Optimization of de novo transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms
Identifieur interne : 000944 ( Pmc/Curation ); précédent : 000943; suivant : 000945Optimization of de novo transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms
Auteurs : Berat Z. Haznedaroglu [États-Unis] ; Darryl Reeves [États-Unis] ; Hamid Rismani-Yazdi [États-Unis] ; Jordan Peccia [États-Unis]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2012.
Abstract
The
Analyses of single
This study demonstrated that different
Url:
DOI: 10.1186/1471-2105-13-170
PubMed: 22808927
PubMed Central: 3489510
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Optimization of <italic>de novo</italic> transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms</title><author><name sortKey="Haznedaroglu, Berat Z" sort="Haznedaroglu, Berat Z" uniqKey="Haznedaroglu B" first="Berat Z" last="Haznedaroglu">Berat Z. Haznedaroglu</name><affiliation wicri:level="1"><nlm:aff id="I1">Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511</wicri:regionArea></affiliation></author><author><name sortKey="Reeves, Darryl" sort="Reeves, Darryl" uniqKey="Reeves D" first="Darryl" last="Reeves">Darryl Reeves</name><affiliation wicri:level="1"><nlm:aff id="I2">Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511</wicri:regionArea></affiliation></author><author><name sortKey="Rismani Yazdi, Hamid" sort="Rismani Yazdi, Hamid" uniqKey="Rismani Yazdi H" first="Hamid" last="Rismani-Yazdi">Hamid Rismani-Yazdi</name><affiliation wicri:level="1"><nlm:aff id="I1">Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="I3">Now at the Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Now at the Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA</wicri:regionArea></affiliation></author><author><name sortKey="Peccia, Jordan" sort="Peccia, Jordan" uniqKey="Peccia J" first="Jordan" last="Peccia">Jordan Peccia</name><affiliation wicri:level="1"><nlm:aff id="I1">Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22808927</idno><idno type="pmc">3489510</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3489510</idno><idno type="RBID">PMC:3489510</idno><idno type="doi">10.1186/1471-2105-13-170</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000944</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000944</idno><idno type="wicri:Area/Pmc/Curation">000944</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000944</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Optimization of <italic>de novo</italic> transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms</title><author><name sortKey="Haznedaroglu, Berat Z" sort="Haznedaroglu, Berat Z" uniqKey="Haznedaroglu B" first="Berat Z" last="Haznedaroglu">Berat Z. Haznedaroglu</name><affiliation wicri:level="1"><nlm:aff id="I1">Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511</wicri:regionArea></affiliation></author><author><name sortKey="Reeves, Darryl" sort="Reeves, Darryl" uniqKey="Reeves D" first="Darryl" last="Reeves">Darryl Reeves</name><affiliation wicri:level="1"><nlm:aff id="I2">Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511</wicri:regionArea></affiliation></author><author><name sortKey="Rismani Yazdi, Hamid" sort="Rismani Yazdi, Hamid" uniqKey="Rismani Yazdi H" first="Hamid" last="Rismani-Yazdi">Hamid Rismani-Yazdi</name><affiliation wicri:level="1"><nlm:aff id="I1">Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="I3">Now at the Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Now at the Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA</wicri:regionArea></affiliation></author><author><name sortKey="Peccia, Jordan" sort="Peccia, Jordan" uniqKey="Peccia J" first="Jordan" last="Peccia">Jordan Peccia</name><affiliation wicri:level="1"><nlm:aff id="I1">Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511</wicri:regionArea></affiliation></author></analytic><series><title level="j">BMC Bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>The <italic>k</italic>-mer hash length is a key factor affecting the output of <italic>de novo</italic> transcriptome assembly packages using de Bruijn graph algorithms. Assemblies constructed with varying single <italic>k</italic>-mer choices might result in the loss of unique contiguous sequences (contigs) and relevant biological information. A common solution to this problem is the clustering of single <italic>k</italic>-mer assemblies. Even though annotation is one of the primary goals of a transcriptome assembly, the success of assembly strategies does not consider the impact of <italic>k</italic>-mer selection on the annotation output. This study provides an in-depth <italic>k</italic>-mer selection analysis that is focused on the degree of functional annotation achieved for a non-model organism where no reference genome information is available. Individual <italic>k</italic>-mers and clustered assemblies (CA) were considered using three representative software packages. Pair-wise comparison analyses (between individual <italic>k</italic>-mers and CAs) were produced to reveal missing Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog identifiers (KOIs), and to determine a strategy that maximizes the recovery of biological information in a <italic>de novo</italic> transcriptome assembly.