STAble: a novel approach to de novo assembly of RNA-seq data and its application in a metabolic model network based metatranscriptomic workflow.
Identifieur interne : 000824 ( PubMed/Curation ); précédent : 000823; suivant : 000825STAble: a novel approach to de novo assembly of RNA-seq data and its application in a metabolic model network based metatranscriptomic workflow.
Auteurs : Igor Saggese [Italie] ; Elisa Bona [Italie] ; Max Conway [Royaume-Uni] ; Francesco Favero [Italie] ; Marco Ladetto [Italie] ; Pietro Li [Royaume-Uni] ; Giovanni Manzini [Italie] ; Flavio Mignone [Italie]Source :
- BMC bioinformatics [ 1471-2105 ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
- génétique : ARN messager, Transcriptome, Voies et réseaux métaboliques.
- métabolisme : ARN messager, Méthane.
- Algorithmes, Analyse de séquence d'ARN, Animaux, Flux de travaux, Humains, Logiciel, Modèles génétiques, Ruminants.
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : RNA, Messenger.
- chemical , metabolism : Methane, RNA, Messenger.
- genetics : Metabolic Networks and Pathways, Transcriptome.
- methods : Sequence Analysis, RNA.
- Algorithms, Animals, Humans, Models, Genetic, Ruminants, Software, Workflow.
Abstract
De novo assembly of RNA-seq data allows the study of transcriptome in absence of a reference genome either if data is obtained from a single organism or from a mixed sample as in metatranscriptomics studies. Given the high number of sequences obtained from NGS approaches, a critical step in any analysis workflow is the assembly of reads to reconstruct transcripts thus reducing the complexity of the analysis. Despite many available tools show a good sensitivity, there is a high percentage of false positives due to the high number of assemblies considered and it is likely that the high frequency of false positive is underestimated by currently used benchmarks. The reconstruction of not existing transcripts may false the biological interpretation of results as - for example - may overestimate the identification of "novel" transcripts. Moreover, benchmarks performed are usually based on RNA-seq data from annotated genomes and assembled transcripts are compared to annotations and genomes to identify putative good and wrong reconstructions, but these tests alone may lead to accept a particular type of false positive as true, as better described below.
DOI: 10.1186/s12859-018-2174-6
PubMed: 30066630
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pubmed:30066630Le document en format XML
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Igor" uniqKey="Saggese I" first="Igor" last="Saggese">Igor Saggese</name><affiliation wicri:level="1"><nlm:affiliation>Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria</wicri:regionArea></affiliation></author><author><name sortKey="Bona, Elisa" sort="Bona, Elisa" uniqKey="Bona E" first="Elisa" last="Bona">Elisa Bona</name><affiliation wicri:level="1"><nlm:affiliation>Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria</wicri:regionArea></affiliation></author><author><name sortKey="Conway, Max" sort="Conway, Max" uniqKey="Conway M" first="Max" last="Conway">Max Conway</name><affiliation wicri:level="1"><nlm:affiliation>Computer Laboratory, University of Cambridge, Cambridge, CB2 1TN, UK.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Computer Laboratory, University of Cambridge, Cambridge, CB2 1TN</wicri:regionArea></affiliation></author><author><name sortKey="Favero, Francesco" sort="Favero, Francesco" uniqKey="Favero F" first="Francesco" last="Favero">Francesco Favero</name><affiliation wicri:level="1"><nlm:affiliation>Dipartimento di Scienze della Salute, Università degli Studi del Piemonte Orientale, 28100, Novara, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Dipartimento di Scienze della Salute, Università degli Studi del Piemonte Orientale, 28100, Novara</wicri:regionArea></affiliation></author><author><name sortKey="Ladetto, Marco" sort="Ladetto, Marco" uniqKey="Ladetto M" first="Marco" last="Ladetto">Marco Ladetto</name><affiliation wicri:level="1"><nlm:affiliation>AO SS Antonio e Biagio e Cesare Arrigo, 15121, Alessandria, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>AO SS Antonio e Biagio e Cesare Arrigo, 15121, Alessandria</wicri:regionArea></affiliation></author><author><name sortKey="Li, Pietro" sort="Li, Pietro" uniqKey="Li P" first="Pietro" last="Li">Pietro Li</name><affiliation wicri:level="1"><nlm:affiliation>Computer Laboratory, University of Cambridge, Cambridge, CB2 1TN, UK.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Computer Laboratory, University of Cambridge, Cambridge, CB2 1TN</wicri:regionArea></affiliation></author><author><name sortKey="Manzini, Giovanni" sort="Manzini, Giovanni" uniqKey="Manzini G" first="Giovanni" last="Manzini">Giovanni Manzini</name><affiliation wicri:level="1"><nlm:affiliation>Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria</wicri:regionArea></affiliation></author><author><name sortKey="Mignone, Flavio" sort="Mignone, Flavio" uniqKey="Mignone F" first="Flavio" last="Mignone">Flavio Mignone</name><affiliation wicri:level="1"><nlm:affiliation>Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria, Italy. flavio.mignone@uniupo.it.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria</wicri:regionArea></affiliation></author></analytic><series><title level="j">BMC bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Humans</term><term>Metabolic Networks and Pathways (genetics)</term><term>Methane (metabolism)</term><term>Models, Genetic</term><term>RNA, Messenger (genetics)</term><term>RNA, Messenger (metabolism)</term><term>Ruminants</term><term>Sequence Analysis, RNA (methods)</term><term>Software</term><term>Transcriptome (genetics)</term><term>Workflow</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>ARN messager (génétique)</term><term>ARN messager (métabolisme)</term><term>Algorithmes</term><term>Analyse de séquence d'ARN ()</term><term>Animaux</term><term>Flux de travaux</term><term>Humains</term><term>Logiciel</term><term>Modèles génétiques</term><term>Méthane (métabolisme)</term><term>Ruminants</term><term>Transcriptome (génétique)</term><term>Voies et réseaux métaboliques (génétique)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>RNA, Messenger</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Methane</term><term>RNA, Messenger</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Metabolic Networks and Pathways</term><term>Transcriptome</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>ARN messager</term><term>Transcriptome</term><term>Voies et réseaux métaboliques</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Sequence Analysis, RNA</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>ARN