Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling
Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by th...
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creator | Lindgreen, Stinus Umu, Sinan Uğur Lai, Alicia Sook-Wei Eldai, Hisham Liu, Wenting McGimpsey, Stephanie Wheeler, Nicole E Biggs, Patrick J Thomson, Nick R Barquist, Lars Poole, Anthony M Gardner, Paul P |
description | Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling. |
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While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1003907</identifier><identifier>PMID: 25357249</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Annotations ; Antisense RNA ; Archaea - genetics ; Bacteria ; Bacteria - genetics ; Bias ; Biology and Life Sciences ; Cluster Analysis ; Computational Biology ; Databases, Genetic ; Datasets ; Experiments ; Gene Expression Profiling - methods ; Genomes ; Genomics ; Noise ; Phylogenetics ; Phylogeny ; Proteins ; Quality control ; RNA sequencing ; RNA, Archaeal - chemistry ; RNA, Archaeal - classification ; RNA, Archaeal - genetics ; RNA, Bacterial - chemistry ; RNA, Bacterial - classification ; RNA, Bacterial - genetics ; RNA, Untranslated - chemistry ; RNA, Untranslated - classification ; RNA, Untranslated - genetics ; Studies ; Transcriptome - genetics</subject><ispartof>PLoS computational biology, 2014-10, Vol.10 (10), p.e1003907-e1003907</ispartof><rights>COPYRIGHT 2014 Public Library of Science</rights><rights>2014 Lindgreen et al 2014 Lindgreen et al</rights><rights>2014 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Lindgreen S, Umu SU, Lai AS-W, Eldai H, Liu W, McGimpsey S, et al. (2014) Robust Identification of Noncoding RNA from Transcriptomes Requires Phylogenetically-Informed Sampling. 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Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.</description><subject>Analysis</subject><subject>Annotations</subject><subject>Antisense RNA</subject><subject>Archaea - genetics</subject><subject>Bacteria</subject><subject>Bacteria - genetics</subject><subject>Bias</subject><subject>Biology and Life Sciences</subject><subject>Cluster Analysis</subject><subject>Computational Biology</subject><subject>Databases, Genetic</subject><subject>Datasets</subject><subject>Experiments</subject><subject>Gene Expression Profiling - methods</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Noise</subject><subject>Phylogenetics</subject><subject>Phylogeny</subject><subject>Proteins</subject><subject>Quality control</subject><subject>RNA sequencing</subject><subject>RNA, Archaeal - chemistry</subject><subject>RNA, Archaeal - classification</subject><subject>RNA, Archaeal - genetics</subject><subject>RNA, Bacterial - chemistry</subject><subject>RNA, Bacterial - classification</subject><subject>RNA, Bacterial - genetics</subject><subject>RNA, Untranslated - chemistry</subject><subject>RNA, Untranslated - classification</subject><subject>RNA, Untranslated - genetics</subject><subject>Studies</subject><subject>Transcriptome - genetics</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>DOA</sourceid><recordid>eNqVUk1vEzEUXCEQLYV_gGCP9JBgrz_3ghRVFCJVRQpwtrz-2Dry2lt7tyL_HoekVXNEPvjpeWY8b_Sq6j0ES4gY_LyNcwrSL0fVuSUEALWAvajOISFowRDhL5_VZ9WbnLcFQ3hLX1dnDUGENbg9r-wmdnOeaqdNmJx1Sk4uhjraOsSgonahrze3q9qmONRTkiGr5MYpDibXydzPLpVivNv52JtgpsL3frdwwcY0GF1nOYy-aLytXlnps3l3vC-q39dff119X9z8-La-Wt0sFAVkWmBIEGKAIaoQVgqbRjEOmW6AbTXA2mjV2kZixDUHjCnZIsaN6Tg1hQIwuqg-HnRHH7M4RpQFpJwABFtIC2J9QOgot2JMbpBpJ6J04l8jpl7IVObwRoCm04ZxpjpKMSWQE2kb22FCKJYANUXry_G3uSvTqhJhkv5E9PQluDvRxweBG1hUSBH4dBRI8X42eRKDy8p4L4OJ8943bIttRPa-lwdoL4u1fcBFUZWjzeBUDMa60l8h3nIAKGgL4fKEUDCT-TP1cs5ZrH9u_gN7e4rFB6xKMedk7NO8EIj9Zj7GLvabKY6bWWgfnmf1RHpcRfQX2Tfh6w</recordid><startdate>20141001</startdate><enddate>20141001</enddate><creator>Lindgreen, Stinus</creator><creator>Umu, Sinan Uğur</creator><creator>Lai, Alicia