A benchmark study of k-mer counting methods for high-throughput sequencing.
Identifieur interne : 000A43 ( PubMed/Checkpoint ); précédent : 000A42; suivant : 000A44A benchmark study of k-mer counting methods for high-throughput sequencing.
Auteurs : Swati C. Manekar [Inde] ; Shailesh R. Sathe [Inde]Source :
- GigaScience [ 2047-217X ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- genetics : Arabidopsis.
- methods : Computational Biology.
- Animals, Databases, Genetic, Genome, High-Throughput Nucleotide Sequencing, Humans, Sequence Alignment, Sequence Analysis, DNA, Software, Transcriptome.
Abstract
The rapid development of high-throughput sequencing technologies means that hundreds of gigabytes of sequencing data can be produced in a single study. Many bioinformatics tools require counts of substrings of length k in DNA/RNA sequencing reads obtained for applications such as genome and transcriptome assembly, error correction, multiple sequence alignment, and repeat detection. Recently, several techniques have been developed to count k-mers in large sequencing datasets, with a trade-off between the time and memory required to perform this function. We assessed several k-mer counting programs and evaluated their relative performance, primarily on the basis of runtime and memory usage. We also considered additional parameters such as disk usage, accuracy, parallelism, the impact of compressed input, performance in terms of counting large k values and the scalability of the application to larger datasets.We make specific recommendations for the setup of a current state-of-the-art program and suggestions for further development.
DOI: 10.1093/gigascience/giy125
PubMed: 30346548
Affiliations:
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Many bioinformatics tools require counts of substrings of length k in DNA/RNA sequencing reads obtained for applications such as genome and transcriptome assembly, error correction, multiple sequence alignment, and repeat detection. Recently, several techniques have been developed to count k-mers in large sequencing datasets, with a trade-off between the time and memory required to perform this function. We assessed several k-mer counting programs and evaluated their relative performance, primarily on the basis of runtime and memory usage. We also considered additional parameters such as disk usage, accuracy, parallelism, the impact of compressed input, performance in terms of counting large k values and the scalability of the application to larger datasets.We make specific recommendations for the setup of a current state-of-the-art program and suggestions for further development.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30346548</PMID><DateCompleted><Year>2019</Year><Month>04</Month><Day>11</Day></DateCompleted><DateRevised><Year>2019</Year><Month>04</Month><Day>11</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2047-217X</ISSN><JournalIssue CitedMedium="Internet"><Volume>7</Volume><Issue>12</Issue><PubDate><Year>2018</Year><Month>12</Month><Day>01</Day></PubDate></JournalIssue><Title>GigaScience</Title><ISOAbbreviation>Gigascience</ISOAbbreviation></Journal><ArticleTitle>A benchmark study of k-mer counting methods for high-throughput sequencing.</ArticleTitle><ELocationID EIdType="doi" ValidYN="Y">10.1093/gigascience/giy125</ELocationID><Abstract><AbstractText>The rapid development of high-throughput sequencing technologies means that hundreds of gigabytes of sequencing data can be produced in a single study. Many bioinformatics tools require counts of substrings of length k in DNA/RNA sequencing reads obtained for applications such as genome and transcriptome assembly, error correction, multiple sequence alignment, and repeat detection. Recently, several techniques have been developed to count k-mers in large sequencing datasets, with a trade-off between the time and memory required to perform this function. We assessed several k-mer counting programs and evaluated their relative performance, primarily on the basis of runtime and memory usage. We also considered additional parameters such as disk usage, accuracy, parallelism, the impact of compressed input, performance in terms of counting large k values and the scalability of the application to larger datasets.We make specific recommendations for the setup of a current state-of-the-art program and suggestions for further development.</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 440 010, 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 440 010, 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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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