NDLI: NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer

Content Provider	Springer Nature : BioMed Central
Author	Anzar, Irantzu Sverchkova, Angelina Stratford, Richard Clancy, Trevor
Abstract	Background The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue, especially, tumor genetic intra-heterogeneity coupled with the problem of sequencing and alignment artifacts, makes somatic variant calling a challenging task. Current variant filtering strategies, such as rule-based filtering and consensus voting of different algorithms, have previously helped to increase specificity, although comes at the cost of sensitivity. Methods In light of this, we have developed the NeoMutate framework which incorporates 7 supervised machine learning (ML) algorithms to exploit the strengths of multiple variant callers, using a non-redundant set of biological and sequence features. We benchmarked NeoMutate by simulating more than 10,000 bona fide cancer-related mutations into three well-characterized Genome in a Bottle (GIAB) reference samples. Results A robust and exhaustive evaluation of NeoMutate’s performance based on 5-fold cross validation experiments, in addition to 3 independent tests, demonstrated a substantially improved variant detection accuracy compared to any of its individual composite variant callers and consensus calling of multiple tools. Conclusions We show here that integrating multiple tools in an ensemble ML layer optimizes somatic variant detection rates, leading to a potentially improved variant selection framework for the diagnosis and treatment of cancer.
Related Links	https://bmcmedgenomics.biomedcentral.com/counter/pdf/10.1186/s12920-019-0508-5.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	17558794
DOI	10.1186/s12920-019-0508-5
Journal	BMC Medical Genomics
Issue Number	1
Volume Number	12
Language	English
Publisher	BioMed Central
Publisher Date	2019-05-16
Access Restriction	Open
Subject Keyword	Human Genetics Microarrays Gene Expression Somatic variant detection Machine learning Cancer genomics Precision medicine
Content Type	Text
Resource Type	Article
Subject	Genetics (clinical) Genetics
Journal Impact Factor	2.1/2023
5-Year Journal Impact Factor	2.5/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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