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

By Ravi K. Patel, Mukesh Jain. PLoS ONE, 2012

2,739 citations recorded by the source corpus.

Next generation sequencing (NGS) technologies provide a high-throughput means to generate large amount of sequence data. However, quality control (QC) of sequence data generated from these technologies is extremely important for meaningful downstream analysis. Further, highly efficient and fast processing tools are required to handle the large volume of datasets. Here, we have developed an application, NGS QC Toolkit, for quality check and filtering of high-quality data. This toolkit is a standalone and open source application freely available at http://www.nipgr.res.in/ngsqctoolkit.html. All the tools in the application have been implemented in Perl programming language. The toolkit is comprised of user-friendly tools for QC of sequencing data generated using Roche 454 and Illumina platforms, and additional tools to aid QC (sequence format converter and trimming tools) and analysis (statistics tools). A variety of options have been provided to facilitate the QC at user-defined parameters. The toolkit is expected to be very useful for the QC of NGS data to facilitate better downstream analysis.

Subfields: Artificial Intelligence, Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/94337f064e0b0271f473c618f9de5d4d0b93e34b

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Citations in the corpus

High-fat diet alters gut microbiota physiology in mice (2013, methodology): “The first 10 and last 20 nucleotides of all reads were trimmed using the NGS-QC toolkit (New Dehli, India) (Patel and Jain, 2012) to avoid GC bias and non-random base composition as well as low sequence quality at 30-end.”
LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse (2018, methodology): “For RNA-seq data, NGS QC Toolkit (35) was used for quality filtering and trimming, TopHat (36) was used to map reads to reference genome, and HTSeq (37) was used to count how many reads map to each gene.”
SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data (2017, methodology): “Intermsofaccelerationapproach,multithreading is the sole method adopted by existing tools [14–16, 24– 28 ], For the future works, we will continue adding functions to feature modules.”
Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data (2015, methodology): “In this regard, several bioinformatics tools have been developed to perform quality control (QC) of the HTS data by analyzing raw reads and their derivatives in the form of sequencing alignments and other quantitative data (Patel and Mukesh, 2012).”
JCVI: A versatile toolkit for comparative genomics analysis (2024, background): “Traditional workflows often require researchers to navigate through a maze of separate applications for different aspects of comparative genomics, such as assembly alignment, synteny analysis, and visualization [16].”
Ultrafast one‐pass FASTQ data preprocessing, quality control, and deduplication using fastp (2023, methodology): “Many tools, such as FQC Dashboard [5] and NGS QC Toolkit [6], were developed for FASTQ data quality control.”
Anticancer sulfonamides target splicing by inducing RBM39 degradation via recruitment to DCAF15 (2017, methodology): “1) (51) was used to check the sequencing quality, and high-quality reads were aligned by TopHat (v2.”