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

Authors
  • Lorenzi, Claudio1
  • Barriere, Sylvain1
  • Villemin, Jean-Philippe1
  • Dejardin Bretones, Laureline1
  • Mancheron, Alban2
  • Ritchie, William1
  • 1 University of Montpellier, Montpellier, France , Montpellier (France)
  • 2 Université de Montpellier, CNRS, Montpellier, France , Montpellier (France)
Type
Published Article
Publication Date
Oct 13, 2020
Volume
21
Issue
1
Identifiers
DOI: 10.1186/s13059-020-02165-2
Source
Springer Nature
Keywords
License
Green

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 10.5281/zenodo.4008947.

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