</p></sec><sec><title>Results</title><p>Analyses of single <italic>k</italic>-mer assemblies resulted in the generation of various quantities of contigs and functional annotations within the selection window of <italic>k</italic>-mers (<italic>k-</italic>19 to <italic>k-</italic>63). For each <italic>k</italic>-mer in this window, generated assemblies contained certain unique contigs and KOIs that were not present in the other <italic>k</italic>-mer assemblies. Producing a non-redundant CA of <italic>k</italic>-mers 19 to 63 resulted in a more complete functional annotation than any single <italic>k</italic>-mer assembly. However, a fraction of unique annotations remained (~0.19 to 0.27% of total KOIs) in the assemblies of individual <italic>k</italic>-mers (<italic>k-</italic>19 to <italic>k-</italic>63) that were not present in the non-redundant CA. A workflow to recover these unique annotations is presented.</p></sec><sec><title>Conclusions</title><p>This study demonstrated that different <italic>k</italic>-mer choices result in various quantities of unique contigs per single <italic>k</italic>-mer assembly which affects biological information that is retrievable from the transcriptome. This undesirable effect can be minimized, but not eliminated, with clustering of multi-<italic>k</italic> assemblies with redundancy removal. The complete extraction of biological information in <italic>de novo</italic> transcriptomics studies requires both the production of a CA and efforts to identify unique contigs that are present in individual <italic>k</italic>-mer assemblies but not in the CA.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Iyer, Mk" uniqKey="Iyer M">MK Iyer</name></author><author><name sortKey="Chinnaiyan, Am" uniqKey="Chinnaiyan A">AM Chinnaiyan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Martin, Ja" uniqKey="Martin J">JA Martin</name></author><author><name sortKey="Wang, Z" uniqKey="Wang Z">Z Wang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="De Bruijn, Ng" uniqKey="De Bruijn N">NG De Bruijn</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Schulz, Mh" uniqKey="Schulz M">MH Schulz</name></author><author><name sortKey="Zerbino, 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<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Bioinformatics</journal-id><journal-id journal-id-type="iso-abbrev">BMC Bioinformatics</journal-id><journal-title-group><journal-title>BMC Bioinformatics</journal-title></journal-title-group><issn pub-type="epub">1471-2105</issn><publisher><publisher-name>BioMed Central</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22808927</article-id><article-id pub-id-type="pmc">3489510</article-id><article-id pub-id-type="publisher-id">1471-2105-13-170</article-id><article-id pub-id-type="doi">10.1186/1471-2105-13-170</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title>Optimization of <italic>de novo</italic> transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms</article-title></title-group><contrib-group><contrib contrib-type="author" id="A1"><name><surname>Haznedaroglu</surname><given-names>Berat Z</given-names></name><xref ref-type="aff" rid="I1">1</xref><email>berat.haznedaroglu@yale.edu</email></contrib><contrib contrib-type="author" id="A2"><name><surname>Reeves</surname><given-names>Darryl</given-names></name><xref ref-type="aff" rid="I2">2</xref><email>darryl.reeves@yale.edu</email></contrib><contrib contrib-type="author" id="A3"><name><surname>Rismani-Yazdi</surname><given-names>Hamid</given-names></name><xref ref-type="aff" rid="I1">1</xref><xref ref-type="aff" rid="I3">3</xref><email>hrismani@mit.edu</email></contrib><contrib contrib-type="author" corresp="yes" id="A4"><name><surname>Peccia</surname><given-names>Jordan</given-names></name><xref ref-type="aff" rid="I1">1</xref><email>jordan.peccia@yale.edu</email></contrib></contrib-group><aff id="I1"><label>1</label>Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA</aff><aff