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Repliscan: a tool for classifying replication timing regions.
Zynda, Gregory J; Song, Jawon; Concia, Lorenzo; Wear, Emily E; Hanley-Bowdoin, Linda; Thompson, William F; Vaughn, Matthew W.
Afiliação
  • Zynda GJ; Texas Advanced Computing Center, University of Texas at Austin, 10100 Burnet Road, Austin, 78758-4497, TX, USA.
  • Song J; Texas Advanced Computing Center, University of Texas at Austin, 10100 Burnet Road, Austin, 78758-4497, TX, USA.
  • Concia L; Department of Plant and Microbial Biology, North Carolina State University, Raleigh, 27695-7612, NC, USA.
  • Wear EE; Department of Plant and Microbial Biology, North Carolina State University, Raleigh, 27695-7612, NC, USA.
  • Hanley-Bowdoin L; Department of Plant and Microbial Biology, North Carolina State University, Raleigh, 27695-7612, NC, USA.
  • Thompson WF; Department of Plant and Microbial Biology, North Carolina State University, Raleigh, 27695-7612, NC, USA.
  • Vaughn MW; Texas Advanced Computing Center, University of Texas at Austin, 10100 Burnet Road, Austin, 78758-4497, TX, USA. vaughn@tacc.utexas.edu.
BMC Bioinformatics ; 18(1): 362, 2017 Aug 07.
Article em En | MEDLINE | ID: mdl-28784090
BACKGROUND: Replication timing experiments that use label incorporation and high throughput sequencing produce peaked data similar to ChIP-Seq experiments. However, the differences in experimental design, coverage density, and possible results make traditional ChIP-Seq analysis methods inappropriate for use with replication timing. RESULTS: To accurately detect and classify regions of replication across the genome, we present Repliscan. Repliscan robustly normalizes, automatically removes outlying and uninformative data points, and classifies Repli-seq signals into discrete combinations of replication signatures. The quality control steps and self-fitting methods make Repliscan generally applicable and more robust than previous methods that classify regions based on thresholds. CONCLUSIONS: Repliscan is simple and effective to use on organisms with different genome sizes. Even with analysis window sizes as small as 1 kilobase, reliable profiles can be generated with as little as 2.4x coverage.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Análise de Sequência de DNA / Período de Replicação do DNA / Sequenciamento de Nucleotídeos em Larga Escala Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Análise de Sequência de DNA / Período de Replicação do DNA / Sequenciamento de Nucleotídeos em Larga Escala Idioma: En Ano de publicação: 2017 Tipo de documento: Article