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JAMI: Fast Computation of Conditional Mutual Information for ceRNA network analysis

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posted on 2023-11-29, 18:07 authored by Andrea Hornáková, Markus List, Jilles VreekenJilles Vreeken, Marcel H. Schulz
Motivation: Genome-wide measurements of paired miRNA and gene expression data have enabled the prediction of competing endogenous RNAs (ceRNAs). It has been shown that the sponge effect mediated by protein-coding as well as non-coding ceRNAs can play an important regulatory role in the cell in health and disease. Therefore, many computational methods for the computational identification of ceRNAs have been suggested. In particular, methods based on Conditional Mutual Information (CMI) have shown promising results. However, the currently available implementation is slow and cannot be used to perform computations on a large scale. Results: Here, we present JAMI, a Java tool that uses a non-parametric estimator for CMI values from gene and miRNA expression data. We show that JAMI speeds up the computation of ceRNA networks by a factor of 70 compared to currently available implementations. Further, JAMI supports multi-threading to make use of common multi-core architectures for further performance gain.

History

Preferred Citation

Andrea Hornáková, Markus List, Jilles Vreeken and Marcel Schulz. JAMI: Fast Computation of Conditional Mutual Information for ceRNA network analysis. In: Bioinformatics. 2018.

Primary Research Area

  • Trustworthy Information Processing

Legacy Posted Date

2019-06-07

Journal

Bioinformatics

Pages

3050 - 3051

Open Access Type

  • Unknown

Sub Type

  • Article

BibTeX

@article{cispa_all_2908, title = "JAMI: Fast Computation of Conditional Mutual Information for ceRNA network analysis", author = "Hornáková, Andrea and List, Markus and Vreeken, Jilles and Schulz, Marcel H.", journal="{Bioinformatics}", year="2018", }

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