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Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2
sRNAs are small, non-coding RNA species that control numerous cellular processes. Although it iswidely accepted that sRNAs are encoded by most if not all bacteria, genome-wide annotations for sRNA-encoding genes have been conducted in only a few of the nearly 300 bacterial species sequenced to date....
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2006
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1524904/ https://www.ncbi.nlm.nih.gov/pubmed/16870723 http://dx.doi.org/10.1093/nar/gkl453 |
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author | Livny, Jonathan Brencic, Anja Lory, Stephen Waldor, Matthew K. |
author_facet | Livny, Jonathan Brencic, Anja Lory, Stephen Waldor, Matthew K. |
author_sort | Livny, Jonathan |
collection | PubMed |
description | sRNAs are small, non-coding RNA species that control numerous cellular processes. Although it iswidely accepted that sRNAs are encoded by most if not all bacteria, genome-wide annotations for sRNA-encoding genes have been conducted in only a few of the nearly 300 bacterial species sequenced to date. To facilitate the efficient annotation of bacterial genomes for sRNA-encoding genes, we developed a program, sRNAPredict2, that identifies putative sRNAs by searching for co-localization of genetic features commonly associated with sRNA-encoding genes. Using sRNAPredict2, we conducted genome-wide annotations for putative sRNA-encoding genes in the intergenic regions of 11 diverse pathogens. In total, 2759 previously unannotated candidate sRNA loci were predicted. There was considerable range in the number of sRNAs predicted in the different pathogens analyzed, raising the possibility that there are species-specific differences in the reliance on sRNA-mediated regulation. Of 34 previously unannotated sRNAs predicted in the opportunistic pathogen Pseudomonas aeruginosa, 31 were experimentally tested and 17 were found to encode sRNA transcripts. Our findings suggest that numerous genes have been missed in the current annotations of bacterial genomes and that, by using improved bioinformatic approaches and tools, much remains to be discovered in ‘intergenic’ sequences. |
format | Text |
id | pubmed-1524904 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-15249042006-08-09 Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2 Livny, Jonathan Brencic, Anja Lory, Stephen Waldor, Matthew K. Nucleic Acids Res Article sRNAs are small, non-coding RNA species that control numerous cellular processes. Although it iswidely accepted that sRNAs are encoded by most if not all bacteria, genome-wide annotations for sRNA-encoding genes have been conducted in only a few of the nearly 300 bacterial species sequenced to date. To facilitate the efficient annotation of bacterial genomes for sRNA-encoding genes, we developed a program, sRNAPredict2, that identifies putative sRNAs by searching for co-localization of genetic features commonly associated with sRNA-encoding genes. Using sRNAPredict2, we conducted genome-wide annotations for putative sRNA-encoding genes in the intergenic regions of 11 diverse pathogens. In total, 2759 previously unannotated candidate sRNA loci were predicted. There was considerable range in the number of sRNAs predicted in the different pathogens analyzed, raising the possibility that there are species-specific differences in the reliance on sRNA-mediated regulation. Of 34 previously unannotated sRNAs predicted in the opportunistic pathogen Pseudomonas aeruginosa, 31 were experimentally tested and 17 were found to encode sRNA transcripts. Our findings suggest that numerous genes have been missed in the current annotations of bacterial genomes and that, by using improved bioinformatic approaches and tools, much remains to be discovered in ‘intergenic’ sequences. Oxford University Press 2006 2006-07-27 /pmc/articles/PMC1524904/ /pubmed/16870723 http://dx.doi.org/10.1093/nar/gkl453 Text en © 2006 The Author(s) |
spellingShingle | Article Livny, Jonathan Brencic, Anja Lory, Stephen Waldor, Matthew K. Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2 |
title | Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2 |
title_full | Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2 |
title_fullStr | Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2 |
title_full_unstemmed | Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2 |
title_short | Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2 |
title_sort | identification of 17 pseudomonas aeruginosa srnas and prediction of srna-encoding genes in 10 diverse pathogens using the bioinformatic tool srnapredict2 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1524904/ https://www.ncbi.nlm.nih.gov/pubmed/16870723 http://dx.doi.org/10.1093/nar/gkl453 |
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