Optimization of de novo transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms.
Identifieur interne : 001D65 ( PubMed/Curation ); précédent : 001D64; suivant : 001D66Optimization 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 ; Hamid Rismani-Yazdi ; Jordan PecciaSource :
- BMC bioinformatics [ 1471-2105 ] ; 2012.
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Abstract
The k-mer hash length is a key factor affecting the output of de novo transcriptome assembly packages using de Bruijn graph algorithms. Assemblies constructed with varying single k-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 k-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 k-mer selection on the annotation output. This study provides an in-depth k-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 k-mers and clustered assemblies (CA) were considered using three representative software packages. Pair-wise comparison analyses (between individual k-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 de novo transcriptome assembly.
DOI: 10.1186/1471-2105-13-170
PubMed: 22808927
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Haznedaroglu</name><affiliation wicri:level="1"><nlm:affiliation>Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA.</nlm:affiliation><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></author><author><name sortKey="Rismani Yazdi, Hamid" sort="Rismani Yazdi, Hamid" uniqKey="Rismani Yazdi H" first="Hamid" last="Rismani-Yazdi">Hamid Rismani-Yazdi</name></author><author><name sortKey="Peccia, Jordan" sort="Peccia, Jordan" uniqKey="Peccia J" first="Jordan" last="Peccia">Jordan Peccia</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="RBID">pubmed:22808927</idno><idno type="pmid">22808927</idno><idno type="doi">10.1186/1471-2105-13-170</idno><idno type="wicri:Area/PubMed/Corpus">001D65</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001D65</idno><idno type="wicri:Area/PubMed/Curation">001D65</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001D65</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Optimization of de novo 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:affiliation>Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA.</nlm:affiliation><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></author><author><name sortKey="Rismani Yazdi, Hamid" sort="Rismani Yazdi, Hamid" uniqKey="Rismani Yazdi H" first="Hamid" last="Rismani-Yazdi">Hamid Rismani-Yazdi</name></author><author><name sortKey="Peccia, Jordan" sort="Peccia, Jordan" uniqKey="Peccia J" first="Jordan" last="Peccia">Jordan Peccia</name></author></analytic><series><title level="j">BMC bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Gene Expression Profiling (methods)</term><term>Genome</term><term>High-Throughput Nucleotide Sequencing</term><term>Molecular Sequence Annotation</term><term>Sequence Analysis, DNA</term><term>Software</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Analyse de profil d'expression de gènes ()</term><term>Analyse de séquence d'ADN</term><term>Annotation de séquence moléculaire</term><term>Génome</term><term>Logiciel</term><term>Séquençage nucléotidique à haut débit</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Gene Expression Profiling</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Genome</term><term>High-Throughput Nucleotide Sequencing</term><term>Molecular Sequence Annotation</term><term>Sequence Analysis, DNA</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de profil d'expression de gènes</term><term>Analyse de séquence d'ADN</term><term>Annotation de séquence moléculaire</term><term>Génome</term><term>Logiciel</term><term>Séquençage nucléotidique à haut débit</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The k-mer hash length is a key factor affecting the output of de novo transcriptome assembly packages using de Bruijn graph algorithms. Assemblies constructed with varying single k-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 k-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 k-mer selection on the annotation output. This study provides an in-depth k-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 k-mers and clustered assemblies (CA) were considered using three representative software packages. Pair-wise comparison analyses (between individual k-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 de novo transcriptome assembly.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">22808927</PMID><DateCompleted><Year>2013</Year><Month>06</Month><Day>28</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>02</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2105</ISSN><JournalIssue CitedMedium="Internet"><Volume>13</Volume><PubDate><Year>2012</Year><Month>Jul</Month><Day>18</Day></PubDate></JournalIssue><Title>BMC bioinformatics</Title><ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>Optimization of de novo transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms.</ArticleTitle><Pagination><MedlinePgn>170</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1471-2105-13-170</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">The k-mer hash length is a key factor affecting the output of de novo transcriptome assembly packages using de Bruijn graph algorithms. Assemblies constructed with varying single k-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 k-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 k-mer selection on the annotation output. This study provides an in-depth k-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 k-mers and clustered assemblies (CA) were considered using three representative software packages. Pair-wise comparison analyses (between individual k-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 de novo transcriptome assembly.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Analyses of single k-mer assemblies resulted in the generation of various quantities of contigs and functional annotations within the selection window of k-mers (k-19 to k-63). For each k-mer in this window, generated assemblies contained certain unique contigs and KOIs that were not present in the other k-mer assemblies. Producing a non-redundant CA of k-mers 19 to 63 resulted in a more complete functional annotation than any single k-mer assembly. However, a fraction of unique annotations remained (~0.19 to 0.27% of total KOIs) in the assemblies of individual k-mers (k-19 to k-63) that were not present in the non-redundant CA. A workflow to recover these unique annotations is presented.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">This study demonstrated that different k-mer choices result in various quantities of unique contigs per single k-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-k assemblies with redundancy removal. The complete extraction of biological information in de novo transcriptomics studies requires both the production of a CA and efforts to identify unique contigs that are present in individual k-mer assemblies but not in the CA.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Haznedaroglu</LastName><ForeName>Berat Z</ForeName><Initials>BZ</Initials><AffiliationInfo><Affiliation>Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Reeves</LastName><ForeName>Darryl</ForeName><Initials>D</Initials></Author><Author ValidYN="Y"><LastName>Rismani-Yazdi</LastName><ForeName>Hamid</ForeName><Initials>H</Initials></Author><Author ValidYN="Y"><LastName>Peccia</LastName><ForeName>Jordan</ForeName><Initials>J</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>RR19895</GrantID><Acronym>RR</Acronym><Agency>NCRR NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>T15 LM07056</GrantID><Acronym>LM</Acronym><Agency>NLM NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2012</Year><Month>07</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Bioinformatics</MedlineTA><NlmUniqueID>100965194</NlmUniqueID><ISSNLinking>1471-2105</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName 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