scholarly journals Statistical guidelines for quality control of next-generation sequencing techniques

2021 ◽  
Vol 4 (11) ◽  
pp. e202101113
Author(s):  
Maximilian Sprang ◽  
Matteo Krüger ◽  
Miguel A Andrade-Navarro ◽  
Jean-Fred Fontaine
Keyword(s):  
Quality Control ◽  
Next Generation Sequencing ◽  
Genome Mapping ◽  
Data Driven ◽  
Next Generation ◽  
Experimental Conditions ◽  
Ngs Data ◽  
Generation Sequencing ◽  
Quality Features

More and more next-generation sequencing (NGS) data are made available every day. However, the quality of this data is not always guaranteed. Available quality control tools require profound knowledge to correctly interpret the multiplicity of quality features. Moreover, it is usually difficult to know if quality features are relevant in all experimental conditions. Therefore, the NGS community would highly benefit from condition-specific data-driven guidelines derived from many publicly available experiments, which reflect routinely generated NGS data. In this work, we have characterized well-known quality guidelines and related features in big datasets and concluded that they are too limited for assessing the quality of a given NGS file accurately. Therefore, we present new data-driven guidelines derived from the statistical analysis of many public datasets using quality features calculated by common bioinformatics tools. Thanks to this approach, we confirm the high relevance of genome mapping statistics to assess the quality of the data, and we demonstrate the limited scope of some quality features that are not relevant in all conditions. Our guidelines are available at https://cbdm.uni-mainz.de/ngs-guidelines.

scholarly journals Review of Clinical Next-Generation Sequencing

2017 ◽  
Vol 141 (11) ◽  
pp. 1544-1557 ◽  
Author(s):  
Sophia Yohe ◽  
Bharat Thyagarajan
Keyword(s):  
Next Generation Sequencing ◽  
Clinical Care ◽  
Cost Effective ◽  
Next Generation ◽  
Inherited Disorders ◽  
Academic Center ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing

Context.— Next-generation sequencing (NGS) is a technology being used by many laboratories to test for inherited disorders and tumor mutations. This technology is new for many practicing pathologists, who may not be familiar with the uses, methodology, and limitations of NGS. Objective.— To familiarize pathologists with several aspects of NGS, including current and expanding uses; methodology including wet bench aspects, bioinformatics, and interpretation; validation and proficiency; limitations; and issues related to the integration of NGS data into patient care. Data Sources.— The review is based on peer-reviewed literature and personal experience using NGS in a clinical setting at a major academic center. Conclusions.— The clinical applications of NGS will increase as the technology, bioinformatics, and resources evolve to address the limitations and improve quality of results. The challenge for clinical laboratories is to ensure testing is clinically relevant, cost-effective, and can be integrated into clinical care.

scholarly journals Next-Generation Sequencing (NGS) in COVID-19: A Tool for SARS-CoV-2 Diagnosis, Monitoring New Strains and Phylodynamic Modeling in Molecular Epidemiology

2021 ◽  
Vol 43 (2) ◽  
pp. 845-867
Author(s):  
Goldin John ◽  
Nikhil Shri Sahajpal ◽  
Ashis K. Mondal ◽  
Sudha Ananth ◽  
Colin Williams ◽  
...  
Keyword(s):  
Quality Control ◽  
Next Generation Sequencing ◽  
Real World ◽  
Next Generation ◽  
Comprehensive Review ◽  
Current Testing ◽  
Phylogenetic Evolution ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing

This review discusses the current testing methodologies for COVID-19 diagnosis and explores next-generation sequencing (NGS) technology for the detection of SARS-CoV-2 and monitoring phylogenetic evolution in the current COVID-19 pandemic. The review addresses the development, fundamentals, assay quality control and bioinformatics processing of the NGS data. This article provides a comprehensive review of the obstacles and opportunities facing the application of NGS technologies for the diagnosis, surveillance, and study of SARS-CoV-2 and other infectious diseases. Further, we have contemplated the opportunities and challenges inherent in the adoption of NGS technology as a diagnostic test with real-world examples of its utility in the fight against COVID-19.

scholarly journals The Use of Next-Generation Sequencing for the Quality Control of Live-Attenuated Polio Vaccines

2020 ◽  
Vol 222 (11) ◽  
pp. 1920-1927
Author(s):  
Bethany Charlton ◽  
Jason Hockley ◽  
Majid Laassri ◽  
Thomas Wilton ◽  
Laura Crawt ◽  
...  
Keyword(s):  
Quality Control ◽  
Next Generation Sequencing ◽  
Mutational Analysis ◽  
Pearson Correlation ◽  
Oral Polio Vaccine ◽  
Next Generation ◽  
High Consistency ◽  
Type 3 ◽  
Ngs Data ◽  
Generation Sequencing

