Reverse Transcription Errors and RNA–DNA Differences at Short Tandem Repeats
Identifieur interne : 000742 ( Ncbi/Merge ); précédent : 000741; suivant : 000743Reverse Transcription Errors and RNA–DNA Differences at Short Tandem Repeats
Auteurs : Arkarachai Fungtammasan [États-Unis] ; Marta Tomaszkiewicz [États-Unis] ; Rebeca Campos-Sánchez [États-Unis] ; Kristin A. Eckert [États-Unis] ; Michael Degiorgio [États-Unis] ; Kateryna D. Makova [États-Unis]Source :
- Molecular Biology and Evolution [ 0737-4038 ] ; 2016.
Abstract
Transcript variation has important implications for organismal function in health and disease. Most transcriptome studies focus on assessing variation in gene expression levels and isoform representation. Variation at the level of transcript sequence is caused by RNA editing and transcription errors, and leads to nongenetically encoded transcript variants, or RNA–DNA differences (RDDs). Such variation has been understudied, in part because its detection is obscured by reverse transcription (RT) and sequencing errors. It has only been evaluated for intertranscript base substitution differences. Here, we investigated transcript sequence variation for short tandem repeats (STRs). We developed the first maximum-likelihood estimator (MLE) to infer RT error and RDD rates, taking next generation sequencing error rates into account. Using the MLE, we empirically evaluated RT error and RDD rates for STRs in a large-scale DNA and RNA replicated sequencing experiment conducted in a primate species. The RT error rates increased exponentially with STR length and were biased toward expansions. The RDD rates were approximately 1 order of magnitude lower than the RT error rates. The RT error rates estimated with the MLE from a primate data set were concordant with those estimated with an independent method, barcoded RNA sequencing, from a
Url:
DOI: 10.1093/molbev/msw139
PubMed: 27413049
PubMed Central: 5026258
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<front><div type="abstract" xml:lang="en"><p>Transcript variation has important implications for organismal function in health and disease. Most transcriptome studies focus on assessing variation in gene expression levels and isoform representation. Variation at the level of transcript sequence is caused by RNA editing and transcription errors, and leads to nongenetically encoded transcript variants, or RNA–DNA differences (RDDs). Such variation has been understudied, in part because its detection is obscured by reverse transcription (RT) and sequencing errors. It has only been evaluated for intertranscript base substitution differences. Here, we investigated transcript sequence variation for short tandem repeats (STRs). We developed the first maximum-likelihood estimator (MLE) to infer RT error and RDD rates, taking next generation sequencing error rates into account. Using the MLE, we empirically evaluated RT error and RDD rates for STRs in a large-scale DNA and RNA replicated sequencing experiment conducted in a primate species. The RT error rates increased exponentially with STR length and were biased toward expansions. The RDD rates were approximately 1 order of magnitude lower than the RT error rates. The RT error rates estimated with the MLE from a primate data set were concordant with those estimated with an independent method, barcoded RNA sequencing, from a <italic>Caenorhabditis elegans</italic>
data set. Our results have important implications for medical genomics, as STR allelic variation is associated with >40 diseases. STR nonallelic transcript variation can also contribute to disease phenotype. The MLE and empirical rates presented here can be used to evaluate the probability of disease-associated transcripts arising due to RDD.</p>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Mol Biol Evol</journal-id>
<journal-id journal-id-type="iso-abbrev">Mol. Biol. Evol</journal-id>
<journal-id journal-id-type="publisher-id">molbev</journal-id>
<journal-id journal-id-type="hwp">molbiolevol</journal-id>
<journal-title-group><journal-title>Molecular Biology and Evolution</journal-title>
</journal-title-group>
<issn pub-type="ppub">0737-4038</issn>
<issn pub-type="epub">1537-1719</issn>
<publisher><publisher-name>Oxford University Press</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">27413049</article-id>