id="I2"><label>2</label>Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA</aff><aff id="I3"><label>3</label>Now at the Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA</aff><pub-date pub-type="collection"><year>2012</year></pub-date><pub-date pub-type="epub"><day>18</day><month>7</month><year>2012</year></pub-date><volume>13</volume><fpage>170</fpage><lpage>170</lpage><history><date date-type="received"><day>25</day><month>2</month><year>2012</year></date><date date-type="accepted"><day>26</day><month>6</month><year>2012</year></date></history><permissions><copyright-statement>Copyright ©2012 Haznedaroglu et al.; licensee BioMed Central Ltd.</copyright-statement><copyright-year>2012</copyright-year><copyright-holder>Haznedaroglu et al.; licensee BioMed Central Ltd.</copyright-holder><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0"><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/2.0">http://creativecommons.org/licenses/by/2.0</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri xlink:href="http://www.biomedcentral.com/1471-2105/13/170"></self-uri><abstract><sec><title>Background</title><p>The <italic>k</italic>-mer hash length is a key factor affecting the output of <italic>de novo</italic> transcriptome assembly packages using de Bruijn graph algorithms. Assemblies constructed with varying single <italic>k</italic>-mer choices might result in the loss of unique contiguous sequences (contigs) and relevant biological information. A common solution to this problem is the clustering of single <italic>k</italic>-mer assemblies. Even though annotation is one of the primary goals of a transcriptome assembly, the success of assembly strategies does not consider the impact of <italic>k</italic>-mer selection on the annotation output. This study provides an in-depth <italic>k</italic>-mer selection analysis that is focused on the degree of functional annotation achieved for a non-model organism where no reference genome information is available. Individual <italic>k</italic>-mers and clustered assemblies (CA) were considered using three representative software packages. Pair-wise comparison analyses (between individual <italic>k</italic>-mers and CAs) were produced to reveal missing Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog identifiers (KOIs), and to determine a strategy that maximizes the recovery of biological information in a <italic>de novo</italic> transcriptome assembly.</p></sec><sec><title>Results</title><p>Analyses of single <italic>k</italic>-mer assemblies resulted in the generation of various quantities of contigs and functional annotations within the selection window of <italic>k</italic>-mers (<italic>k-</italic>19 to <italic>k-</italic>63). For each <italic>k</italic>-mer in this window, generated assemblies contained certain unique contigs and KOIs that were not present in the other <italic>k</italic>-mer assemblies. Producing a non-redundant CA of <italic>k</italic>-mers 19 to 63 resulted in a more complete functional annotation than any single <italic>k</italic>-mer assembly. However, a fraction of unique annotations remained (~0.19 to 0.27% of total KOIs) in the assemblies of individual <italic>k</italic>-mers (<italic>k-</italic>19 to <italic>k-</italic>63) that were not present in the non-redundant CA. A workflow to recover these unique annotations is presented.</p></sec><sec><title>Conclusions</title><p>This study demonstrated that different <italic>k</italic>-mer choices result in various quantities of unique contigs per single <italic>k</italic>-mer assembly which affects biological information that is retrievable from the transcriptome. This undesirable effect can be minimized, but not eliminated, with clustering of multi-<italic>k</italic> assemblies with redundancy removal. The complete extraction of biological information in <italic>de novo</italic> transcriptomics studies requires both the production of a CA and efforts to identify unique contigs that are present in individual <italic>k</italic>-mer assemblies but not in the CA.</p></sec></abstract></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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|texte= Optimization of de novo transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms
}}
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HfdIndexSelect -h $EXPLOR_AREA/Data/Pmc/Curation/RBID.i -Sk "pubmed:22808927" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Pmc/Curation/biblio.hfd \
| NlmPubMed2Wicri -a MersV1
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