Towards precision medicine: advances in computational approaches for the analysis of human variants

Peterson, Thomas A.; Doughty, Emily; Kann, Maricel G.

doi:10.1016/j.jmb.2013.08.008

Cited by 123Genomics and Rare DiseasesGenetic Associations and EpidemiologyBiomedical Text Mining and Ontologies

Towards precision medicine: advances in computational approaches for the analysis of human variants

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Peterson, Thomas A., Doughty, Emily, Kann, Maricel G.·2013·Journal of Molecular Biology

DOI 10.1016/j.jmb.2013.08.008PMC PMC3807015

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Abstract

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.

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In practice, many computational methods overlap and thus do not offer independent assessment of pathogenicity.
“In practice, many methods overlap and thus do not offer independent assessment of pathogenicity.4,6”
From Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022) · cites this paper as [4]
Section 4 (page not available) says prediction methods often use common information and gives examples of methods sharing sequence-conservation metrics. It does not conclude that those methods offer no independent assessment of pathogenicity; it also notes differences in their models and additional attributes. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Precision medicine has grown greatly with support from machine learning and data mining tools.
“There has been unprecedented growth in the development of precision medicine supported by ML (machine learning) approaches (Delavan et al., 2017;Peterson et al., 2013;Zou et al., 2017) and data mining tools (Chawla and Davis, 2013;Cheng et al., 2015;Margolies et al., 2016).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Peterson et al., 2013

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[20]About ClinVar [cited 2013 February 3rd]; Available from: http://www.ncbi.nlm.nih.gov/clinvar/
[54]NCBI’s 1000 Genomes Annotation Tracks Available from: http://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/www.ncbi.nlm.nih.gov/variation/
[65]den Dunnen JT Antonarakis SE Mutation nomenclature extensions and suggestions to describe complex mutations: a discussion Hum Mutat 2000 15 1 7 12 10612815
[81]Entrez Help [Internet] Bethesda (MD) National Center for Biotechnology Information (US) 2005-. Entrez Help 2006 1 20 [Updated 2011 Dec 19]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK3837/

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Authors: Peterson, T. A., Doughty, E., & Kann, M. G.
Affiliations: University of Maryland, Baltimore County (US)
Stanford University (US)
Corresponding author: Kann, Maricel G.
ORCID: Kann, Maricel G.
Venue: Journal of Molecular Biology
Publisher: Elsevier BV
Published: 2013-11
Volume: 425
Issue: 21
Pages: 4047-4063
ISSN: 0022-2836 (ISSN-L)
DOI: 10.1016/j.jmb.2013.08.008
PMC: PMC3807015
OpenAlex: W2024364470
Cited by: 123
References: 175
Citations by year: 2026: 2 · 2025: 3 · 2024: 6 · 2023: 3 · 2022: 9 · 2021: 14 · 2020: 11 · 2019: 14 · 2018: 10 · 2017: 7 · 2016: 17 · 2015: 11 · 2014: 13 · 2013: 3
FWCI: 1.80
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Journal of Molecular Biology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institutes of Health (5k22ca143148-03)
National Cancer Institute (1K22CA143148, K22 CA143148)
U.S. National Library of Medicine (R01LM009722, T15LM007033)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
Keywords: human genetic variants, precision medicine, variant pathogenicity prediction, deleterious variant prediction, variant database, ClinVar, OMIM, HGMD, disease susceptibility, drug response, drug target identification, personalized diagnosis and treatment
MeSH terms: Humans, Genetic Variation, Genome, Human, Sequence Analysis, Computational Biology, Genetic Predisposition to Disease

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Towards precision medicine: advances in computational approaches for the analysis of human variants

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