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KSNP: a fast de Bruijn graph-based haplotyping tool approaching data-in time cost.
Zhou, Qian; Ji, Fahu; Lin, Dongxiao; Liu, Xianming; Zhu, Zexuan; Ruan, Jue.
Afiliação
  • Zhou Q; PengCheng Laboratory, Shenzhen, China.
  • Ji F; School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
  • Lin D; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
  • Liu X; PengCheng Laboratory, Shenzhen, China.
  • Zhu Z; School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
  • Ruan J; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. zhuzx@szu.edu.cn.
Nat Commun ; 15(1): 3126, 2024 Apr 11.
Article em En | MEDLINE | ID: mdl-38605047
ABSTRACT
Long reads that cover more variants per read raise opportunities for accurate haplotype construction, whereas the genotype errors of single nucleotide polymorphisms pose great computational challenges for haplotyping tools. Here we introduce KSNP, an efficient haplotype construction tool based on the de Bruijn graph (DBG). KSNP leverages the ability of DBG in handling high-throughput erroneous reads to tackle the challenges. Compared to other notable tools in this field, KSNP achieves at least 5-fold speedup while producing comparable haplotype results. The time required for assembling human haplotypes is reduced to nearly the data-in time.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Contexto em Saúde: 1_ASSA2030 Problema de saúde: 1_financiamento_saude Assunto principal: Algoritmos / Polimorfismo de Nucleotídeo Único Limite: Humans Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Contexto em Saúde: 1_ASSA2030 Problema de saúde: 1_financiamento_saude Assunto principal: Algoritmos / Polimorfismo de Nucleotídeo Único Limite: Humans Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China
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