Yana Bromberg, R. Prabakaran, Anowarul Kabir, Amarda Shehu
{"title":"Variant Effect Prediction in the Age of Machine Learning","authors":"Yana Bromberg, R. Prabakaran, Anowarul Kabir, Amarda Shehu","doi":"10.1101/cshperspect.a041467","DOIUrl":null,"url":null,"abstract":"Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus limited by the inadequate sizes of experimentally curated data sets and by the lack of a standardized definition of variant effect. The emergence of unsupervised, deep learning (DL)-based methods raised an important question: Can machines learn the language of life from the unannotated protein sequence data well enough to identify significant errors in the protein “sentences”? Our analysis suggests that some unsupervised methods perform as well or better than existing supervised methods. Unsupervised methods are also faster and can, thus, be useful in large-scale variant evaluations. For all other methods, however, their performance varies by both evaluation metrics and by the type of variant effect being predicted. We also note that the evaluation of method performance is still lacking on less-studied, nonhuman proteins where unsupervised methods hold the most promise.","PeriodicalId":10494,"journal":{"name":"Cold Spring Harbor perspectives in biology","volume":"23 1","pages":""},"PeriodicalIF":6.9000,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cold Spring Harbor perspectives in biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1101/cshperspect.a041467","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus limited by the inadequate sizes of experimentally curated data sets and by the lack of a standardized definition of variant effect. The emergence of unsupervised, deep learning (DL)-based methods raised an important question: Can machines learn the language of life from the unannotated protein sequence data well enough to identify significant errors in the protein “sentences”? Our analysis suggests that some unsupervised methods perform as well or better than existing supervised methods. Unsupervised methods are also faster and can, thus, be useful in large-scale variant evaluations. For all other methods, however, their performance varies by both evaluation metrics and by the type of variant effect being predicted. We also note that the evaluation of method performance is still lacking on less-studied, nonhuman proteins where unsupervised methods hold the most promise.
期刊介绍:
Cold Spring Harbor Perspectives in Biology offers a comprehensive platform in the molecular life sciences, featuring reviews that span molecular, cell, and developmental biology, genetics, neuroscience, immunology, cancer biology, and molecular pathology. This online publication provides in-depth insights into various topics, making it a valuable resource for those engaged in diverse aspects of biological research.