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Computational identification of functional RNA homologs in metagenomic data

A key step toward understanding a metagenomics data set is the identification of functional sequence elements within it, such as protein coding genes and structural RNAs. Relative to protein coding genes, structural RNAs are more difficult to identify because of their reduced alphabet size, lack of...

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Autores principales: Nawrocki, Eric P., Eddy, Sean R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Landes Bioscience 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3849165/
https://www.ncbi.nlm.nih.gov/pubmed/23722291
http://dx.doi.org/10.4161/rna.25038
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author Nawrocki, Eric P.
Eddy, Sean R.
author_facet Nawrocki, Eric P.
Eddy, Sean R.
author_sort Nawrocki, Eric P.
collection PubMed
description A key step toward understanding a metagenomics data set is the identification of functional sequence elements within it, such as protein coding genes and structural RNAs. Relative to protein coding genes, structural RNAs are more difficult to identify because of their reduced alphabet size, lack of open reading frames, and short length. Infernal is a software package that implements “covariance models” (CMs) for RNA homology search, which harness both sequence and structural conservation when searching for RNA homologs. Thanks to the added statistical signal inherent in the secondary structure conservation of many RNA families, Infernal is more powerful than sequence-only based methods such as BLAST and profile HMMs. Together with the Rfam database of CMs, Infernal is a useful tool for identifying RNAs in metagenomics data sets.
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spelling pubmed-38491652013-12-12 Computational identification of functional RNA homologs in metagenomic data Nawrocki, Eric P. Eddy, Sean R. RNA Biol Review A key step toward understanding a metagenomics data set is the identification of functional sequence elements within it, such as protein coding genes and structural RNAs. Relative to protein coding genes, structural RNAs are more difficult to identify because of their reduced alphabet size, lack of open reading frames, and short length. Infernal is a software package that implements “covariance models” (CMs) for RNA homology search, which harness both sequence and structural conservation when searching for RNA homologs. Thanks to the added statistical signal inherent in the secondary structure conservation of many RNA families, Infernal is more powerful than sequence-only based methods such as BLAST and profile HMMs. Together with the Rfam database of CMs, Infernal is a useful tool for identifying RNAs in metagenomics data sets. Landes Bioscience 2013-07-01 2013-05-20 /pmc/articles/PMC3849165/ /pubmed/23722291 http://dx.doi.org/10.4161/rna.25038 Text en Copyright © 2013 Landes Bioscience http://creativecommons.org/licenses/by-nc/3.0/ This is an open-access article licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License. The article may be redistributed, reproduced, and reused for non-commercial purposes, provided the original source is properly cited.
spellingShingle Review
Nawrocki, Eric P.
Eddy, Sean R.
Computational identification of functional RNA homologs in metagenomic data
title Computational identification of functional RNA homologs in metagenomic data
title_full Computational identification of functional RNA homologs in metagenomic data
title_fullStr Computational identification of functional RNA homologs in metagenomic data
title_full_unstemmed Computational identification of functional RNA homologs in metagenomic data
title_short Computational identification of functional RNA homologs in metagenomic data
title_sort computational identification of functional rna homologs in metagenomic data
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3849165/
https://www.ncbi.nlm.nih.gov/pubmed/23722291
http://dx.doi.org/10.4161/rna.25038
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