Sook-Wei</creator><creator>Eldai, Hisham</creator><creator>Liu, Wenting</creator><creator>McGimpsey, Stephanie</creator><creator>Wheeler, Nicole E</creator><creator>Biggs, Patrick J</creator><creator>Thomson, Nick R</creator><creator>Barquist, Lars</creator><creator>Poole, Anthony M</creator><creator>Gardner, Paul P</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20141001</creationdate><title>Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling</title><author>Lindgreen, Stinus ; Umu, Sinan Uğur ; Lai, Alicia Sook-Wei ; Eldai, Hisham ; Liu, Wenting ; McGimpsey, Stephanie ; Wheeler, Nicole E ; Biggs, Patrick J ; Thomson, Nick R ; Barquist, Lars ; Poole, Anthony M ; Gardner, Paul P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c605t-4153370736c34cc4e2c7817d20f9d04dedc9f2a438d8077ca9378eeb86e36c043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Analysis</topic><topic>Annotations</topic><topic>Antisense RNA</topic><topic>Archaea - genetics</topic><topic>Bacteria</topic><topic>Bacteria - genetics</topic><topic>Bias</topic><topic>Biology and Life Sciences</topic><topic>Cluster Analysis</topic><topic>Computational Biology</topic><topic>Databases, Genetic</topic><topic>Datasets</topic><topic>Experiments</topic><topic>Gene Expression Profiling - methods</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Noise</topic><topic>Phylogenetics</topic><topic>Phylogeny</topic><topic>Proteins</topic><topic>Quality control</topic><topic>RNA sequencing</topic><topic>RNA, Archaeal - chemistry</topic><topic>RNA, Archaeal - classification</topic><topic>RNA, Archaeal - genetics</topic><topic>RNA, Bacterial - chemistry</topic><topic>RNA, Bacterial - classification</topic><topic>RNA, Bacterial - genetics</topic><topic>RNA, Untranslated - chemistry</topic><topic>RNA, Untranslated - classification</topic><topic>RNA, Untranslated - genetics</topic><topic>Studies</topic><topic>Transcriptome - genetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lindgreen, Stinus</creatorcontrib><creatorcontrib>Umu, Sinan Uğur</creatorcontrib><creatorcontrib>Lai, Alicia Sook-Wei</creatorcontrib><creatorcontrib>Eldai, Hisham</creatorcontrib><creatorcontrib>Liu, Wenting</creatorcontrib><creatorcontrib>McGimpsey, Stephanie</creatorcontrib><creatorcontrib>Wheeler, Nicole E</creatorcontrib><creatorcontrib>Biggs, Patrick J</creatorcontrib><creatorcontrib>Thomson, Nick R</creatorcontrib><creatorcontrib>Barquist, Lars</creatorcontrib><creatorcontrib>Poole, Anthony M</creatorcontrib><creatorcontrib>Gardner, Paul P</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lindgreen, Stinus</au><au>Umu, Sinan Uğur</au><au>Lai, Alicia Sook-Wei</au><au>Eldai, Hisham</au><au>Liu, Wenting</au><au>McGimpsey, Stephanie</au><au>Wheeler, Nicole E</au><au>Biggs, Patrick J</au><au>Thomson, Nick R</au><au>Barquist, Lars</au><au>Poole, Anthony M</au><au>Gardner, Paul P</au><au>Chen, Kevin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2014-10-01</date><risdate>2014</risdate><volume>10</volume><issue>10</issue><spage>e1003907</spage><epage>e1003907</epage><pages>e1003907-e1003907</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25357249</pmid><doi>10.1371/journal.pcbi.1003907</doi><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Annotations Antisense RNA Archaea - genetics Bacteria Bacteria - genetics Bias Biology and Life Sciences Cluster Analysis Computational Biology Databases, Genetic Datasets Experiments Gene Expression Profiling - methods Genomes Genomics Noise Phylogenetics Phylogeny Proteins Quality control RNA sequencing RNA, Archaeal - chemistry RNA, Archaeal - classification RNA, Archaeal - genetics RNA, Bacterial - chemistry RNA, Bacterial - classification RNA, Bacterial - genetics RNA, Untranslated - chemistry RNA, Untranslated - classification RNA, Untranslated - genetics Studies Transcriptome - genetics |
title | Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling |
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