Encyclopedia of Metagenomics

2015 Edition
| Editors: Karen E. Nelson

NGS QC Toolkit: A Platform for Quality Control of Next-Generation Sequencing Data

Reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7478-5_348

Synonyms

Format converters; Illumina; NGS data quality control; NGS data trimming; Roche 454

Definition

NGS QC Toolkit is a Perl-based stand-alone program package for the quality control (QC) of next-generation sequencing (NGS) data. In addition to QC tools, it consists of many subsidiary tools for handling and processing of data obtained from Illumina and Roche 454 sequencing platforms. The open-source toolkit is freely available at http://www.nipgr.res.in/ngsqctoolkit.html.

Introduction

The need for fast and high-throughput sequencing has resulted into discovery of NGS technologies. The advent of these technologies has transformed the genomics research by providing an opportunity to study genetic information at a single-base resolution in cost-effective manner (Metzker 2010). However, usually several artifacts are reflected in NGS data due to technical errors and limitations associated with different NGS platforms. These sequence artifacts, including read errors, poor-quality reads,...

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References

  1. Benaglio P, Rivolta C. Ultra high throughput sequencing in human DNA variation detection: a comparative study on the NDUFA3-PRPF31 region. PLoS One. 2010;5(9):e13071.PubMedCentralPubMedGoogle Scholar
  2. Cock PJA, Fields CJ, Goto N, et al. The sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res. 2009;38:1767–71.PubMedCentralPubMedGoogle Scholar
  3. Cox MP, Peterson DA, Biggs PJ. SolexaQA: at-a-glance quality assessment of Illumina second-generation sequencing data. BMC Bioinformatics. 2010;11:485.PubMedCentralPubMedGoogle Scholar
  4. Lassmann T, Hayashizaki Y, Daub CO. TagDust-a program to eliminate artifacts from next generation sequencing data. Bioinformatics. 2009;25:2839–40.PubMedCentralPubMedGoogle Scholar
  5. Margulies M, Egholm M, Altman WE, et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005;437:376–80.PubMedCentralPubMedGoogle Scholar
  6. Metzker ML. Sequencing technologies – the next generation. Nat Rev Genet. 2010;11:31–46.PubMedGoogle Scholar
  7. Pandey RV, Nolte V, Schlotterer C. CANGS: a user-friendly utility for processing and analyzing 454 GS-FLX data in biodiversity studies. BMC Res Notes. 2010;3:3.PubMedCentralPubMedGoogle Scholar
  8. Patel RK, Jain M. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PLoS One. 2012;7(2):e30619.PubMedCentralPubMedGoogle Scholar
  9. Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27:863–4.PubMedCentralPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Functional and Applied Genomics LaboratoryNational Institute of Plant Genome Research (NIPGR)New DelhiIndia