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Tytuł:
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netboxr: Automated discovery of biological process modules by network analysis in R.
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Autorzy:
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Liu EM; Department of Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America.
Luna A; Department of Cell Biology, Harvard Medical School, Boston, MA, United States of America.; cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, United States of America.; Broad Institute of MIT and Harvard, Cambridge, MA, United States of America.
Dong G; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States of America.
Sander C; Department of Cell Biology, Harvard Medical School, Boston, MA, United States of America.; cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, United States of America.; Broad Institute of MIT and Harvard, Cambridge, MA, United States of America.
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Źródło:
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PloS one [PLoS One] 2020 Nov 02; Vol. 15 (11), pp. e0234669. Date of Electronic Publication: 2020 Nov 02 (Print Publication: 2020).
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Typ publikacji:
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Journal Article; Research Support, N.I.H., Extramural
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Język:
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English
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Imprint Name(s):
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Original Publication: San Francisco, CA : Public Library of Science
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MeSH Terms:
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Algorithms*
Gene Regulatory Networks*
Genome, Human*
Software*
Biomarkers, Tumor/*genetics
Genomics/*methods
Neoplasms/*genetics
Humans ; Metabolic Networks and Pathways ; Programming Languages
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References:
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Nucleic Acids Res. 2000 Sep 15;28(18):3442-4. (PMID: 10982861)
PLoS One. 2010 Feb 12;5(2):e8918. (PMID: 20169195)
Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Feb;69(2 Pt 2):026113. (PMID: 14995526)
Nature. 2010 Apr 15;464(7291):993-8. (PMID: 20393554)
Nat Protoc. 2019 Feb;14(2):482-517. (PMID: 30664679)
Nat Genet. 2013 Oct;45(10):1113-20. (PMID: 24071849)
Sci Signal. 2013 Apr 02;6(269):pl1. (PMID: 23550210)
Nucleic Acids Res. 2020 Jan 8;48(D1):D489-D497. (PMID: 31647099)
Blood. 2017 Jul 27;130(4):453-459. (PMID: 28600341)
OMICS. 2012 May;16(5):284-7. (PMID: 22455463)
Cancer Discov. 2012 May;2(5):401-4. (PMID: 22588877)
Cell. 2013 Oct 10;155(2):462-77. (PMID: 24120142)
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Grant Information:
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P30 CA008748 United States CA NCI NIH HHS; P41 GM103504 United States GM NIGMS NIH HHS; U41 HG006623 United States HG NHGRI NIH HHS; F32 CA192901 United States CA NCI NIH HHS
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Substance Nomenclature:
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0 (Biomarkers, Tumor)
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Entry Date(s):
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Date Created: 20201102 Date Completed: 20201230 Latest Revision: 20210610
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Update Code:
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20240105
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PubMed Central ID:
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PMC7605689
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DOI:
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10.1371/journal.pone.0234669
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PMID:
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33137091
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Summary: Large-scale sequencing projects, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have generated high throughput sequencing and molecular profiling data sets, but it is still challenging to identify potentially causal changes in cellular processes in cancer as well as in other diseases in an automated fashion. We developed the netboxr package written in the R programming language, which makes use of the NetBox algorithm to identify candidate cancer-related functional modules. The algorithm makes use of a data-driven, network-based approach that combines prior knowledge with a network clustering algorithm, obviating the need for and the limitation of independently curated functionally labeled gene sets. The method can combine multiple data types, such as mutations and copy number alterations, leading to more reliable identification of functional modules. We make the tool available in the Bioconductor R ecosystem for applications in cancer research and cell biology.
Availability and Implementation: The netboxr package is free and open-sourced under the GNU GPL-3 license R package available at https://www.bioconductor.org/packages/release/bioc/html/netboxr.html.
Competing Interests: The authors have declared that no competing interests exist.
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