Machine learning in genetics and genomics

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Abstract

The field of machine learning promises to enable computers to assist humans in making sense of large, complex data sets. In this review, we outline some of the main applications of machine learning to genetic and genomic data. In the process, we identify some recurrent challenges associated with this type of analysis and provide general guidelines to assist in the practical application of machine learning to real genetic and genomic data.

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Most cited references 28

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Gene Ontology: tool for the unification of biology

Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

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What is a support vector machine?

William Noble (2006)

Support vector machines (SVMs) are becoming popular in a wide variety of biological applications. But, what exactly are SVMs and how do they work? And what are their most promising applications in the life sciences?

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A genomic code for nucleosome positioning.

Eran Segal, Yvonne Fondufe-Mittendorf, Lingyi Chen … (2006)

Eukaryotic genomes are packaged into nucleosome particles that occlude the DNA from interacting with most DNA binding proteins. Nucleosomes have higher affinity for particular DNA sequences, reflecting the ability of the sequence to bend sharply, as required by the nucleosome structure. However, it is not known whether these sequence preferences have a significant influence on nucleosome position in vivo, and thus regulate the access of other proteins to DNA. Here we isolated nucleosome-bound sequences at high resolution from yeast and used these sequences in a new computational approach to construct and validate experimentally a nucleosome-DNA interaction model, and to predict the genome-wide organization of nucleosomes. Our results demonstrate that genomes encode an intrinsic nucleosome organization and that this intrinsic organization can explain approximately 50% of the in vivo nucleosome positions. This nucleosome positioning code may facilitate specific chromosome functions including transcription factor binding, transcription initiation, and even remodelling of the nucleosomes themselves.

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Author and article information

Journal

Journal ID (nlm-journal-id): 100962779

Journal ID (pubmed-jr-id): 22269

Journal ID (nlm-ta): Nat Rev Genet

Journal ID (iso-abbrev): Nat. Rev. Genet.

Title: Nature reviews. Genetics

ISSN (Print): 1471-0056

ISSN (Electronic): 1471-0064

Publication date Nihms-submitted: 29 December 2016

Publication date (Electronic): 07 May 2015

Publication date (Print): June 2015

Publication date PMC-release: 02 January 2017

Volume: 16

Issue: 6

Pages: 321-332

Affiliations

Department of Computer Science and Engineering University of Washington Genome Sciences, Foege Building 3720 15th Ave NE Seattle, WA 98195-5065

Department of Genome Sciences Department of Computer Science and Engineering Genome Sciences, Box 355065 Foege Building, S220B 3720 15th Ave NE Seattle, WA 98195-5065 University of Washington