Clustering of reads with alignment-free measures and quality values.
Identifieur interne : 001696 ( PubMed/Curation ); précédent : 001695; suivant : 001697Clustering of reads with alignment-free measures and quality values.
Auteurs : Matteo Comin [Italie] ; Andrea Leoni [Italie] ; Michele Schimd [Italie]Source :
- Algorithms for molecular biology : AMB [ 1748-7188 ] ; 2015.
Abstract
The data volume generated by Next-Generation Sequencing (NGS) technologies is growing at a pace that is now challenging the storage and data processing capacities of modern computer systems. In this context an important aspect is the reduction of data complexity by collapsing redundant reads in a single cluster to improve the run time, memory requirements, and quality of post-processing steps like assembly and error correction. Several alignment-free measures, based on k-mers counts, have been used to cluster reads. Quality scores produced by NGS platforms are fundamental for various analysis of NGS data like reads mapping and error detection. Moreover future-generation sequencing platforms will produce long reads but with a large number of erroneous bases (up to 15 %).
DOI: 10.1186/s13015-014-0029-x
PubMed: 25691913
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In this context an important aspect is the reduction of data complexity by collapsing redundant reads in a single cluster to improve the run time, memory requirements, and quality of post-processing steps like assembly and error correction. Several alignment-free measures, based on k-mers counts, have been used to cluster reads. Quality scores produced by NGS platforms are fundamental for various analysis of NGS data like reads mapping and error detection. Moreover future-generation sequencing platforms will produce long reads but with a large number of erroneous bases (up to 15 %).</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">25691913</PMID><DateCompleted><Year>2015</Year><Month>02</Month><Day>18</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">1748-7188</ISSN><JournalIssue CitedMedium="Print"><Volume>10</Volume><PubDate><Year>2015</Year></PubDate></JournalIssue><Title>Algorithms for molecular biology : AMB</Title><ISOAbbreviation>Algorithms Mol Biol</ISOAbbreviation></Journal><ArticleTitle>Clustering of reads with alignment-free measures and quality values.</ArticleTitle><Pagination><MedlinePgn>4</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s13015-014-0029-x</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">The data volume generated by Next-Generation Sequencing (NGS) technologies is growing at a pace that is now challenging the storage and data processing capacities of modern computer systems. In this context an important aspect is the reduction of data complexity by collapsing redundant reads in a single cluster to improve the run time, memory requirements, and quality of post-processing steps like assembly and error correction. Several alignment-free measures, based on k-mers counts, have been used to cluster reads. Quality scores produced by NGS platforms are fundamental for various analysis of NGS data like reads mapping and error detection. Moreover future-generation sequencing platforms will produce long reads but with a large number of erroneous bases (up to 15 %).</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">In this scenario it will be fundamental to exploit quality value information within the alignment-free framework. To the best of our knowledge this is the first study that incorporates quality value information and k-mers counts, in the context of alignment-free measures, for the comparison of reads data. Based on this principles, in this paper we present a family of alignment-free measures called D (q) -type. A set of experiments on simulated and real reads data confirms that the new measures are superior to other classical alignment-free statistics, especially when erroneous reads are considered. Also results on de novo assembly and metagenomic reads classification show that the introduction of quality values improves over standard alignment-free measures. These statistics are implemented in a software called QCluster (http://www.dei.unipd.it/~ciompin/main/qcluster.html).</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Comin</LastName><ForeName>Matteo</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Information Engineering, University of Padova, Padova, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Leoni</LastName><ForeName>Andrea</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Department of Information Engineering, University of Padova, Padova, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Schimd</LastName><ForeName>Michele</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Information Engineering, University of Padova, Padova, Italy.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2015</Year><Month>01</Month><Day>28</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Algorithms Mol Biol</MedlineTA><NlmUniqueID>101265088</NlmUniqueID><ISSNLinking>1748-7188</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Alignment-free measures</Keyword><Keyword MajorTopicYN="N">Reads clustering</Keyword><Keyword MajorTopicYN="N">Reads quality values</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2014</Year><Month>11</Month><Day>19</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>12</Month><Day>17</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>2</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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