Abstract Background Next-generation sequencing (NGS) analysis was compared to the current MAPREC (mutational analysis by polymerase chain reaction and restriction enzyme cleavage) assay for quality control of live-attenuated oral polio vaccine (OPV). Methods MAPREC measures reversion of the main OPV attenuating mutations such as uracil (U) to cytosine (C) at nucleotide 472 in the 5′ noncoding region of type 3 OPV. Eleven type 3 OPV samples were analyzed by 8 laboratories using their in-house NGS method. Results Intraassay, intralaboratory, and interlaboratory variability of NGS 472-C estimates across samples and laboratories were very low, leading to excellent agreement between laboratories. A high degree of correlation between %472-C results by MAPREC and NGS was observed in all laboratories (Pearson correlation coefficient r = 0.996). NGS estimates of sequences at nucleotide 2493 with known polymorphism among type 3 OPV lots also produced low assay variability and excellent between-laboratory agreement. Conclusions The high consistency of NGS data demonstrates that NGS analysis can be used as high-resolution test alternative to MAPREC, producing whole-genome profiles to evaluate OPV production consistency, possibly eliminating the need for tests in animals. This would be very beneficial for the quality assessment of next-generation polio vaccines and, eventually, for other live-attenuated viral vaccines.

scholarly journals Automated quality control of next generation sequencing data using machine learning

2019 ◽  
Author(s):  
Steffen Albrecht ◽  
Miguel A. Andrade-Navarro ◽  
Jean-Fred Fontaine
Keyword(s):  
Quality Control ◽  
Next Generation Sequencing ◽  
Predictive Models ◽  
Next Generation Sequencing Data ◽  
Control Procedure ◽  
Next Generation ◽  
Sequencing Data ◽  
Statistical Guidelines ◽  
Ngs Data ◽  
Generation Sequencing

AbstractControlling quality of next generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterized common NGS quality features and developed a novel quality control procedure involving tree-based and deep learning classification algorithms. Predictive models, validated on internal data and external disease diagnostic datasets, are to some extent generalizable to data from unseen species. The derived statistical guidelines and predictive models represent a valuable resource for users of NGS data to better understand quality issues and perform automatic quality control. Our guidelines and software are available at the following URL: https://github.com/salbrec/seqQscorer.

scholarly journals seqQscorer: automated quality control of next-generation sequencing data using machine learning

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Steffen Albrecht ◽  
Maximilian Sprang ◽  
Miguel A. Andrade-Navarro ◽  
Jean-Fred Fontaine
Keyword(s):  
Quality Control ◽  
Next Generation Sequencing ◽  
Predictive Models ◽  
Next Generation Sequencing Data ◽  
Control Procedure ◽  
Next Generation ◽  
Sequencing Data ◽  
Statistical Guidelines ◽  
Ngs Data ◽  
Generation Sequencing

AbstractControlling quality of next-generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterize common NGS quality features and develop a novel quality control procedure involving tree-based and deep learning classification algorithms. Predictive models, validated on internal and external functional genomics datasets, are to some extent generalizable to data from unseen species. The derived statistical guidelines and predictive models represent a valuable resource for users of NGS data to better understand quality issues and perform automatic quality control. Our guidelines and software are available at https://github.com/salbrec/seqQscorer.

scholarly journals NGSremix: A software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data

2021 ◽  
Author(s):  
Anne Krogh Nøhr ◽  
Kristian Hanghøj ◽  
Genis Garcia Erill ◽  
Zilong Li ◽  
Ida Moltke ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Genetic Research ◽  
Likelihood Estimation ◽  
Software Tool ◽  
Estimation Methods ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Ngs Data ◽  
Generation Sequencing

Abstract Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C ++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.

scholarly journals Applying Ancestry and Sex Computation as a Quality Control Tool in Targeted Next-Generation Sequencing

2016 ◽  
Vol 145 (3) ◽  
pp. 308-315 ◽  
Author(s):  
Patrick C. Mathias ◽  
Emily H. Turner ◽  
Sheena M. Scroggins ◽  
Stephen J. Salipante ◽  
Noah G. Hoffman ◽  
...  
Keyword(s):  
Quality Control ◽  
Next Generation Sequencing ◽  
Next Generation ◽  
Targeted Next Generation Sequencing ◽  
Quality Control Tool ◽  
Generation Sequencing ◽  
Control Tool

scholarly journals Mining and Development of Novel SSR Markers Using Next Generation Sequencing (NGS) Data in Plants

Molecules ◽  
2018 ◽  
Vol 23 (2) ◽  
pp. 399 ◽  
Author(s):  
Sima Taheri ◽  
Thohirah Lee Abdullah ◽  
Mohd Yusop ◽  
Mohamed Hanafi ◽  
Mahbod Sahebi ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Ssr Markers ◽  
Next Generation ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing
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