scholarly journals Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data

2019 ◽  
Author(s):  
Tiffany M. Delhomme ◽  
Patrice H. Avogbe ◽  
Aurélie Gabriel ◽  
Nicolas Alcala ◽  
Noemie Leblay ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Error Rate ◽  
Somatic Mutations ◽  
Next Generation Sequencing Data ◽  
Sequencing Error ◽  
Next Generation ◽  
Sequencing Error Rate ◽  
Main Challenge ◽  
A Genome ◽  
Generation Sequencing

ABSTRACTThe emergence of Next-Generation Sequencing (NGS) has revolutionized the way of reaching a genome sequence, with the promise of potentially providing a comprehensive characterization of DNA variations. Nevertheless, detecting somatic mutations is still a difficult problem, in particular when trying to identify low abundance mutations such as subclonal mutations, tumour-derived alterations in body fluids or somatic mutations from histological normal tissue. The main challenge is to precisely distinguish between sequencing artefacts and true mutations, particularly when the latter are so rare they reach similar abundance levels as artefacts. Here, we present needlestack, a highly sensitive variant caller, which directly learns from the data the level of systematic sequencing errors to accurately call mutations. Needlestack is based on the idea that the sequencing error rate can be dynamically estimated from analyzing multiple samples together. We show that the sequencing error rate varies across alterations, illustrating the need to precisely estimate it. We evaluate the performance of needlestack for various types of variations, and we show that needlestack is robust among positions and outperforms existing state-of-the-art method for low abundance mutations. Needlestack, along with its source code is freely available on the GitHub plateform: https://github.com/IARCbioinfo/needlestack.

scholarly journals Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Tiffany M Delhomme ◽  
Patrice H Avogbe ◽  
Aurélie A G Gabriel ◽  
Nicolas Alcala ◽  
Noemie Leblay ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Error Rate ◽  
Somatic Mutations ◽  
Next Generation Sequencing Data ◽  
Sequencing Error ◽  
Next Generation ◽  
Sequencing Error Rate ◽  
Main Challenge ◽  
A Genome ◽  
Generation Sequencing

Abstract The emergence of next-generation sequencing (NGS) has revolutionized the way of reaching a genome sequence, with the promise of potentially providing a comprehensive characterization of DNA variations. Nevertheless, detecting somatic mutations is still a difficult problem, in particular when trying to identify low abundance mutations, such as subclonal mutations, tumour-derived alterations in body fluids or somatic mutations from histological normal tissue. The main challenge is to precisely distinguish between sequencing artefacts and true mutations, particularly when the latter are so rare they reach similar abundance levels as artefacts. Here, we present needlestack, a highly sensitive variant caller, which directly learns from the data the level of systematic sequencing errors to accurately call mutations. Needlestack is based on the idea that the sequencing error rate can be dynamically estimated from analysing multiple samples together. We show that the sequencing error rate varies across alterations, illustrating the need to precisely estimate it. We evaluate the performance of needlestack for various types of variations, and we show that needlestack is robust among positions and outperforms existing state-of-the-art method for low abundance mutations. Needlestack, along with its source code is freely available on the GitHub platform: https://github.com/IARCbioinfo/needlestack.

scholarly journals Lacer: accurate base quality score recalibration for improving variant calling from next-generation sequencing data in any organism

2017 ◽  
Author(s):  
Jade C.S. Chung ◽  
Swaine L. Chen
Keyword(s):  
Next Generation Sequencing ◽  
Variant Calling ◽  
Quality Score ◽  
Identification Accuracy ◽  
Next Generation Sequencing Data ◽  
Sequencing Error ◽  
Next Generation ◽  
Sequencing Data ◽  
Base Quality Score ◽  
Generation Sequencing

