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Evolutionary analysis across mammals reveals distinct classes of long non-coding RNAs
BACKGROUND: Recent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across many species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain u...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4739325/ https://www.ncbi.nlm.nih.gov/pubmed/26838501 http://dx.doi.org/10.1186/s13059-016-0880-9 |
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author | Chen, Jenny Shishkin, Alexander A. Zhu, Xiaopeng Kadri, Sabah Maza, Itay Guttman, Mitchell Hanna, Jacob H. Regev, Aviv Garber, Manuel |
author_facet | Chen, Jenny Shishkin, Alexander A. Zhu, Xiaopeng Kadri, Sabah Maza, Itay Guttman, Mitchell Hanna, Jacob H. Regev, Aviv Garber, Manuel |
author_sort | Chen, Jenny |
collection | PubMed |
description | BACKGROUND: Recent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across many species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: identifying truly non-coding genes from de novo reconstructed transcriptomes, and prioritizing the hundreds of resulting putative lncRNAs for downstream experimental interrogation. RESULTS: We present slncky, a lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-sequencing data and further uses evolutionary constraint to prioritize lncRNAs that are likely to be functionally important. Our automated filtering pipeline is comparable to manual curation efforts and more sensitive than previously published computational approaches. Furthermore, we developed a sensitive alignment pipeline for aligning lncRNA loci and propose new evolutionary metrics relevant for analyzing sequence and transcript evolution. Our analysis reveals that evolutionary selection acts in several distinct patterns, and uncovers two notable classes of intergenic lncRNAs: one showing strong purifying selection on RNA sequence and another where constraint is restricted to the regulation but not the sequence of the transcript. CONCLUSION: Our results highlight that lncRNAs are not a homogenous class of molecules but rather a mixture of multiple functional classes with distinct biological mechanism and/or roles. Our novel comparative methods for lncRNAs reveals 233 constrained lncRNAs out of tens of thousands of currently annotated transcripts, which we make available through the slncky Evolution Browser. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-016-0880-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4739325 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-47393252016-02-04 Evolutionary analysis across mammals reveals distinct classes of long non-coding RNAs Chen, Jenny Shishkin, Alexander A. Zhu, Xiaopeng Kadri, Sabah Maza, Itay Guttman, Mitchell Hanna, Jacob H. Regev, Aviv Garber, Manuel Genome Biol Research BACKGROUND: Recent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across many species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: identifying truly non-coding genes from de novo reconstructed transcriptomes, and prioritizing the hundreds of resulting putative lncRNAs for downstream experimental interrogation. RESULTS: We present slncky, a lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-sequencing data and further uses evolutionary constraint to prioritize lncRNAs that are likely to be functionally important. Our automated filtering pipeline is comparable to manual curation efforts and more sensitive than previously published computational approaches. Furthermore, we developed a sensitive alignment pipeline for aligning lncRNA loci and propose new evolutionary metrics relevant for analyzing sequence and transcript evolution. Our analysis reveals that evolutionary selection acts in several distinct patterns, and uncovers two notable classes of intergenic lncRNAs: one showing strong purifying selection on RNA sequence and another where constraint is restricted to the regulation but not the sequence of the transcript. CONCLUSION: Our results highlight that lncRNAs are not a homogenous class of molecules but rather a mixture of multiple functional classes with distinct biological mechanism and/or roles. Our novel comparative methods for lncRNAs reveals 233 constrained lncRNAs out of tens of thousands of currently annotated transcripts, which we make available through the slncky Evolution Browser. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-016-0880-9) contains supplementary material, which is available to authorized users. BioMed Central 2016-02-02 2016 /pmc/articles/PMC4739325/ /pubmed/26838501 http://dx.doi.org/10.1186/s13059-016-0880-9 Text en © Chen et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Chen, Jenny Shishkin, Alexander A. Zhu, Xiaopeng Kadri, Sabah Maza, Itay Guttman, Mitchell Hanna, Jacob H. Regev, Aviv Garber, Manuel Evolutionary analysis across mammals reveals distinct classes of long non-coding RNAs |
title | Evolutionary analysis across mammals reveals distinct classes of long non-coding RNAs |
title_full | Evolutionary analysis across mammals reveals distinct classes of long non-coding RNAs |
title_fullStr | Evolutionary analysis across mammals reveals distinct classes of long non-coding RNAs |
title_full_unstemmed | Evolutionary analysis across mammals reveals distinct classes of long non-coding RNAs |
title_short | Evolutionary analysis across mammals reveals distinct classes of long non-coding RNAs |
title_sort | evolutionary analysis across mammals reveals distinct classes of long non-coding rnas |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4739325/ https://www.ncbi.nlm.nih.gov/pubmed/26838501 http://dx.doi.org/10.1186/s13059-016-0880-9 |
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