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Pregled bibliografske jedinice broj: 602539

Phyletic Profiling With Cliques of Orthologs is Enhanced by Signatures of Paralogy Relationships


Škunca, Nives; Bošnjak, Matko; Kriško, Anita; Panov, Panče; Džeroski, Sašo; Šmuc, Tomislav; Supek, Fran
Phyletic Profiling With Cliques of Orthologs is Enhanced by Signatures of Paralogy Relationships // Plos computational biology, 9 (2012), 1; e100285, 14 doi:10.1371/journal.pcbi.1002852 (međunarodna recenzija, članak, znanstveni)


Naslov
Phyletic Profiling With Cliques of Orthologs is Enhanced by Signatures of Paralogy Relationships

Autori
Škunca, Nives ; Bošnjak, Matko ; Kriško, Anita ; Panov, Panče ; Džeroski, Sašo ; Šmuc, Tomislav ; Supek, Fran

Izvornik
Plos computational biology (1553-734X) 9 (2012), 1; E100285, 14

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Phyletic profiles ; orthologs ; paralogs ; random forest ; gene function prediction ; functional annotation

Sažetak
New microbial genomes are sequenced at a high pace, allowing insight into the genetics of not only cultured microbes, but a wide range of metagenomic collections such as the human microbiome. To understand the deluge of genomic data we face, computational approaches for gene functional annotation are invaluable. We introduce a novel model for computational annotation that refines two established concepts: annotation based on homology and annotation based on phyletic profiling. The phyletic profiling-based model that includes both inferred orthologs and paralogs—homologs separated by a speciation and a duplication event, respectively—provides more annotations at the same average Precision than the model that includes only inferred orthologs. For experimental validation, we selected 38 poorly annotated Escherichia coli genes for which the model assigned one of three GO terms with high confidence: involvement in DNA repair, protein translation, or cell wall synthesis. Results of antibiotic stress survival assays on E. coli knockout mutants showed high agreement with our model's estimates of accuracy: out of 38 predictions obtained at the reported Precision of 60%, we confirmed 25 predictions, indicating that our confidence estimates can be used to make informed decisions on experimental validation. Our work will contribute to making experimental validation of computational predictions more approachable, both in cost and time. Our predictions for 998 prokaryotic genomes include ~400000 specific annotations with the estimated Precision of 90%, ~19000 of which are highly specific—e.g. "penicillin binding, " "tRNA aminoacylation for protein translation, " or "pathogenesis"—and are freely available at http://gorbi.irb.hr/

Izvorni jezik
Engleski

Znanstvena područja
Biologija, Računarstvo, Biotehnologija



POVEZANOST RADA


Projekt / tema
098-0000000-3168 - Strojno učenje prediktivnih modela u računalnoj biologiji (Tomislav Šmuc, )

Ustanove
Institut "Ruđer Bošković", Zagreb,
Mediteranski institut za istraživanje života

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


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