Abstract
Background: With 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 computingresources, and potentially removing or modifying parts of the biological information contained in the data. Ourapproach proposes to focus directly on biological questions, by considering raw unassembled NGS data, through asuite of six command-line tools.Findings: Dedicated to ‘whole-genome assembly-free’ treatments, the Colib’read tools suite uses optimizedalgorithms for various analyses of NGS datasets, such as variant calling or read set comparisons. Based on the use of ade 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. Tofacilitate data analysis and tools dissemination, we developed Galaxy tools and tool shed repositories.Conclusions: With the Colib’read Galaxy tools suite, we enable a broad range of life scientists to analyze raw NGSdata. More importantly, our approach allows the maximum biological information to be retained in the data, and usesa very low memory footprint.