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pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP
BACKGROUND: Structure comparison can provide useful information to identify functional and evolutionary relationship between proteins. With the dramatic increase of protein structure data in the Protein Data Bank, computation time quickly becomes the bottleneck for large scale structure comparisons....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526426/ https://www.ncbi.nlm.nih.gov/pubmed/32993484 http://dx.doi.org/10.1186/s12859-020-03757-2 |
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author | Chen, Weiya Yao, Chun Guo, Yingzhong Wang, Yan Xue, Zhidong |
author_facet | Chen, Weiya Yao, Chun Guo, Yingzhong Wang, Yan Xue, Zhidong |
author_sort | Chen, Weiya |
collection | PubMed |
description | BACKGROUND: Structure comparison can provide useful information to identify functional and evolutionary relationship between proteins. With the dramatic increase of protein structure data in the Protein Data Bank, computation time quickly becomes the bottleneck for large scale structure comparisons. To more efficiently deal with informative multiple structure alignment tasks, we propose pmTM-align, a parallel protein structure alignment approach based on mTM-align/TM-align. pmTM-align contains two stages to handle pairwise structure alignments with Spark and the phylogenetic tree-based multiple structure alignment task on a single computer with OpenMP. RESULTS: Experiments with the SABmark dataset showed that parallelization along with data structure optimization provided considerable speedup for mTM-align. The Spark-based structure alignments achieved near ideal scalability with large datasets, and the OpenMP-based construction of the phylogenetic tree accelerated the incremental alignment of multiple structures and metrics computation by a factor of about 2–5. CONCLUSIONS: pmTM-align enables scalable pairwise and multiple structure alignment computing and offers more timely responses for medium to large-sized input data than existing alignment tools such as mTM-align. |
format | Online Article Text |
id | pubmed-7526426 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75264262020-10-01 pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP Chen, Weiya Yao, Chun Guo, Yingzhong Wang, Yan Xue, Zhidong BMC Bioinformatics Software BACKGROUND: Structure comparison can provide useful information to identify functional and evolutionary relationship between proteins. With the dramatic increase of protein structure data in the Protein Data Bank, computation time quickly becomes the bottleneck for large scale structure comparisons. To more efficiently deal with informative multiple structure alignment tasks, we propose pmTM-align, a parallel protein structure alignment approach based on mTM-align/TM-align. pmTM-align contains two stages to handle pairwise structure alignments with Spark and the phylogenetic tree-based multiple structure alignment task on a single computer with OpenMP. RESULTS: Experiments with the SABmark dataset showed that parallelization along with data structure optimization provided considerable speedup for mTM-align. The Spark-based structure alignments achieved near ideal scalability with large datasets, and the OpenMP-based construction of the phylogenetic tree accelerated the incremental alignment of multiple structures and metrics computation by a factor of about 2–5. CONCLUSIONS: pmTM-align enables scalable pairwise and multiple structure alignment computing and offers more timely responses for medium to large-sized input data than existing alignment tools such as mTM-align. BioMed Central 2020-09-29 /pmc/articles/PMC7526426/ /pubmed/32993484 http://dx.doi.org/10.1186/s12859-020-03757-2 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Software Chen, Weiya Yao, Chun Guo, Yingzhong Wang, Yan Xue, Zhidong pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP |
title | pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP |
title_full | pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP |
title_fullStr | pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP |
title_full_unstemmed | pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP |
title_short | pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP |
title_sort | pmtm-align: scalable pairwise and multiple structure alignment with apache spark and openmp |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526426/ https://www.ncbi.nlm.nih.gov/pubmed/32993484 http://dx.doi.org/10.1186/s12859-020-03757-2 |
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