AbstractNext-generation sequencing data is accompanied by quality scores that quantify sequencing error. Inaccuracies in these quality scores propagate through all subsequent analyses; thus base quality score recalibration is a standard step in many next-generation sequencing workflows, resulting in improved variant calls. Current base quality score recalibration algorithms rely on the assumption that sequencing errors are already known; for human resequencing data, relatively complete variant databases facilitate this. However, because existing databases are still incomplete, recalibration is still inaccurate; and most organisms do not have variant databases, exacerbating inaccuracy for non-human data. To overcome these logical and practical problems, we introduce Lacer, which recalibrates base quality scores without assuming knowledge of correct and incorrect bases and without requiring knowledge of common variants. Lacer is the first logically sound, fully general, and truly accurate base recalibrator. Lacer enhances variant identification accuracy for resequencing data of human as well as other organisms (which are not accessible to current recalibrators), simultaneously improving and extending the benefits of base quality score recalibration to nearly all ongoing sequencing projects. Lacer is available at: https://github.com/swainechen/lacer.

scholarly journals SequencErr: measuring and suppressing sequencer errors in next-generation sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Eric M. Davis ◽  
Yu Sun ◽  
Yanling Liu ◽  
Pandurang Kolekar ◽  
Ying Shao ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Error Rate ◽  
Control Method ◽  
Error Rates ◽  
Computational Method ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Flow Cells ◽  
Generation Sequencing

Abstract Background There is currently no method to precisely measure the errors that occur in the sequencing instrument/sequencer, which is critical for next-generation sequencing applications aimed at discovering the genetic makeup of heterogeneous cellular populations. Results We propose a novel computational method, SequencErr, to address this challenge by measuring the base correspondence between overlapping regions in forward and reverse reads. An analysis of 3777 public datasets from 75 research institutions in 18 countries revealed the sequencer error rate to be ~ 10 per million (pm) and 1.4% of sequencers and 2.7% of flow cells have error rates > 100 pm. At the flow cell level, error rates are elevated in the bottom surfaces and > 90% of HiSeq and NovaSeq flow cells have at least one outlier error-prone tile. By sequencing a common DNA library on different sequencers, we demonstrate that sequencers with high error rates have reduced overall sequencing accuracy, and removal of outlier error-prone tiles improves sequencing accuracy. We demonstrate that SequencErr can reveal novel insights relative to the popular quality control method FastQC and achieve a 10-fold lower error rate than popular error correction methods including Lighter and Musket. Conclusions Our study reveals novel insights into the nature of DNA sequencing errors incurred on DNA sequencers. Our method can be used to assess, calibrate, and monitor sequencer accuracy, and to computationally suppress sequencer errors in existing datasets.

scholarly journals Design and Statistical Analysis of Pooled Next Generation Sequencing for Rare Variants

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Tao Wang ◽  
Chang-Yun Lin ◽  
Yuanhao Zhang ◽  
Ruofeng Wen ◽  
Kenny Ye
Keyword(s):  
Next Generation Sequencing ◽  
Error Rate ◽  
Statistical Power ◽  
Rare Variants ◽  
Good Control ◽  
Testing Procedure ◽  
Sequencing Error ◽  
Type I ◽  
Next Generation ◽  
Generation Sequencing

Next generation sequencing (NGS) is a revolutionary technology for biomedical research. One highly cost-efficient application of NGS is to detect disease association based on pooled DNA samples. However, several key issues need to be addressed for pooled NGS. One of them is the high sequencing error rate and its high variability across genomic positions and experiment runs, which, if not well considered in the experimental design and analysis, could lead to either inflated false positive rates or loss in statistical power. Another important issue is how to test association of a group of rare variants. To address the first issue, we proposed a new blocked pooling design in which multiple pools of DNA samples from cases and controls are sequenced together on same NGS functional units. To address the second issue, we proposed a testing procedure that does not require individual genotypes but by taking advantage of multiple DNA pools. Through a simulation study, we demonstrated that our approach provides a good control of the type I error rate, and yields satisfactory power compared to the test-based on individual genotypes. Our results also provide guidelines for designing an efficient pooled.

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.

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