<article-id pub-id-type="pmc">5026258</article-id>
<article-id pub-id-type="doi">10.1093/molbev/msw139</article-id>
<article-id pub-id-type="publisher-id">msw139</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Methods</subject>
</subj-group>
</article-categories>
<title-group><article-title>Reverse Transcription Errors and RNA–DNA Differences at Short Tandem Repeats</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Fungtammasan</surname>
<given-names>Arkarachai</given-names>
</name>
<xref ref-type="aff" rid="msw139-aff1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="msw139-aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="msw139-aff3"><sup>3</sup>
</xref>
<xref ref-type="aff" rid="msw139-aff4"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Tomaszkiewicz</surname>
<given-names>Marta</given-names>
</name>
<xref ref-type="aff" rid="msw139-aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="msw139-aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Campos-Sánchez</surname>
<given-names>Rebeca</given-names>
</name>
<xref ref-type="author-notes" rid="msw139-FM1"><sup>‡</sup>
</xref>
<xref ref-type="aff" rid="msw139-aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="msw139-aff3"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Eckert</surname>
<given-names>Kristin A.</given-names>
</name>
<xref ref-type="aff" rid="msw139-aff3"><sup>3</sup>
</xref>
<xref ref-type="aff" rid="msw139-aff5"><sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>DeGiorgio</surname>
<given-names>Michael</given-names>
</name>
<xref ref-type="corresp" rid="msw139-cor1">*</xref>
<xref ref-type="aff" rid="msw139-aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="msw139-aff3"><sup>3</sup>
</xref>
<xref ref-type="aff" rid="msw139-aff6"><sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Makova</surname>
<given-names>Kateryna D.</given-names>
</name>
<xref ref-type="corresp" rid="msw139-cor1">*</xref>
<xref ref-type="aff" rid="msw139-aff2"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="msw139-aff3"><sup>3</sup>
</xref>
<xref ref-type="aff" rid="msw139-aff4"><sup>4</sup>
</xref>
</contrib>
<aff id="msw139-aff1"><sup>1</sup>
Integrative Biosciences, Bioinformatics and Genomics Option, Pennsylvania State University</aff>
<aff id="msw139-aff2"><sup>2</sup>
Department of Biology, Pennsylvania State University</aff>
<aff id="msw139-aff3"><sup>3</sup>
Center for Medical Genomics, Pennsylvania State University</aff>
<aff id="msw139-aff4"><sup>4</sup>
Huck Institute of Genome Sciences, Pennsylvania State University</aff>
<aff id="msw139-aff5"><sup>5</sup>
Department of Pathology, The Jake Gittlen Laboratories for Cancer Research, The Pennsylvania State University College of Medicine</aff>
<aff id="msw139-aff6"><sup>6</sup>
Institute for CyberScience, Pennsylvania State University</aff>
</contrib-group>
<author-notes><fn id="msw139-FM1"><p><sup>‡</sup>
Present address: Centro De Investigación En Biología Celular Y Molecular, Universidad De Costa Rica, San José, Costa Rica</p>
</fn>
<corresp id="msw139-cor1">*<bold>Corresponding author:</bold>
E-mail: <email>kdm16@psu.edu</email>
; <email>mxd60@psu.edu</email>
.</corresp>
<fn id="msw139-FM2"><p><bold>Associate editor:</bold>
Claus Wilke</p>
</fn>
</author-notes>
<pub-date pub-type="ppub"><month>10</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="epub"><day>12</day>
<month>7</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>12</day>
<month>7</month>
<year>2016</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on the . </pmc-comment>
<volume>33</volume>
<issue>10</issue>
<fpage>2744</fpage>
<lpage>2758</lpage>
<permissions><copyright-statement>© The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.</copyright-statement>
<copyright-year>2016</copyright-year>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/" license-type="creative-commons"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
</license>
</permissions>
