Functional knowledge transfer for high-accuracy prediction of under-studied biological processes.

A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning) that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathwa...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Christopher Y Park, Aaron K Wong, Casey S Greene, Jessica Rowland, Yuanfang Guan, Lars A Bongo, Rebecca D Burdine, Olga G Troyanskaya
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2013
Materias:
Acceso en línea:https://doaj.org/article/4b1046dff387432187875d3b5d68dd39
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:4b1046dff387432187875d3b5d68dd39
record_format dspace
spelling oai:doaj.org-article:4b1046dff387432187875d3b5d68dd392021-11-18T05:52:21ZFunctional knowledge transfer for high-accuracy prediction of under-studied biological processes.1553-734X1553-735810.1371/journal.pcbi.1002957https://doaj.org/article/4b1046dff387432187875d3b5d68dd392013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23516347/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning) that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator's organism of interest. Sequence-based search methods (e.g. BLAST) have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between organisms. Our method transfers annotations only when functionally appropriate as determined by genomic data and can be used with any prediction algorithm to combine transferred gene function knowledge with organism-specific high-throughput data to enable accurate function prediction. We show that diverse state-of-art machine learning algorithms leveraging functional knowledge transfer (FKT) dramatically improve their accuracy in predicting gene-pathway membership, particularly for processes with little experimental knowledge in an organism. We also show that our method compares favorably to annotation transfer by sequence similarity. Next, we deploy FKT with state-of-the-art SVM classifier to predict novel genes to 11,000 biological processes across six diverse organisms and expand the coverage of accurate function predictions to processes that are often ignored because of a dearth of annotated genes in an organism. Finally, we perform in vivo experimental investigation in Danio rerio and confirm the regulatory role of our top predicted novel gene, wnt5b, in leftward cell migration during heart development. FKT is immediately applicable to many bioinformatics techniques and will help biologists systematically integrate prior knowledge from diverse systems to direct targeted experiments in their organism of study.Christopher Y ParkAaron K WongCasey S GreeneJessica RowlandYuanfang GuanLars A BongoRebecca D BurdineOlga G TroyanskayaPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 3, p e1002957 (2013)
institution DOAJ
collection DOAJ
language EN
topic Biology (General)
QH301-705.5
spellingShingle Biology (General)
QH301-705.5
Christopher Y Park
Aaron K Wong
Casey S Greene
Jessica Rowland
Yuanfang Guan
Lars A Bongo
Rebecca D Burdine
Olga G Troyanskaya
Functional knowledge transfer for high-accuracy prediction of under-studied biological processes.
description A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning) that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator's organism of interest. Sequence-based search methods (e.g. BLAST) have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between organisms. Our method transfers annotations only when functionally appropriate as determined by genomic data and can be used with any prediction algorithm to combine transferred gene function knowledge with organism-specific high-throughput data to enable accurate function prediction. We show that diverse state-of-art machine learning algorithms leveraging functional knowledge transfer (FKT) dramatically improve their accuracy in predicting gene-pathway membership, particularly for processes with little experimental knowledge in an organism. We also show that our method compares favorably to annotation transfer by sequence similarity. Next, we deploy FKT with state-of-the-art SVM classifier to predict novel genes to 11,000 biological processes across six diverse organisms and expand the coverage of accurate function predictions to processes that are often ignored because of a dearth of annotated genes in an organism. Finally, we perform in vivo experimental investigation in Danio rerio and confirm the regulatory role of our top predicted novel gene, wnt5b, in leftward cell migration during heart development. FKT is immediately applicable to many bioinformatics techniques and will help biologists systematically integrate prior knowledge from diverse systems to direct targeted experiments in their organism of study.
format article
author Christopher Y Park
Aaron K Wong
Casey S Greene
Jessica Rowland
Yuanfang Guan
Lars A Bongo
Rebecca D Burdine
Olga G Troyanskaya
author_facet Christopher Y Park
Aaron K Wong
Casey S Greene
Jessica Rowland
Yuanfang Guan
Lars A Bongo
Rebecca D Burdine
Olga G Troyanskaya
author_sort Christopher Y Park
title Functional knowledge transfer for high-accuracy prediction of under-studied biological processes.
title_short Functional knowledge transfer for high-accuracy prediction of under-studied biological processes.
title_full Functional knowledge transfer for high-accuracy prediction of under-studied biological processes.
title_fullStr Functional knowledge transfer for high-accuracy prediction of under-studied biological processes.
title_full_unstemmed Functional knowledge transfer for high-accuracy prediction of under-studied biological processes.
title_sort functional knowledge transfer for high-accuracy prediction of under-studied biological processes.
publisher Public Library of Science (PLoS)
publishDate 2013
url https://doaj.org/article/4b1046dff387432187875d3b5d68dd39
work_keys_str_mv AT christopherypark functionalknowledgetransferforhighaccuracypredictionofunderstudiedbiologicalprocesses
AT aaronkwong functionalknowledgetransferforhighaccuracypredictionofunderstudiedbiologicalprocesses
AT caseysgreene functionalknowledgetransferforhighaccuracypredictionofunderstudiedbiologicalprocesses
AT jessicarowland functionalknowledgetransferforhighaccuracypredictionofunderstudiedbiologicalprocesses
AT yuanfangguan functionalknowledgetransferforhighaccuracypredictionofunderstudiedbiologicalprocesses
AT larsabongo functionalknowledgetransferforhighaccuracypredictionofunderstudiedbiologicalprocesses
AT rebeccadburdine functionalknowledgetransferforhighaccuracypredictionofunderstudiedbiologicalprocesses
AT olgagtroyanskaya functionalknowledgetransferforhighaccuracypredictionofunderstudiedbiologicalprocesses
_version_ 1718424723997589504