Statistical Approaches to Use a Model Organism for Regulatory Sequences Annotation of Newly Sequenced Species (Articolo in rivista)

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  • Statistical Approaches to Use a Model Organism for Regulatory Sequences Annotation of Newly Sequenced Species (Articolo in rivista) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1371/journal.pone.0042489 (literal)
Alternative label
  • Pietro Liò1*, Claudia Angelini2, Italia De Feis2, Viet-Anh Nguyen1 (2012)
    Statistical Approaches to Use a Model Organism for Regulatory Sequences Annotation of Newly Sequenced Species
    in PloS one
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Pietro Liò1*, Claudia Angelini2, Italia De Feis2, Viet-Anh Nguyen1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0042489 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 13 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • e42489 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopus (literal)
  • PubMed (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1 Computer Laboratory, University of Cambridge, Cambridge, United Kingdom, 2 Istituto per le Applicazioni del Calcolo \"Mauro Picone\" (CNR), Napoli, Italy (literal)
Titolo
  • Statistical Approaches to Use a Model Organism for Regulatory Sequences Annotation of Newly Sequenced Species (literal)
Abstract
  • 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. (literal)
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