ChronQC: a quality control monitoring system for clinical next generation sequencing

Next Generation Sequencing (NGS) analysis has become a widely used method for studying the structure of DNA and RNA, but complexity of the procedure leads to obtaining error-prone datasets which need to be cleansed in order to avoid misinterpretation of data. We address the usage and proper interpretations of characteristic metrics for RNA sequencing (RNAseq) quality control, implemented in and reported by FastQC, and provide a comprehensive guidance for their assessment in the context of total RNAseq quality control of Illumina raw reads. Additionally, we give recommendations how to adequately perform the quality control preprocessing step of raw total RNAseq Illumina reads according to the obtained results of the quality control evaluation step; the aim is to provide the best dataset to downstream analysis, rather than to get better FastQC results. We also tested effects of different preprocessing approaches to the downstream analysis and recommended the most suitable approach.

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Next Generation Sequencing Based In-house HIV Genotyping Method: Validation Report

10.21203/rs.3.rs-386721/v1 ◽

2021 ◽

Author(s):

Alisen Ayitewala ◽

Isaac Ssewanyana ◽

Charles Kiyaga

Keyword(s):

Drug Resistance ◽

Quality Control ◽

Next Generation Sequencing ◽

Low Income ◽

Rna Extraction ◽

Clinical Samples ◽

Small Range ◽

Next Generation ◽

Genotyping Method ◽

Generation Sequencing

Abstract BackgroundHIV genotyping has had a significant impact on care and treatment of HIV/AIDS. At clinical level, the test guides physicians on the choice of treatment regimens. At surveillance level, it informs policy on consolidated treatment guidelines and microbial resistance control strategies. Until recently, the conventional test has utilized Sanger sequencing (SS) method. Unlike Next Generation Sequencing (NGS), SS is limited by low data throughput and the inability of detecting low abundant drug resistant variants. NGS has the capacity to improve sensitivity and quantitatively identify low-abundance variants; in addition, it has the potential to improve efficiency as well as lowering costs when samples are batched. Despite the NGS benefits, its utilization in clinical drug resistance profiling is faced with mixed reactions. These are largely based on lack of a consensus regarding the quality control strategy. Nonetheless, transitional views suggest validating the method against the gold-standard SS. Therefore, we present a validation report of an NGS-based in-house HIV genotyping method against SS method in Uganda. ResultsSince there were no established proficiency test panels for NGS-based HIV genotyping, fifteen (15) clinical plasma samples for routine care were utilized. The use of clinical samples allowed for accuracy and precision studies. The workflow involved four (4) main steps; viral RNA extraction, targeted amplicon generation, amplicon sequencing and data analysis. Accuracy of 98% with an average percentage error of 3% was reported for the NGS based assay against the SS platform demonstrating similar performance. The coefficient of variation (CV) findings for both the inter-run and inter-personnel precision showed no variability (CV ≤0%) at the relative abundance of ≥20%. For both inter-run and inter-personnel, variation that affected the precision was observed at 1% frequency. Overall, for all the frequencies, CV registered a small range of (0-2%).Conclusion The NGS-based in-house HIV genotyping method fulfilled the minimum requirements that support its utilization for drug resistance profiling in a clinical setting of a low-income country. For more inclusive quality control studies, well characterized wet panels need to be established.

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CoverView: a sequence quality evaluation tool for next generation sequencing data

Wellcome Open Research ◽

10.12688/wellcomeopenres.14306.1 ◽

2018 ◽

Vol 3 ◽

pp. 36 ◽

Cited By ~ 5

Author(s):

Márton Münz ◽

Shazia Mahamdallie ◽

Shawn Yost ◽

Andrew Rimmer ◽

Emma Poyastro-Pearson ◽

...

Keyword(s):

Quality Control ◽

Next Generation Sequencing ◽

Quality Evaluation ◽

Reference Sample ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Evaluation Tool ◽

Link Type ◽

Generation Sequencing

Quality assurance and quality control are essential for robust next generation sequencing (NGS). Here we present CoverView, a fast, flexible, user-friendly quality evaluation tool for NGS data. CoverView processes mapped sequencing reads and user-specified regions to report depth of coverage, base and mapping quality metrics with increasing levels of detail from a chromosome-level summary to per-base profiles. CoverView can flag regions that do not fulfil user-specified quality requirements, allowing suboptimal data to be systematically and automatically presented for review. It also provides an interactive graphical user interface (GUI) that can be opened in a web browser and allows intuitive exploration of results. We have integrated CoverView into our accredited clinical cancer predisposition gene testing laboratory that uses the TruSight Cancer Panel (TSCP). CoverView has been invaluable for optimisation and quality control of our testing pipeline, providing transparent, consistent quality metric information and automatic flagging of regions that fall below quality thresholds. We demonstrate this utility with TSCP data from the Genome in a Bottle reference sample, which CoverView analysed in 13 seconds. CoverView uses data routinely generated by NGS pipelines, reads standard input formats, and rapidly creates easy-to-parse output text (.txt) files that are customised by a simple configuration file. CoverView can therefore be easily integrated into any NGS pipeline. CoverView and detailed documentation for its use are freely available at github.com/RahmanTeamDevelopment/CoverView/releases and www.icr.ac.uk/CoverView

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