messager</term><term>Méthane</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Humans</term><term>Models, Genetic</term><term>Ruminants</term><term>Software</term><term>Workflow</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ARN</term><term>Animaux</term><term>Flux de travaux</term><term>Humains</term><term>Logiciel</term><term>Modèles génétiques</term><term>Ruminants</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">De novo assembly of RNA-seq data allows the study of transcriptome in absence of a reference genome either if data is obtained from a single organism or from a mixed sample as in metatranscriptomics studies. Given the high number of sequences obtained from NGS approaches, a critical step in any analysis workflow is the assembly of reads to reconstruct transcripts thus reducing the complexity of the analysis. Despite many available tools show a good sensitivity, there is a high percentage of false positives due to the high number of assemblies considered and it is likely that the high frequency of false positive is underestimated by currently used benchmarks. The reconstruction of not existing transcripts may false the biological interpretation of results as - for example - may overestimate the identification of "novel" transcripts. Moreover, benchmarks performed are usually based on RNA-seq data from annotated genomes and assembled transcripts are compared to annotations and genomes to identify putative good and wrong reconstructions, but these tests alone may lead to accept a particular type of false positive as true, as better described below.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">30066630</PMID><DateCompleted><Year>2019</Year><Month>05</Month><Day>17</Day></DateCompleted><DateRevised><Year>2019</Year><Month>05</Month><Day>17</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2105</ISSN><JournalIssue CitedMedium="Internet"><Volume>19</Volume><Issue>Suppl 7</Issue><PubDate><Year>2018</Year><Month>07</Month><Day>09</Day></PubDate></JournalIssue><Title>BMC bioinformatics</Title><ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>STAble: a novel approach to de novo assembly of RNA-seq data and its application in a metabolic model network based metatranscriptomic workflow.</ArticleTitle><Pagination><MedlinePgn>184</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12859-018-2174-6</ELocationID><Abstract><AbstractText Label="BACKGROUND">De novo assembly of RNA-seq data allows the study of transcriptome in absence of a reference genome either if data is obtained from a single organism or from a mixed sample as in metatranscriptomics studies. Given the high number of sequences obtained from NGS approaches, a critical step in any analysis workflow is the assembly of reads to reconstruct transcripts thus reducing the complexity of the analysis. Despite many available tools show a good sensitivity, there is a high percentage of false positives due to the high number of assemblies considered and it is likely that the high frequency of false positive is underestimated by currently used benchmarks. The reconstruction of not existing transcripts may false the biological interpretation of results as - for example - may overestimate the identification of "novel" transcripts. Moreover, benchmarks performed are usually based on RNA-seq data from annotated genomes and assembled transcripts are compared to annotations and genomes to identify putative good and wrong reconstructions, but these tests alone may lead to accept a particular type of false positive as true, as better described below.</AbstractText><AbstractText Label="RESULTS">Here we present a novel methodology of de novo assembly, implemented in a software named STAble (Short-reads Transcriptome Assembler). The novel concept of this assembler is that the whole reads are used to determine possible alignments instead of using smaller k-mers, with the aim of reducing the number of chimeras produced. Furthermore, we applied a new set of benchmarks based on simulated data to better define the performance of assembly method and carefully identifying true reconstructions. STAble was also used to build a prototype workflow to analyse metatranscriptomics data in connection to a steady state metabolic modelling algorithm. This algorithm was used to produce high quality metabolic interpretations of small gene expression sets obtained from already published RNA-seq data that we assembled with STAble.</AbstractText><AbstractText Label="CONCLUSIONS">The presented results, albeit preliminary, clearly suggest that with this approach is possible to identify informative reactions not directly revealed by raw transcriptomic data.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Saggese</LastName><ForeName>Igor</ForeName><Initials>I</Initials><AffiliationInfo><Affiliation>Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bona</LastName><ForeName>Elisa</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Conway</LastName><ForeName>Max</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Computer Laboratory, University of Cambridge, Cambridge, CB2 1TN, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Favero</LastName><ForeName>Francesco</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Dipartimento di Scienze della Salute, Università degli Studi del Piemonte Orientale, 28100, Novara, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ladetto</LastName><ForeName>Marco</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>AO SS Antonio e Biagio e Cesare Arrigo, 15121, Alessandria, Italy.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Dipartimento di Biotecnologie e Scienze per la Salute, Università di Torino, 10124, Torino, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liò</LastName><ForeName>Pietro</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Computer Laboratory, University of Cambridge, Cambridge, CB2 1TN, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Manzini</LastName><ForeName>Giovanni</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria, Italy.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Istituto di Informatica e Telematica, CNR, 56124, Pisa, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mignone</LastName><ForeName>Flavio</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Dipartimento di Scienze e Innovazione Tecnologica, Università degli Studi del Piemonte Orientale, 15121, Alessandria, Italy. flavio.mignone@uniupo.it.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>07</Month><Day>09</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Bioinformatics</MedlineTA><NlmUniqueID>100965194</NlmUniqueID><ISSNLinking>1471-2105</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D012333">RNA, Messenger</NameOfSubstance></Chemical><Chemical><RegistryNumber>OP0UW79H66</RegistryNumber><NameOfSubstance UI="D008697">Methane</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" 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