JAMI - Fast computation of Conditional Mutual Information for ceRNA network analysis


Motivation: Genome-wide measurements of paired miRNA and geneexpression data have enabled the prediction of competingendogenous RNAs (ceRNAs). It has been shown hat the sponge effectmediated by protein-coding as well as non-coding ceRNAs can playan important regulatory role in the cell in health and disease.Therefore many computational methods for the computationalidentification of ceRNAs have been suggested. In particularmethods based on Conditional Mutual Information (CMI) have shownpromising results. However, the currently availableimplementation is slow and cannot be used to perform computationson a large scale. Results: Here we present JAMI, a Java tool thatuses a non-parametric estimator for CMI values from gene andmiRNA expression data. We show that JAMI speeds up thecomputation of ceRNA networks by a factor of ∼70 compared tocurrently available implementations. Further, JAMI supportsmulti-threading to make use of common multi-core architecturesfor further performance gain. Requirements: Java 8. Availability:JAMI is available as open-source software fromhttps://github.com/SchulzLab/JAMI. Supplementary information:Supplementary data are available at Bioinformatics online.