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Toward perfect reads: self-correction of short reads via mapping on de Bruijn graphs.
Limasset, Antoine; Flot, Jean-François; Peterlongo, Pierre.
Afiliação
  • Limasset A; Evolutionary Biology & Ecology, Université Libre de Bruxelles (ULB), Bruxelles, Belgium.
  • Flot JF; Evolutionary Biology & Ecology, Université Libre de Bruxelles (ULB), Bruxelles, Belgium.
  • Peterlongo P; Interuniversity Institute of Bioinformatics in Brussels - (IB) 2, Brussels, Belgium.
Bioinformatics ; 36(5): 1374-1381, 2020 03 01.
Article em En | MEDLINE | ID: mdl-30785192
MOTIVATION: Short-read accuracy is important for downstream analyses such as genome assembly and hybrid long-read correction. Despite much work on short-read correction, present-day correctors either do not scale well on large datasets or consider reads as mere suites of k-mers, without taking into account their full-length sequence information. RESULTS: We propose a new method to correct short reads using de Bruijn graphs and implement it as a tool called Bcool. As a first step, Bcool constructs a compacted de Bruijn graph from the reads. This graph is filtered on the basis of k-mer abundance then of unitig abundance, thereby removing most sequencing errors. The cleaned graph is then used as a reference on which the reads are mapped to correct them. We show that this approach yields more accurate reads than k-mer-spectrum correctors while being scalable to human-size genomic datasets and beyond. AVAILABILITY AND IMPLEMENTATION: The implementation is open source, available at http://github.com/Malfoy/BCOOL under the Affero GPL license and as a Bioconda package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Sequenciamento de Nucleotídeos em Larga Escala Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Sequenciamento de Nucleotídeos em Larga Escala Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article