GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks

Inferring a Gene Regulatory Network (GRN) from gene expression data is a computationally expensive task, exacerbated by increasing data sizes due to advances in high-throughput gene profiling technology, such as single-cell RNA-seq. To equip researchers with a toolset to infer GRNs from large expres...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2019-06, Vol.35 (12), p.2159-2161
Main Authors: Moerman, Thomas, Aibar Santos, Sara, Bravo González-Blas, Carmen, Simm, Jaak, Moreau, Yves, Aerts, Jan, Aerts, Stein
Format: Article
Language:English
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Summary:Inferring a Gene Regulatory Network (GRN) from gene expression data is a computationally expensive task, exacerbated by increasing data sizes due to advances in high-throughput gene profiling technology, such as single-cell RNA-seq. To equip researchers with a toolset to infer GRNs from large expression datasets, we propose GRNBoost2 and the Arboreto framework. GRNBoost2 is an efficient algorithm for regulatory network inference using gradient boosting, based on the GENIE3 architecture. Arboreto is a computational framework that scales up GRN inference algorithms complying with this architecture. Arboreto includes both GRNBoost2 and an improved implementation of GENIE3, as a user-friendly open source Python package. Arboreto is available under the 3-Clause BSD license at http://arboreto.readthedocs.io. Supplementary data are available at Bioinformatics online.
ISSN:1367-4803
1367-4811
DOI:10.1093/bioinformatics/bty916