Evidence map›Paper›PMID 34906079›Full record

ArticleBMC bioinformatics2021

gcMECM: graph clustering of mutual exclusivity of cancer mutations.

Ying Hu, Chunhua Yan, Qingrong Chen, Daoud Meerzaman

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PubMed Full text at PMC Publisher
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Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ying HuCenter for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA. yhu@mail.nih.gov.
Chunhua YanCenter for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA. yanch@mail.nih.gov.ORCID http://orcid.org/0000-0001-5474-7431
Qingrong ChenCenter for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA.
Daoud MeerzamanCenter for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNext-generation sequencing platforms allow us to sequence millions of small fragments of DNA simultaneously, revolutionizing cancer research. Sequence analysis has revealed that cancer driver genes operate across multiple intricate pathways and networks with mutations often occurring in a mutually exclusive pattern. Currently, low-frequency mutations are understudied as cancer-relevant genes, especially in the context of networks.

resultsHere we describe a tool, gcMECM, that enables us to visualize the functionality of mutually exclusive genes in the subnetworks derived from mutation associations, gene-gene interactions, and graph clustering. These subnetworks have revealed crucial biological components in the canonical pathway, especially those mutated at low frequency. Examining the subnetwork, and not just the impact of a single gene, significantly increases the statistical power of clinical analysis and enables us to build models to better predict how and why cancer develops.

conclusionsgcMECM uses a computationally efficient and scalable algorithm to identify subnetworks in a canonical pathway with mutually exclusive mutation patterns and distinct biological functions.

Indexed as

Computational BiologyNeoplasmsAlgorithmsCluster AnalysisHumansMutationCancer driver genesMutually exclusive mutationsNetwork analysisR package

Identifiers

PMID34906079
PMCPMC8670134

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.