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FastProNGS: fast preprocessing of next-generation sequencing reads.
Liu, Xiaoshuang; Yan, Zhenhe; Wu, Chao; Yang, Yang; Li, Xiaomin; Zhang, Guangxin.
Afiliação
  • Liu X; Megagenomics Corporation, Beijing, China.
  • Yan Z; Ping An Health Technology, Beijing, China.
  • Wu C; Megagenomics Corporation, Beijing, China.
  • Yang Y; NLP, R&D Suning, Beijing, China.
  • Li X; National Renewable Energy Laboratory CO, Colorado, USA.
  • Zhang G; Megagenomics Corporation, Beijing, China.
BMC Bioinformatics ; 20(1): 345, 2019 Jun 17.
Article em En | MEDLINE | ID: mdl-31208325
ABSTRACT

BACKGROUND:

Next-generation sequencing technology is developing rapidly and the vast amount of data that is generated needs to be preprocessed for downstream analyses. However, until now, software that can efficiently make all the quality assessments and filtration of raw data is still lacking.

RESULTS:

We developed FastProNGS to integrate the quality control process with automatic adapter removal. Parallel processing was implemented to speed up the process by allocating multiple threads. Compared with similar up-to-date preprocessing tools, FastProNGS is by far the fastest. Read information before and after filtration can be output in plain-text, JSON, or HTML formats with user-friendly visualization.

CONCLUSIONS:

FastProNGS is a rapid, standardized, and user-friendly tool for preprocessing next-generation sequencing data within minutes. It is an all-in-one software that is convenient for bulk data analysis. It is also very flexible and can implement different functions using different user-set parameter combinations.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Sequenciamento de Nucleotídeos em Larga Escala Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Sequenciamento de Nucleotídeos em Larga Escala Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article