Statistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species.

A major goal of bioinformatics is the characterization of transcription factors and the transcriptional programs they regulate. Given the speed of genome sequencing, we would like to quickly annotate regulatory sequences in newly-sequenced genomes. In such cases, it would be helpful to predict seque...

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Autores principales:	Pietro Liò, Claudia Angelini, Italia De Feis, Viet-Anh Nguyen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2801908567ec448c9d897ec7576a02f6
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spelling	oai:doaj.org-article:2801908567ec448c9d897ec7576a02f62021-11-18T07:06:08ZStatistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species.1932-620310.1371/journal.pone.0042489https://doaj.org/article/2801908567ec448c9d897ec7576a02f62012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22984403/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203A major goal of bioinformatics is the characterization of transcription factors and the transcriptional programs they regulate. Given the speed of genome sequencing, we would like to quickly annotate regulatory sequences in newly-sequenced genomes. In such cases, it would be helpful to predict sequence motifs by using experimental data from closely related model organism. Here we present a general algorithm that allow to identify transcription factor binding sites in one newly sequenced species by performing Bayesian regression on the annotated species. First we set the rationale of our method by applying it within the same species, then we extend it to use data available in closely related species. Finally, we generalise the method to handle the case when a certain number of experiments, from several species close to the species on which to make inference, are available. In order to show the performance of the method, we analyse three functionally related networks in the Ascomycota. Two gene network case studies are related to the G2/M phase of the Ascomycota cell cycle; the third is related to morphogenesis. We also compared the method with MatrixReduce and discuss other types of validation and tests. The first network is well known and provides a biological validation test of the method. The two cell cycle case studies, where the gene network size is conserved, demonstrate an effective utility in annotating new species sequences using all the available replicas from model species. The third case, where the gene network size varies among species, shows that the combination of information is less powerful but is still informative. Our methodology is quite general and could be extended to integrate other high-throughput data from model organisms.Pietro LiòClaudia AngeliniItalia De FeisViet-Anh NguyenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 9, p e42489 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pietro Liò Claudia Angelini Italia De Feis Viet-Anh Nguyen Statistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species.
description	A major goal of bioinformatics is the characterization of transcription factors and the transcriptional programs they regulate. Given the speed of genome sequencing, we would like to quickly annotate regulatory sequences in newly-sequenced genomes. In such cases, it would be helpful to predict sequence motifs by using experimental data from closely related model organism. Here we present a general algorithm that allow to identify transcription factor binding sites in one newly sequenced species by performing Bayesian regression on the annotated species. First we set the rationale of our method by applying it within the same species, then we extend it to use data available in closely related species. Finally, we generalise the method to handle the case when a certain number of experiments, from several species close to the species on which to make inference, are available. In order to show the performance of the method, we analyse three functionally related networks in the Ascomycota. Two gene network case studies are related to the G2/M phase of the Ascomycota cell cycle; the third is related to morphogenesis. We also compared the method with MatrixReduce and discuss other types of validation and tests. The first network is well known and provides a biological validation test of the method. The two cell cycle case studies, where the gene network size is conserved, demonstrate an effective utility in annotating new species sequences using all the available replicas from model species. The third case, where the gene network size varies among species, shows that the combination of information is less powerful but is still informative. Our methodology is quite general and could be extended to integrate other high-throughput data from model organisms.
format	article
author	Pietro Liò Claudia Angelini Italia De Feis Viet-Anh Nguyen
author_facet	Pietro Liò Claudia Angelini Italia De Feis Viet-Anh Nguyen
author_sort	Pietro Liò
title	Statistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species.
title_short	Statistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species.
title_full	Statistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species.
title_fullStr	Statistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species.
title_full_unstemmed	Statistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species.
title_sort	statistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/2801908567ec448c9d897ec7576a02f6
work_keys_str_mv	AT pietrolio statisticalapproachestouseamodelorganismforregulatorysequencesannotationofnewlysequencedspecies AT claudiaangelini statisticalapproachestouseamodelorganismforregulatorysequencesannotationofnewlysequencedspecies AT italiadefeis statisticalapproachestouseamodelorganismforregulatorysequencesannotationofnewlysequencedspecies AT vietanhnguyen statisticalapproachestouseamodelorganismforregulatorysequencesannotationofnewlysequencedspecies
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Statistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species.

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