In silico read normalization using set multi-cover optimization.
Identifieur interne : 000864 ( PubMed/Curation ); précédent : 000863; suivant : 000865In silico read normalization using set multi-cover optimization.
Auteurs : Dilip A. Durai [Allemagne] ; Marcel H. Schulz [Allemagne]Source :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2018.
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Abstract
De Bruijn graphs are a common assembly data structure for sequencing datasets. But with the advances in sequencing technologies, assembling high coverage datasets has become a computational challenge. Read normalization, which removes redundancy in datasets, is widely applied to reduce resource requirements. Current normalization algorithms, though efficient, provide no guarantee to preserve important k-mers that form connections between regions in the graph.
DOI: 10.1093/bioinformatics/bty307
PubMed: 29912280
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">In silico read normalization using set multi-cover optimization.</title><author><name sortKey="Durai, Dilip A" sort="Durai, Dilip A" uniqKey="Durai D" first="Dilip A" last="Durai">Dilip A. Durai</name><affiliation wicri:level="1"><nlm:affiliation>Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken</wicri:regionArea></affiliation></author><author><name sortKey="Schulz, Marcel H" sort="Schulz, Marcel H" uniqKey="Schulz M" first="Marcel H" last="Schulz">Marcel H. Schulz</name><affiliation wicri:level="1"><nlm:affiliation>Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2018">2018</date><idno type="RBID">pubmed:29912280</idno><idno type="pmid">29912280</idno><idno type="doi">10.1093/bioinformatics/bty307</idno><idno type="wicri:Area/PubMed/Corpus">000864</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000864</idno><idno type="wicri:Area/PubMed/Curation">000864</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000864</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">In silico read normalization using set multi-cover optimization.</title><author><name sortKey="Durai, Dilip A" sort="Durai, Dilip A" uniqKey="Durai D" first="Dilip A" last="Durai">Dilip A. Durai</name><affiliation wicri:level="1"><nlm:affiliation>Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken</wicri:regionArea></affiliation></author><author><name sortKey="Schulz, Marcel H" sort="Schulz, Marcel H" uniqKey="Schulz M" first="Marcel H" last="Schulz">Marcel H. Schulz</name><affiliation wicri:level="1"><nlm:affiliation>Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken</wicri:regionArea></affiliation></author></analytic><series><title level="j">Bioinformatics (Oxford, England)</title><idno type="eISSN">1367-4811</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>Computational Biology</term><term>Computer Simulation</term><term>Sequence Analysis, RNA</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ARN</term><term>Biologie informatique</term><term>Simulation numérique</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Computational Biology</term><term>Computer Simulation</term><term>Sequence Analysis, RNA</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ARN</term><term>Biologie informatique</term><term>Simulation numérique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">De Bruijn graphs are a common assembly data structure for sequencing datasets. But with the advances in sequencing technologies, assembling high coverage datasets has become a computational challenge. Read normalization, which removes redundancy in datasets, is widely applied to reduce resource requirements. Current normalization algorithms, though efficient, provide no guarantee to preserve important k-mers that form connections between regions in the graph.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">29912280</PMID><DateCompleted><Year>2019</Year><Month>10</Month><Day>21</Day></DateCompleted><DateRevised><Year>2019</Year><Month>10</Month><Day>22</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1367-4811</ISSN><JournalIssue CitedMedium="Internet"><Volume>34</Volume><Issue>19</Issue><PubDate><Year>2018</Year><Month>10</Month><Day>01</Day></PubDate></JournalIssue><Title>Bioinformatics (Oxford, England)</Title><ISOAbbreviation>Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>In silico read normalization using set multi-cover optimization.</ArticleTitle><Pagination><MedlinePgn>3273-3280</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bioinformatics/bty307</ELocationID><Abstract><AbstractText Label="Motivation">De Bruijn graphs are a common assembly data structure for sequencing datasets. But with the advances in sequencing technologies, assembling high coverage datasets has become a computational challenge. Read normalization, which removes redundancy in datasets, is widely applied to reduce resource requirements. Current normalization algorithms, though efficient, provide no guarantee to preserve important k-mers that form connections between regions in the graph.</AbstractText><AbstractText Label="Results">Here, normalization is phrased as a set multi-cover problem on reads and a heuristic algorithm, Optimized Read Normalization Algorithm (ORNA), is proposed. ORNA normalizes to the minimum number of reads required to retain all k-mers and their relative k-mer abundances from the original dataset. Hence, all connections from the original graph are preserved. ORNA was tested on various RNA-seq datasets with different coverage values. It was compared to the current normalization algorithms and was found to be performing better. Normalizing error corrected data allows for more accurate assemblies compared to the normalized uncorrected dataset. Further, an application is proposed in which multiple datasets are combined and normalized to predict novel transcripts that would have been missed otherwise. Finally, ORNA is a general purpose normalization algorithm that is fast and significantly reduces datasets with loss of assembly quality in between [1, 30]% depending on reduction stringency.</AbstractText><AbstractText Label="Availability and implementation">ORNA is available at https://github.com/SchulzLab/ORNA.</AbstractText><AbstractText Label="Supplementary information">Supplementary data are available at Bioinformatics online.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Durai</LastName><ForeName>Dilip A</ForeName><Initials>DA</Initials><AffiliationInfo><Affiliation>Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken, Germany.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Saarbrücken Graduate School of Computer Science, Saarland University, Saarbrücken, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Schulz</LastName><ForeName>Marcel H</ForeName><Initials>MH</Initials><AffiliationInfo><Affiliation>Cluster of Excellence on Multimodal Computing and Interaction, Saarland University, Saarbrücken, Germany.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. 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