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

iMOKA (interactive multi-objective -mer analysis) is 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.

The files "iMOKA" and "iMOKA_extended" are singularity images containing the environment and the compiled software.

The files iMOKA-[xx]-[yy].zip contain the GUI for different architectures.

The zipped folder iMOKA-master.zip contains the source code ( git-hub master of the 31/08/20). It contains also the templates of the codes used to produce the results of the paper ( iMOKA-maste/paper_codes/ ) and the final iMOKA results, in json format, that can be opened using the GUI.

The zipped folder models.tar.gz contains the final matrices of all the methods for each datasets.

The zipped folder models_comparisons.zip contains the results of the models for the OOB score and CV, together with the codes to generate the graphs.