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fastBMA: scalable network inference and transitive reduction
Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for elimina...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5632288/ https://www.ncbi.nlm.nih.gov/pubmed/29020744 http://dx.doi.org/10.1093/gigascience/gix078 |
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author | Hung, Ling-Hong Shi, Kaiyuan Wu, Migao Young, William Chad Raftery, Adrian E. Yeung, Ka Yee |
author_facet | Hung, Ling-Hong Shi, Kaiyuan Wu, Migao Young, William Chad Raftery, Adrian E. Yeung, Ka Yee |
author_sort | Hung, Ling-Hong |
collection | PubMed |
description | Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory-efficient, parallel, and distributed application that scales to human genome-wide expression data. A 10 000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/). |
format | Online Article Text |
id | pubmed-5632288 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-56322882017-10-12 fastBMA: scalable network inference and transitive reduction Hung, Ling-Hong Shi, Kaiyuan Wu, Migao Young, William Chad Raftery, Adrian E. Yeung, Ka Yee Gigascience Technical Note Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory-efficient, parallel, and distributed application that scales to human genome-wide expression data. A 10 000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/). Oxford University Press 2017-08-22 /pmc/articles/PMC5632288/ /pubmed/29020744 http://dx.doi.org/10.1093/gigascience/gix078 Text en © The Authors 2017. 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 | Technical Note Hung, Ling-Hong Shi, Kaiyuan Wu, Migao Young, William Chad Raftery, Adrian E. Yeung, Ka Yee fastBMA: scalable network inference and transitive reduction |
title | fastBMA: scalable network inference and transitive reduction |
title_full | fastBMA: scalable network inference and transitive reduction |
title_fullStr | fastBMA: scalable network inference and transitive reduction |
title_full_unstemmed | fastBMA: scalable network inference and transitive reduction |
title_short | fastBMA: scalable network inference and transitive reduction |
title_sort | fastbma: scalable network inference and transitive reduction |
topic | Technical Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5632288/ https://www.ncbi.nlm.nih.gov/pubmed/29020744 http://dx.doi.org/10.1093/gigascience/gix078 |
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