Robust predictions of specialized metabolism genes through machine learning

Moore, Bethany M.; Wang, Peipei; Fan, Pengxiang; Leong, Bryan; Schenck, Craig A.; Lloyd, John P.; Lehti-Shiu, Melissa D.; Last, Robert L.; Pichersky, Eran; Shiu, Shin-Han

doi:10.1073/pnas.1817074116

Title: Robust predictions of specialized metabolism genes through machine learning

Journal Article · Wed Jan 23 00:00:00 EST 2019 · Proceedings of the National Academy of Sciences of the United States of America

DOI:https://doi.org/10.1073/pnas.1817074116· OSTI ID:1491911

; Wang, Peipei;

;

; Schenck, Craig A.; Lloyd, John P.; Lehti-Shiu, Melissa D.;

; Pichersky, Eran;

Plant specialized metabolism (SM) enzymes produce lineage-specific metabolites with important ecological, evolutionary, and biotechnological implications. Using Arabidopsis thalianaas a model, we identified distinguishing characteristics of SM and GM (general metabolism, traditionally referred to as primary metabolism) genes through a detailed study of features including duplication pattern, sequence conservation, transcription, protein domain content, and gene network properties. Analysis of multiple sets of benchmark genes revealed that SM genes tend to be tandemly duplicated, coexpressed with their paralogs, narrowly expressed at lower levels, less conserved, and less well connected in gene networks relative to GM genes. Although the values of each of these features significantly differed between SM and GM genes, any single feature was ineffective at predicting SM from GM genes. Using machine learning methods to integrate all features, a prediction model was established with a true positive rate of 87% and a true negative rate of 71%. In addition, 86% of known SM genes not used to create the machine learning model were predicted. We also demonstrated that the model could be further improved when we distinguished between SM, GM, and junction genes responsible for reactions shared by SM and GM pathways, indicating that topological considerations may further improve the SM prediction model. Application of the prediction model led to the identification of 1,220 A. thaliana genes with previously unknown functions, each assigned a confidence measure called an SM score, providing a global estimate of SM gene content in a plant genome.

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Research Organization:: Univ. of Wisconsin, Madison, WI (United States); USDOE Bioenergy Research Centers (BRC) (United States). Great Lakes Bioenergy Research Center (GLBRC)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)

Grant/Contract Number:: SC0018409; IOS-1546617; NSF DEB-1655386

OSTI ID:: 1491911

Alternate ID(s):: OSTI ID: 1612975

Journal Information:: Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Vol. 116 Journal Issue: 6; ISSN 0027-8424

Publisher:: National Academy of SciencesCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 56 works

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Title: Robust predictions of specialized metabolism genes through machine learning

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