Cargando…
BUFET: boosting the unbiased miRNA functional enrichment analysis using bitsets
BACKGROUND: A group of miRNAs can regulate a biological process by targeting genes involved in the process. The unbiased miRNA functional enrichment analysis is the most precise in silico approach to predict the biological processes that may be regulated by a given miRNA group. However, it is comput...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5585958/ https://www.ncbi.nlm.nih.gov/pubmed/28874117 http://dx.doi.org/10.1186/s12859-017-1812-8 |
_version_ | 1783261725443751936 |
---|---|
author | Zagganas, Konstantinos Vergoulis, Thanasis Paraskevopoulou, Maria D. Vlachos, Ioannis S. Skiadopoulos, Spiros Dalamagas, Theodore |
author_facet | Zagganas, Konstantinos Vergoulis, Thanasis Paraskevopoulou, Maria D. Vlachos, Ioannis S. Skiadopoulos, Spiros Dalamagas, Theodore |
author_sort | Zagganas, Konstantinos |
collection | PubMed |
description | BACKGROUND: A group of miRNAs can regulate a biological process by targeting genes involved in the process. The unbiased miRNA functional enrichment analysis is the most precise in silico approach to predict the biological processes that may be regulated by a given miRNA group. However, it is computationally intensive and significantly more expensive than its alternatives. RESULTS: We introduce BUFET, a new approach to significantly reduce the time required for the execution of the unbiased miRNA functional enrichment analysis. It derives its strength from the utilization of efficient bitset-based methods and parallel computation techniques. CONCLUSIONS: BUFET outperforms the state-of-the-art implementation, in regard to computational efficiency, in all scenarios (both single- and multi-core), being, in some cases, more than one order of magnitude faster. |
format | Online Article Text |
id | pubmed-5585958 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55859582017-09-06 BUFET: boosting the unbiased miRNA functional enrichment analysis using bitsets Zagganas, Konstantinos Vergoulis, Thanasis Paraskevopoulou, Maria D. Vlachos, Ioannis S. Skiadopoulos, Spiros Dalamagas, Theodore BMC Bioinformatics Software BACKGROUND: A group of miRNAs can regulate a biological process by targeting genes involved in the process. The unbiased miRNA functional enrichment analysis is the most precise in silico approach to predict the biological processes that may be regulated by a given miRNA group. However, it is computationally intensive and significantly more expensive than its alternatives. RESULTS: We introduce BUFET, a new approach to significantly reduce the time required for the execution of the unbiased miRNA functional enrichment analysis. It derives its strength from the utilization of efficient bitset-based methods and parallel computation techniques. CONCLUSIONS: BUFET outperforms the state-of-the-art implementation, in regard to computational efficiency, in all scenarios (both single- and multi-core), being, in some cases, more than one order of magnitude faster. BioMed Central 2017-09-06 /pmc/articles/PMC5585958/ /pubmed/28874117 http://dx.doi.org/10.1186/s12859-017-1812-8 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Zagganas, Konstantinos Vergoulis, Thanasis Paraskevopoulou, Maria D. Vlachos, Ioannis S. Skiadopoulos, Spiros Dalamagas, Theodore BUFET: boosting the unbiased miRNA functional enrichment analysis using bitsets |
title | BUFET: boosting the unbiased miRNA functional enrichment analysis using bitsets |
title_full | BUFET: boosting the unbiased miRNA functional enrichment analysis using bitsets |
title_fullStr | BUFET: boosting the unbiased miRNA functional enrichment analysis using bitsets |
title_full_unstemmed | BUFET: boosting the unbiased miRNA functional enrichment analysis using bitsets |
title_short | BUFET: boosting the unbiased miRNA functional enrichment analysis using bitsets |
title_sort | bufet: boosting the unbiased mirna functional enrichment analysis using bitsets |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5585958/ https://www.ncbi.nlm.nih.gov/pubmed/28874117 http://dx.doi.org/10.1186/s12859-017-1812-8 |
work_keys_str_mv | AT zagganaskonstantinos bufetboostingtheunbiasedmirnafunctionalenrichmentanalysisusingbitsets AT vergoulisthanasis bufetboostingtheunbiasedmirnafunctionalenrichmentanalysisusingbitsets AT paraskevopouloumariad bufetboostingtheunbiasedmirnafunctionalenrichmentanalysisusingbitsets AT vlachosioanniss bufetboostingtheunbiasedmirnafunctionalenrichmentanalysisusingbitsets AT skiadopoulosspiros bufetboostingtheunbiasedmirnafunctionalenrichmentanalysisusingbitsets AT dalamagastheodore bufetboostingtheunbiasedmirnafunctionalenrichmentanalysisusingbitsets |