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JAMI: fast computation of conditional mutual information for ceRNA network analysis
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 healt...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6129307/ https://www.ncbi.nlm.nih.gov/pubmed/29659721 http://dx.doi.org/10.1093/bioinformatics/bty221 |
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author | Hornakova, Andrea List, Markus Vreeken, Jilles Schulz, Marcel H |
author_facet | Hornakova, Andrea List, Markus Vreeken, Jilles Schulz, Marcel H |
author_sort | Hornakova, Andrea |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6129307 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-61293072018-09-12 JAMI: fast computation of conditional mutual information for ceRNA network analysis Hornakova, Andrea List, Markus Vreeken, Jilles Schulz, Marcel H Bioinformatics Applications Notes 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. Oxford University Press 2018-09-01 2018-04-06 /pmc/articles/PMC6129307/ /pubmed/29659721 http://dx.doi.org/10.1093/bioinformatics/bty221 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Notes Hornakova, Andrea List, Markus Vreeken, Jilles Schulz, Marcel H JAMI: fast computation of conditional mutual information for ceRNA network analysis |
title | JAMI: fast computation of conditional mutual information for ceRNA network analysis |
title_full | JAMI: fast computation of conditional mutual information for ceRNA network analysis |
title_fullStr | JAMI: fast computation of conditional mutual information for ceRNA network analysis |
title_full_unstemmed | JAMI: fast computation of conditional mutual information for ceRNA network analysis |
title_short | JAMI: fast computation of conditional mutual information for ceRNA network analysis |
title_sort | jami: fast computation of conditional mutual information for cerna network analysis |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6129307/ https://www.ncbi.nlm.nih.gov/pubmed/29659721 http://dx.doi.org/10.1093/bioinformatics/bty221 |
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