JAMI: Fast Computation of Conditional Mutual Information for CeRNA Network Analysis

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2018 Apr 17

PMID 29659721

Citations 10

Authors

Andrea Hornakova

Markus List

Jilles Vreeken

Marcel H Schulz

Affiliations

Soon will be listed here.

Abstract

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.

Requirements: Java 8.

Availability And Implementation: JAMI is available as open-source software from https://github.com/SchulzLab/JAMI.

Supplementary Information: Supplementary data are available at Bioinformatics online.

Citing Articles

Modeling ncRNA Synergistic Regulation in Cancer.

Zhang J, Xiong C, Wei X, Yang H, Zhao C Methods Mol Biol. 2024; 2883:377-402.

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Identification of aberrantly expressed lncRNAs and ceRNA networks in multiple myeloma: a combined high-throughput sequencing and microarray analysis.

Lu M, He Y, Wu Y, Zhou H, Jian Y, Gao W Front Oncol. 2023; 13:1160342.

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ceRNAR: An R package for identification and analysis of ceRNA-miRNA triplets.

Hsiao Y, Wang L, Lu T PLoS Comput Biol. 2022; 18(9):e1010497.

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MethReg: estimating the regulatory potential of DNA methylation in gene transcription.

C Silva T, Young J, Martin E, Chen X, Wang L Nucleic Acids Res. 2022; 50(9):e51.

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Illuminating lncRNA Function Through Target Prediction.

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