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A Flow Procedure for Linearization of Genome Sequence Graphs.
Haussler, David; Smuga-Otto, Maciej; Eizenga, Jordan M; Paten, Benedict; Novak, Adam M; Nikitin, Sergei; Zueva, Maria; Miagkov, Dmitrii.
Afiliação
  • Haussler D; 1 UC Santa Cruz Genomics Institute, University of California , Santa Cruz, California.
  • Smuga-Otto M; 1 UC Santa Cruz Genomics Institute, University of California , Santa Cruz, California.
  • Eizenga JM; 1 UC Santa Cruz Genomics Institute, University of California , Santa Cruz, California.
  • Paten B; 1 UC Santa Cruz Genomics Institute, University of California , Santa Cruz, California.
  • Novak AM; 1 UC Santa Cruz Genomics Institute, University of California , Santa Cruz, California.
  • Nikitin S; 2 EPAM Systems, Inc. , Newtown, Pennsylvania.
  • Zueva M; 2 EPAM Systems, Inc. , Newtown, Pennsylvania.
  • Miagkov D; 2 EPAM Systems, Inc. , Newtown, Pennsylvania.
J Comput Biol ; 25(7): 664-676, 2018 07.
Article em En | MEDLINE | ID: mdl-29792514
ABSTRACT
Efforts to incorporate human genetic variation into the reference human genome have converged on the idea of a graph representation of genetic variation within a species, a genome sequence graph. A sequence graph represents a set of individual haploid reference genomes as paths in a single graph. When that set of reference genomes is sufficiently diverse, the sequence graph implicitly contains all frequent human genetic variations, including translocations, inversions, deletions, and insertions. In representing a set of genomes as a sequence graph, one encounters certain challenges. One of the most important is the problem of graph linearization, essential both for efficiency of storage and access, and for natural graph visualization and compatibility with other tools. The goal of graph linearization is to order nodes of the graph in such a way that operations such as access, traversal, and visualization are as efficient and effective as possible. A new algorithm for the linearization of sequence graphs, called the flow procedure (FP), is proposed in this article. Comparative experimental evaluation of the FP against other algorithms shows that it outperforms its rivals in the metrics most relevant to sequence graphs.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Genoma Humano / Biologia Computacional / Genômica Limite: Humans Idioma: En Revista: J Comput Biol Assunto da revista: BIOLOGIA MOLECULAR / INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Genoma Humano / Biologia Computacional / Genômica Limite: Humans Idioma: En Revista: J Comput Biol Assunto da revista: BIOLOGIA MOLECULAR / INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article