Efficient Mining Multi-mers in a Variety of Biological Sequences.
Identifieur interne : 000840 ( PubMed/Corpus ); précédent : 000839; suivant : 000841Efficient Mining Multi-mers in a Variety of Biological Sequences.
Auteurs : Jingsong Zhang ; Jianmei Guo ; Ming Zhang ; Xiangtian Yu ; Xiaoqing Yu ; Weifeng Guo ; Tao Zeng ; Luonan ChenSource :
- IEEE/ACM transactions on computational biology and bioinformatics [ 1557-9964 ] ; 2018.
Abstract
Counting the occurrence frequency of each -mer in a biological sequence is a preliminary yet important step in many bioinformatics applications. However, most -mer counting algorithms rely on a given k to produce single-length -mers, which is inefficient for sequence analysis for different k. Moreover, existing -mer counters focus more on DNA and RNA sequences and less on protein ones. In practice, the analysis of -mers in protein sequences can provide substantial biological insights in structure, function and evolution. To this end, an efficient algorithm, called MulMer (Multiple-Mer mining), is proposed to mine -mers of various lengths termed multi-mers via inverted-index technique, which is orders of magnitude faster than the conventional forward-index methods. Moreover, to the best of our knowledge, MulMer is the first able to mine multi-mers in a variety of sequences, including DNARNA and protein sequences.
DOI: 10.1109/TCBB.2018.2828313
PubMed: 29993642
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However, most -mer counting algorithms rely on a given k to produce single-length -mers, which is inefficient for sequence analysis for different k. Moreover, existing -mer counters focus more on DNA and RNA sequences and less on protein ones. In practice, the analysis of -mers in protein sequences can provide substantial biological insights in structure, function and evolution. To this end, an efficient algorithm, called MulMer (Multiple-Mer mining), is proposed to mine -mers of various lengths termed multi-mers via inverted-index technique, which is orders of magnitude faster than the conventional forward-index methods. Moreover, to the best of our knowledge, MulMer is the first able to mine multi-mers in a variety of sequences, including DNARNA and protein sequences.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">29993642</PMID><DateRevised><Year>2019</Year><Month>11</Month><Day>20</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1557-9964</ISSN><JournalIssue CitedMedium="Internet"><PubDate><Year>2018</Year><Month>Apr</Month><Day>19</Day></PubDate></JournalIssue><Title>IEEE/ACM transactions on computational biology and bioinformatics</Title><ISOAbbreviation>IEEE/ACM Trans Comput Biol Bioinform</ISOAbbreviation></Journal><ArticleTitle>Efficient Mining Multi-mers in a Variety of Biological Sequences.</ArticleTitle><ELocationID EIdType="doi" ValidYN="Y">10.1109/TCBB.2018.2828313</ELocationID><Abstract><AbstractText>Counting the occurrence frequency of each -mer in a biological sequence is a preliminary yet important step in many bioinformatics applications. However, most -mer counting algorithms rely on a given k to produce single-length -mers, which is inefficient for sequence analysis for different k. Moreover, existing -mer counters focus more on DNA and RNA sequences and less on protein ones. In practice, the analysis of -mers in protein sequences can provide substantial biological insights in structure, function and evolution. To this end, an efficient algorithm, called MulMer (Multiple-Mer mining), is proposed to mine -mers of various lengths termed multi-mers via inverted-index technique, which is orders of magnitude faster than the conventional forward-index methods. Moreover, to the best of our knowledge, MulMer is the first able to mine multi-mers in a variety of sequences, including DNARNA and protein sequences.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Jingsong</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Guo</LastName><ForeName>Jianmei</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Ming</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Yu</LastName><ForeName>Xiangtian</ForeName><Initials>X</Initials></Author><Author ValidYN="Y"><LastName>Yu</LastName><ForeName>Xiaoqing</ForeName><Initials>X</Initials></Author><Author ValidYN="Y"><LastName>Guo</LastName><ForeName>Weifeng</ForeName><Initials>W</Initials></Author><Author ValidYN="Y"><LastName>Zeng</LastName><ForeName>Tao</ForeName><Initials>T</Initials></Author><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>Luonan</ForeName><Initials>L</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>04</Month><Day>19</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>IEEE/ACM Trans Comput Biol Bioinform</MedlineTA><NlmUniqueID>101196755</NlmUniqueID><ISSNLinking>1545-5963</ISSNLinking></MedlineJournalInfo></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2018</Year><Month>7</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2018</Year><Month>7</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2018</Year><Month>7</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">29993642</ArticleId><ArticleId IdType="doi">10.1109/TCBB.2018.2828313</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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