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pmid:33050927

iMOKA: k-mer based software to analyze large collections of sequencing data.

Claudio Lorenzi · Sylvain Barriere · Jean-Philippe Villemin · Laureline Dejardin Bretones · Alban Mancheron · William Ritchie

Genome biology · 2020 doi:10.1186/s13059-020-02165-2

Paper

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Data

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iMOKA: k-mer based software to analyze large collections of sequencing data.
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10.1186/s13059-020-02165-2
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Claudio Lorenzi
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Sylvain Barriere
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Jean-Philippe Villemin
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Laureline Dejardin Bretones
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Alban Mancheron
[5]
William Ritchie
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Genome biology
year
2020
abstract
iMOKA (interactive multi-objective k-mer analysis) is a software that enables comprehensive analysis of sequencing data from large cohorts to generate robust classification models or explore specific genetic elements associated with disease etiology. iMOKA uses a fast and accurate feature reduction step that combines a Naïve Bayes classifier augmented by an adaptive entropy filter and a graph-based filter to rapidly reduce the search space. By using a flexible file format and distributed indexing, iMOKA can easily integrate data from multiple experiments and also reduces disk space requirements and identifies changes in transcript levels and single nucleotide variants. iMOKA is available at https://github.com/RitchieLabIGH/iMOKA and Zenodo https://doi.org/10.5281/zenodo.4008947 .
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In our benchmark, we compared the list of features output by each algorithm by using them in a random forest classifier and determining their out of bag scores (OOB score).
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