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TSD: A Computational Tool To Study the Complex Structural Variants Using PacBio Targeted Sequencing Data.
Meng, Guofeng; Tan, Ying; Fan, Yue; Wang, Yan; Yang, Guang; Fanning, Gregory; Qiu, Yang.
Afiliação
  • Meng G; Janssen R&D China, 4560 Jinke Road, Shanghai 201210, China menggf@gmail.com.
  • Tan Y; Janssen R&D China, 4560 Jinke Road, Shanghai 201210, China.
  • Fan Y; Janssen R&D China, 4560 Jinke Road, Shanghai 201210, China.
  • Wang Y; Janssen R&D China, 4560 Jinke Road, Shanghai 201210, China.
  • Yang G; Janssen R&D China, 4560 Jinke Road, Shanghai 201210, China.
  • Fanning G; Janssen R&D China, 4560 Jinke Road, Shanghai 201210, China.
  • Qiu Y; Janssen R&D China, 4560 Jinke Road, Shanghai 201210, China.
G3 (Bethesda) ; 9(5): 1371-1376, 2019 05 07.
Article em En | MEDLINE | ID: mdl-30850377
ABSTRACT
PacBio sequencing is a powerful approach to study DNA or RNA sequences in a longer scope. It is especially useful in exploring the complex structural variants generated by random integration or multiple rearrangement of endogenous or exogenous sequences. Here, we present a tool, TSD, for complex structural variant discovery using PacBio targeted sequencing data. It allows researchers to identify and visualize the genomic structures of targeted sequences by unlimited splitting, alignment and assembly of long PacBio reads. Application to the sequencing data derived from an HBV integrated human cell line(PLC/PRF/5) indicated that TSD could recover the full profile of HBV integration events, especially for the regions with the complex human-HBV genome integrations and multiple HBV rearrangements. Compared to other long read analysis tools, TSD showed a better performance for detecting complex genomic structural variants. TSD is publicly available at https//github.com/menggf/tsd.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Biologia Computacional / Genômica Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Biologia Computacional / Genômica Idioma: En Ano de publicação: 2019 Tipo de documento: Article