<abstract><p>Transcript variation has important implications for organismal function in health and disease. Most transcriptome studies focus on assessing variation in gene expression levels and isoform representation. Variation at the level of transcript sequence is caused by RNA editing and transcription errors, and leads to nongenetically encoded transcript variants, or RNA–DNA differences (RDDs). Such variation has been understudied, in part because its detection is obscured by reverse transcription (RT) and sequencing errors. It has only been evaluated for intertranscript base substitution differences. Here, we investigated transcript sequence variation for short tandem repeats (STRs). We developed the first maximum-likelihood estimator (MLE) to infer RT error and RDD rates, taking next generation sequencing error rates into account. Using the MLE, we empirically evaluated RT error and RDD rates for STRs in a large-scale DNA and RNA replicated sequencing experiment conducted in a primate species. The RT error rates increased exponentially with STR length and were biased toward expansions. The RDD rates were approximately 1 order of magnitude lower than the RT error rates. The RT error rates estimated with the MLE from a primate data set were concordant with those estimated with an independent method, barcoded RNA sequencing, from a <italic>Caenorhabditis elegans</italic>
data set. Our results have important implications for medical genomics, as STR allelic variation is associated with >40 diseases. STR nonallelic transcript variation can also contribute to disease phenotype. The MLE and empirical rates presented here can be used to evaluate the probability of disease-associated transcripts arising due to RDD.</p>
</abstract>
<kwd-group kwd-group-type="author"><kwd>microsatellites</kwd>
<kwd>tandem repeats</kwd>
<kwd>RNA sequencing</kwd>
<kwd>RNA–DNA differences</kwd>
<kwd>transcription errors</kwd>
<kwd>reverse transcription errors</kwd>
<kwd>sequencing errors</kwd>
<kwd>error correction model.</kwd>
</kwd-group>
<counts><page-count count="15"></page-count>
</counts>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Pennsylvanie</li>
</region>
<settlement><li>University Park (Pennsylvanie)</li>
</settlement>
<orgName><li>Université d'État de Pennsylvanie</li>
</orgName>
</list>
<tree><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Fungtammasan, Arkarachai" sort="Fungtammasan, Arkarachai" uniqKey="Fungtammasan A" first="Arkarachai" last="Fungtammasan">Arkarachai Fungtammasan</name>
</region>
<name sortKey="Campos Sanchez, Rebeca" sort="Campos Sanchez, Rebeca" uniqKey="Campos Sanchez R" first="Rebeca" last="Campos-Sánchez">Rebeca Campos-Sánchez</name>
<name sortKey="Campos Sanchez, Rebeca" sort="Campos Sanchez, Rebeca" uniqKey="Campos Sanchez R" first="Rebeca" last="Campos-Sánchez">Rebeca Campos-Sánchez</name>
<name sortKey="Degiorgio, Michael" sort="Degiorgio, Michael" uniqKey="Degiorgio M" first="Michael" last="Degiorgio">Michael Degiorgio</name>
<name sortKey="Degiorgio, Michael" sort="Degiorgio, Michael" uniqKey="Degiorgio M" first="Michael" last="Degiorgio">Michael Degiorgio</name>
<name sortKey="Degiorgio, Michael" sort="Degiorgio, Michael" uniqKey="Degiorgio M" first="Michael" last="Degiorgio">Michael Degiorgio</name>
<name sortKey="Eckert, Kristin A" sort="Eckert, Kristin A" uniqKey="Eckert K" first="Kristin A." last="Eckert">Kristin A. Eckert</name>
<name sortKey="Fungtammasan, Arkarachai" sort="Fungtammasan, Arkarachai" uniqKey="Fungtammasan A" first="Arkarachai" last="Fungtammasan">Arkarachai Fungtammasan</name>
<name sortKey="Fungtammasan, Arkarachai" sort="Fungtammasan, Arkarachai" uniqKey="Fungtammasan A" first="Arkarachai" last="Fungtammasan">Arkarachai Fungtammasan</name>
<name sortKey="Fungtammasan, Arkarachai" sort="Fungtammasan, Arkarachai" uniqKey="Fungtammasan A" first="Arkarachai" last="Fungtammasan">Arkarachai Fungtammasan</name>
<name sortKey="Makova, Kateryna D" sort="Makova, Kateryna D" uniqKey="Makova K" first="Kateryna D." last="Makova">Kateryna D. Makova</name>
<name sortKey="Makova, Kateryna D" sort="Makova, Kateryna D" uniqKey="Makova K" first="Kateryna D." last="Makova">Kateryna D. Makova</name>
<name sortKey="Makova, Kateryna D" sort="Makova, Kateryna D" uniqKey="Makova K" first="Kateryna D." last="Makova">Kateryna D. Makova</name>
<name sortKey="Tomaszkiewicz, Marta" sort="Tomaszkiewicz, Marta" uniqKey="Tomaszkiewicz M" first="Marta" last="Tomaszkiewicz">Marta Tomaszkiewicz</name>
<name sortKey="Tomaszkiewicz, Marta" sort="Tomaszkiewicz, Marta" uniqKey="Tomaszkiewicz M" first="Marta" last="Tomaszkiewicz">Marta Tomaszkiewicz</name>
</country>
</tree>
</affiliations>
</record>
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