Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning

The function of most genes is unknown. The best results in automated function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data. Even though there is ample evidence showing that...

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Authors: Pazos Obregón, Flavio, Silvera, Diego, Cantera, Rafael, Yankilevich, Patricio, Guerberoff, Gustavo, Soto, Pablo
Format: article
Status:Published version
Publication Date:2022
Country:Uruguay
Institution:Universidad de la República
Repository:COLIBRI
Language:English
OAI Identifier:oai:colibri.udelar.edu.uy:20.500.12008/39141
Online Access:https://hdl.handle.net/20.500.12008/39141
Access Level:Open access
Keyword:Bioinformatics
Comparative genomics
Machine learning
Protein function predictions
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dc.title.none.fl_str_mv Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning
title Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning
spellingShingle Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning
Pazos Obregón, Flavio
Bioinformatics
Comparative genomics
Machine learning
Protein function predictions
title_short Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning
title_full Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning
title_fullStr Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning
title_full_unstemmed Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning
title_sort Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning
dc.creator.none.fl_str_mv Pazos Obregón, Flavio
Silvera, Diego
Cantera, Rafael
Yankilevich, Patricio
Guerberoff, Gustavo
Soto, Pablo
author Pazos Obregón, Flavio
author_facet Pazos Obregón, Flavio
Silvera, Diego
Cantera, Rafael
Yankilevich, Patricio
Guerberoff, Gustavo
Soto, Pablo
author_role author
author2 Silvera, Diego
Cantera, Rafael
Yankilevich, Patricio
Guerberoff, Gustavo
Soto, Pablo
author2_role author
author
author
author
author
dc.contributor.filiacion.none.fl_str_mv Pazos Obregón Flavio, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Biología.
Silvera Diego, IIBCE
Cantera Rafael, IIBCE
Yankilevich Patricio
Guerberoff Gustavo, Universidad de la República (Uruguay). Facultad de Ingeniería.
Soto Pablo, IIBCE
dc.subject.es.fl_str_mv Bioinformatics
Comparative genomics
Machine learning
Protein function predictions
topic Bioinformatics
Comparative genomics
Machine learning
Protein function predictions
description The function of most genes is unknown. The best results in automated function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data. Even though there is ample evidence showing that a gene’s function is not independent of its location, the few available examples of gene function prediction based on gene location rely on sequence identity between genes of different organisms and are thus subjected to the limitations of the relationship between sequence and function. Here we predict thousands of gene functions in five model eukaryotes (Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Homo sapiens) using machine learning models exclusively trained with features derived from the location of genes in the genomes to which they belong. Our aim was not to obtain the best performing method to automated function prediction but to explore the extent to which a gene's location can predict its function in eukaryotes. We found that our models outperform BLAST when predicting terms from Biological Process and Cellular Component Ontologies, showing that, at least in some cases, gene location alone can be more useful than sequence to infer gene function.
publishDate 2022
dc.date.issued.none.fl_str_mv 2022
dc.date.accessioned.none.fl_str_mv 2023-08-10T12:24:40Z
dc.date.available.none.fl_str_mv 2023-08-10T12:24:40Z
dc.type.es.fl_str_mv Artículo
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.citation.es.fl_str_mv Pazos Obregón, F, Silvera, D, Cantera, R, [y otros autores]. "Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning". Scientific Reports. [en línea] 2022, 12: 11655. 11 h. DOI: 10.1038/s41598-022-15329-w
dc.identifier.issn.none.fl_str_mv 2045-2322
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12008/39141
dc.identifier.doi.none.fl_str_mv 10.1038/s41598-022-15329-w
identifier_str_mv Pazos Obregón, F, Silvera, D, Cantera, R, [y otros autores]. "Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning". Scientific Reports. [en línea] 2022, 12: 11655. 11 h. DOI: 10.1038/s41598-022-15329-w
2045-2322
10.1038/s41598-022-15329-w
url https://hdl.handle.net/20.500.12008/39141
dc.language.iso.none.fl_str_mv en_US
eng
language_invalid_str_mv en_US
language eng
dc.relation.none.fl_str_mv Scientific Reports, 2022, 12: 11655.
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.license.none.fl_str_mv Licencia Creative Commons Atribución (CC - By 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv Licencia Creative Commons Atribución (CC - By 4.0)
dc.format.extent.es.fl_str_mv 11 h.
dc.format.mimetype.es.fl_str_mv application/pdf
dc.publisher.es.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:COLIBRI
instname:Universidad de la República
instacron:Universidad de la República
instname_str Universidad de la República
instacron_str Universidad de la República
institution Universidad de la República
reponame_str COLIBRI
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spelling Pazos Obregón Flavio, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Biología.Silvera Diego, IIBCECantera Rafael, IIBCEYankilevich PatricioGuerberoff Gustavo, Universidad de la República (Uruguay). Facultad de Ingeniería.Soto Pablo, IIBCE2023-08-10T12:24:40Z2023-08-10T12:24:40Z2022Pazos Obregón, F, Silvera, D, Cantera, R, [y otros autores]. "Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning". Scientific Reports. [en línea] 2022, 12: 11655. 11 h. DOI: 10.1038/s41598-022-15329-w2045-2322https://hdl.handle.net/20.500.12008/3914110.1038/s41598-022-15329-wThe function of most genes is unknown. The best results in automated function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data. Even though there is ample evidence showing that a gene’s function is not independent of its location, the few available examples of gene function prediction based on gene location rely on sequence identity between genes of different organisms and are thus subjected to the limitations of the relationship between sequence and function. Here we predict thousands of gene functions in five model eukaryotes (Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Homo sapiens) using machine learning models exclusively trained with features derived from the location of genes in the genomes to which they belong. Our aim was not to obtain the best performing method to automated function prediction but to explore the extent to which a gene's location can predict its function in eukaryotes. We found that our models outperform BLAST when predicting terms from Biological Process and Cellular Component Ontologies, showing that, at least in some cases, gene location alone can be more useful than sequence to infer gene function.Submitted by Farías Verónica (vfarias@fcien.edu.uy) on 2023-08-09T17:37:24Z No. of bitstreams: 2 license_rdf: 19875 bytes, checksum: 9fdbed07f52437945402c4e70fa4773e (MD5) 101038s4159802215329w.pdf: 2843720 bytes, checksum: 16c74945cbfcfca3a90ca505001f3028 (MD5)Approved for entry into archive by Faget Cecilia (lfaget@fcien.edu.uy) on 2023-08-09T17:46:04Z (GMT) No. of bitstreams: 2 license_rdf: 19875 bytes, checksum: 9fdbed07f52437945402c4e70fa4773e (MD5) 101038s4159802215329w.pdf: 2843720 bytes, checksum: 16c74945cbfcfca3a90ca505001f3028 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2023-08-10T12:24:40Z (GMT). No. of bitstreams: 2 license_rdf: 19875 bytes, checksum: 9fdbed07f52437945402c4e70fa4773e (MD5) 101038s4159802215329w.pdf: 2843720 bytes, checksum: 16c74945cbfcfca3a90ca505001f3028 (MD5) Previous issue date: 2022ANII: FSDA_1_2017_1_1424211 h.application/pdfen_USengSpringer NatureScientific Reports, 2022, 12: 11655.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. 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