Estimating the k-mer Coverage Frequencies in Genomic Datasets: A Comparative Assessment of the State-of-the-art.
Identifieur interne : 000574 ( PubMed/Checkpoint ); précédent : 000573; suivant : 000575Estimating the k-mer Coverage Frequencies in Genomic Datasets: A Comparative Assessment of the State-of-the-art.
Auteurs : Swati C. Manekar [Inde] ; Shailesh R. Sathe [Inde]Source :
- Current genomics [ 1389-2029 ] ; 2019.
Abstract
In bioinformatics, estimation of k-mer abundance histograms or just enumerat-ing the number of unique k-mers and the number of singletons are desirable in many genome sequence analysis applications. The applications include predicting genome sizes, data pre-processing for de Bruijn graph assembly methods (tune runtime parameters for analysis tools), repeat detection, sequenc-ing coverage estimation, measuring sequencing error rates, etc. Different methods for cardinality estima-tion in sequencing data have been developed in recent years.
DOI: 10.2174/1389202919666181026101326
PubMed: 31015787
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Estimating the <i>k</i>-mer Coverage Frequencies in Genomic Datasets: A Comparative Assessment of the State-of-the-art.</title><author><name sortKey="Manekar, Swati C" sort="Manekar, Swati C" uniqKey="Manekar S" first="Swati C" last="Manekar">Swati C. Manekar</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur</wicri:regionArea><wicri:noRegion>Nagpur</wicri:noRegion></affiliation></author><author><name sortKey="Sathe, Shailesh R" sort="Sathe, Shailesh R" uniqKey="Sathe S" first="Shailesh R" last="Sathe">Shailesh R. Sathe</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur</wicri:regionArea><wicri:noRegion>Nagpur</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2019">2019</date><idno type="RBID">pubmed:31015787</idno><idno type="pmid">31015787</idno><idno type="doi">10.2174/1389202919666181026101326</idno><idno type="wicri:Area/PubMed/Corpus">000557</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000557</idno><idno type="wicri:Area/PubMed/Curation">000557</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000557</idno><idno type="wicri:Area/PubMed/Checkpoint">000574</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000574</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Estimating the <i>k</i>-mer Coverage Frequencies in Genomic Datasets: A Comparative Assessment of the State-of-the-art.</title><author><name sortKey="Manekar, Swati C" sort="Manekar, Swati C" uniqKey="Manekar S" first="Swati C" last="Manekar">Swati C. Manekar</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur</wicri:regionArea><wicri:noRegion>Nagpur</wicri:noRegion></affiliation></author><author><name sortKey="Sathe, Shailesh R" sort="Sathe, Shailesh R" uniqKey="Sathe S" first="Shailesh R" last="Sathe">Shailesh R. Sathe</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur</wicri:regionArea><wicri:noRegion>Nagpur</wicri:noRegion></affiliation></author></analytic><series><title level="j">Current genomics</title><idno type="ISSN">1389-2029</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In bioinformatics, estimation of k-mer abundance histograms or just enumerat-ing the number of unique k-mers and the number of singletons are desirable in many genome sequence analysis applications. The applications include predicting genome sizes, data pre-processing for de Bruijn graph assembly methods (tune runtime parameters for analysis tools), repeat detection, sequenc-ing coverage estimation, measuring sequencing error rates, etc. Different methods for cardinality estima-tion in sequencing data have been developed in recent years.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">31015787</PMID><DateRevised><Year>2020</Year><Month>02</Month><Day>25</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">1389-2029</ISSN><JournalIssue CitedMedium="Print"><Volume>20</Volume><Issue>1</Issue><PubDate><Year>2019</Year><Month>Jan</Month></PubDate></JournalIssue><Title>Current genomics</Title><ISOAbbreviation>Curr. Genomics</ISOAbbreviation></Journal><ArticleTitle>Estimating the <i>k</i>-mer Coverage Frequencies in Genomic Datasets: A Comparative Assessment of the State-of-the-art.</ArticleTitle><Pagination><MedlinePgn>2-15</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.2174/1389202919666181026101326</ELocationID><Abstract><AbstractText Label="Background" NlmCategory="UNASSIGNED">In bioinformatics, estimation of k-mer abundance histograms or just enumerat-ing the number of unique k-mers and the number of singletons are desirable in many genome sequence analysis applications. The applications include predicting genome sizes, data pre-processing for de Bruijn graph assembly methods (tune runtime parameters for analysis tools), repeat detection, sequenc-ing coverage estimation, measuring sequencing error rates, etc. Different methods for cardinality estima-tion in sequencing data have been developed in recent years.</AbstractText><AbstractText Label="Objective" NlmCategory="UNASSIGNED">In this article, we present a comparative assessment of the different k-mer frequency estima-tion programs (ntCard, KmerGenie, KmerStream and Khmer (abundance-dist-single.py and unique-kmers.py) to assess their relative merits and demerits.</AbstractText><AbstractText Label="Methods" NlmCategory="UNASSIGNED">Principally, the miscounts/error-rates of these tools are analyzed by rigorous experimental analysis for a varied range of k. We also present experimental results on runtime, scalability for larger datasets, memory, CPU utilization as well as parallelism of k-mer frequency estimation methods.</AbstractText><AbstractText Label="Results" NlmCategory="UNASSIGNED">The results indicate that ntCard is more accurate in estimating F0, f1 and full k-mer abundance histograms compared with other methods. ntCard is the fastest but it has more memory requirements compared to KmerGenie.</AbstractText><AbstractText Label="Conclusion" NlmCategory="UNASSIGNED">The results of this evaluation may serve as a roadmap to potential users and practitioners of streaming algorithms for estimating k-mer coverage frequencies, to assist them in identifying an appro-priate method. Such results analysis also help researchers to discover remaining open research ques-tions, effective combinations of existing techniques and possible avenues for future research.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Manekar</LastName><ForeName>Swati C</ForeName><Initials>SC</Initials><AffiliationInfo><Affiliation>Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sathe</LastName><ForeName>Shailesh R</ForeName><Initials>SR</Initials><AffiliationInfo><Affiliation>Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United Arab Emirates</Country><MedlineTA>Curr Genomics</MedlineTA><NlmUniqueID>100960527</NlmUniqueID><ISSNLinking>1389-2029</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Distinct k-mers</Keyword><Keyword MajorTopicYN="N">Hashing</Keyword><Keyword MajorTopicYN="N">High-throughput sequencing</Keyword><Keyword MajorTopicYN="N">K-mer abundance histogram</Keyword><Keyword MajorTopicYN="N">Singleton k-mers</Keyword><Keyword MajorTopicYN="N">Streaming algorithms</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>07</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2018</Year><Month>10</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2018</Year><Month>10</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>4</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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Manekar</name></noRegion><name sortKey="Sathe, Shailesh R" sort="Sathe, Shailesh R" uniqKey="Sathe S" first="Shailesh R" last="Sathe">Shailesh R. Sathe</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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