ArticleBMC bioinformatics2016
ClinQC: a tool for quality control and cleaning of Sanger and NGS data in clinical research.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
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- Utilising Human Myometrial and Uterine Fibroid Stem Cell-Derived Three Dimentional Organoids as a Robust Model System for Understanding the Pathophysiology of Uterine Fibroids.Cell proliferation · 2025Article
- Unraveling metagenomics through long-read sequencing: a comprehensive review.Journal of translational medicine · 2024Review
- Advances of mRNA vaccine in tumor: a maze of opportunities and challenges.Biomarker research · 2023Review
- Music of metagenomics-a review of its applications, analysis pipeline, and associated tools.Functional & integrative genomics · 2022Review
- Computational challenges in detection of cancer using cell-free DNA methylation.Computational and structural biotechnology journal · 2022Review
- A Large-Scale and Serverless Computational Approach for Improving Quality of NGS Data Supporting Big Multi-Omics Data Analyses.Frontiers in genetics · 2021Article
- FQStat: a parallel architecture for very high-speed assessment of sequencing quality metrics.BMC bioinformatics · 2019Article
- SQUAT: a Sequencing Quality Assessment Tool for data quality assessments of genome assemblies.BMC genomics · 2019Article
- ANASTASIA: An Automated Metagenomic Analysis Pipeline for Novel Enzyme Discovery Exploiting Next Generation Sequencing Data.Frontiers in genetics · 2019Article
- PIPEBAR and OverlapPER: tools for a fast and accurate DNA barcoding analysis and paired-end assembly.BMC bioinformatics · 2018Article
- On the study of microbial transcriptomes using second- and third-generation sequencing technologies.Journal of microbiology (Seoul, Korea) · 2016Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTraditional Sanger sequencing has been used as a gold standard method for genetic testing in clinic to perform single gene test, which has been a cumbersome and expensive method to test several genes in heterogeneous disease such as cancer. With the advent of Next Generation Sequencing technologies, which produce data on unprecedented speed in a cost effective manner have overcome the limitation of Sanger sequencing. Therefore, for the efficient and affordable genetic testing, Next Generation Sequencing has been used as a complementary method with Sanger sequencing for disease causing mutation identification and confirmation in clinical research. However, in order to identify the potential disease causing mutations with great sensitivity and specificity it is essential to ensure high quality sequencing data. Therefore, integrated software tools are lacking which can analyze Sanger and NGS data together and eliminate platform specific sequencing errors, low quality reads and support the analysis of several sample/patients data set in a single run.
resultsWe have developed ClinQC, a flexible and user-friendly pipeline for format conversion, quality control, trimming and filtering of raw sequencing data generated from Sanger sequencing and three NGS sequencing platforms including Illumina, 454 and Ion Torrent. First, ClinQC convert input read files from their native formats to a common FASTQ format and remove adapters, and PCR primers. Next, it split bar-coded samples, filter duplicates, contamination and low quality sequences and generates a QC report. ClinQC output high quality reads in FASTQ format with Sanger quality encoding, which can be directly used in down-stream analysis. It can analyze hundreds of sample/patients data in a single run and generate unified output files for both Sanger and NGS sequencing data. Our tool is expected to be very useful for quality control and format conversion of Sanger and NGS data to facilitate improved downstream analysis and mutation screening.
conclusionsClinQC is a powerful and easy to handle pipeline for quality control and trimming in clinical research. ClinQC is written in Python with multiprocessing capability, run on all major operating systems and is available at https://sourceforge.net/projects/clinqc.
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