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Title: FastProNGS: fast preprocessing of next-generation sequencing reads

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. Yet, 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 additionally very flexible and can implement different functions using different user-set parameter combinations.

Authors:
 [1];  [2];  [3];  [4];  [4]; ORCiD logo [4]
  1. Megagenomics Corp., Beijing (China); Ping An Health Technology, Beijing (China)
  2. Megagenomics Corp., Beijing (China); R&D Suning, Beijing (China)
  3. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  4. Megagenomics Corp., Beijing (China)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1542756
Report Number(s):
NREL/JA-2700-74302
Journal ID: ISSN 1471-2105
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
BMC Bioinformatics
Additional Journal Information:
Journal Volume: 20; Journal Issue: 1; Journal ID: ISSN 1471-2105
Publisher:
BioMed Central
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; quality control; adapter removing; NGS

Citation Formats

Liu, Xiaoshuang, Yan, Zhenhe, Wu, Chao, Yang, Yang, Li, Xiaomin, and Zhang, Guangxin. FastProNGS: fast preprocessing of next-generation sequencing reads. United States: N. p., 2019. Web. doi:10.1186/s12859-019-2936-9.
Liu, Xiaoshuang, Yan, Zhenhe, Wu, Chao, Yang, Yang, Li, Xiaomin, & Zhang, Guangxin. FastProNGS: fast preprocessing of next-generation sequencing reads. United States. doi:10.1186/s12859-019-2936-9.
Liu, Xiaoshuang, Yan, Zhenhe, Wu, Chao, Yang, Yang, Li, Xiaomin, and Zhang, Guangxin. Mon . "FastProNGS: fast preprocessing of next-generation sequencing reads". United States. doi:10.1186/s12859-019-2936-9. https://www.osti.gov/servlets/purl/1542756.
@article{osti_1542756,
title = {FastProNGS: fast preprocessing of next-generation sequencing reads},
author = {Liu, Xiaoshuang and Yan, Zhenhe and Wu, Chao and Yang, Yang and Li, Xiaomin and Zhang, Guangxin},
abstractNote = {Background Next-generation sequencing technology is developing rapidly and the vast amount of data that is generated needs to be preprocessed for downstream analyses. Yet, 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 additionally very flexible and can implement different functions using different user-set parameter combinations.},
doi = {10.1186/s12859-019-2936-9},
journal = {BMC Bioinformatics},
number = 1,
volume = 20,
place = {United States},
year = {2019},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
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Figures / Tables:

Table 1 Table 1: Tools developed for processing next-generation sequencing data

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Works referenced in this record:

Illumina error profiles: resolving fine-scale variation in metagenomic sequencing data
journal, March 2016


PIQA: pipeline for Illumina G1 genome analyzer data quality assessment
journal, July 2009


Quality control and preprocessing of metagenomic datasets
journal, January 2011


NGS QC Toolkit: A Toolkit for Quality Control of Next Generation Sequencing Data
journal, February 2012


Rapid evaluation and quality control of next generation sequencing data with FaQCs
journal, November 2014


    Works referencing / citing this record:

    PIQA: pipeline for Illumina G1 genome analyzer data quality assessment
    journal, July 2009


    Quality control and preprocessing of metagenomic datasets
    journal, January 2011


    Rapid evaluation and quality control of next generation sequencing data with FaQCs
    journal, November 2014


    Illumina error profiles: resolving fine-scale variation in metagenomic sequencing data
    journal, March 2016


    NGS QC Toolkit: A Toolkit for Quality Control of Next Generation Sequencing Data
    journal, February 2012


      Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.