accuracy of these tools compared to classical approaches. To facilitate data analysis and tools dissemination, we developed Galaxy tools and tool shed repositories.
conclusionsWith the Colib'read Galaxy tools suite, we enable a broad range of life scientists to analyze raw NGS data. More importantly, our approach allows the maximum biological information to be retained in the data, and uses a very low memory footprint.
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Registered trials
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