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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...

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Autores principales: Hung, Ling-Hong, Shi, Kaiyuan, Wu, Migao, Young, William Chad, Raftery, Adrian E., Yeung, Ka Yee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
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/).
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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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