Validation and assessment of variant calling pipelines for next-generation sequencing

Tumor DNA sequencing results can have important clinical implications. However, its use is often limited by low DNA input, owing to small tumor biopsy size. To help overcome this limitation we have developed a simple improvement to a commonly used next-generation sequencing (NGS) capture-based library preparation method using formalin-fixed paraffin-embedded-derived tumor DNA. By using on-bead PCR for pre-capture library generation we show that library yields are dramatically increased, resulting in decreased sample failure rates. Improved yields allowed for a reduction in PCR cycles, which translated into improved sequencing parameters without affecting variant calling. This methodology should be applicable to any NGS system in which input DNA is a limiting factor.

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A customized scaffolds approach for the detection and phasing of complex variants by next-generation sequencing

Scientific Reports ◽

10.1038/s41598-020-71471-3 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Qiandong Zeng ◽

Natalia T. Leach ◽

Zhaoqing Zhou ◽

Hui Zhu ◽

Jean A. Smith ◽

...

Keyword(s):

Next Generation Sequencing ◽

Variant Calling ◽

Causative Mutation ◽

Next Generation ◽

Single Nucleotide ◽

Highly Sensitive ◽

Highly Sensitive Detection ◽

Next Generation Sequencing Ngs ◽

Reference Bias ◽

Generation Sequencing

Abstract Next-generation sequencing (NGS) is widely used in genetic testing for the highly sensitive detection of single nucleotide changes and small insertions or deletions. However, detection and phasing of structural variants, especially in repetitive or homologous regions, can be problematic due to uneven read coverage or genome reference bias, resulting in false calls. To circumvent this challenge, a computational approach utilizing customized scaffolds as supplementary reference sequences for read alignment was developed, and its effectiveness demonstrated with two CBS gene variants: NM_000071.2:c.833T>C and NM_000071.2:c.[833T>C; 844_845ins68]. Variant c.833T>C is a known causative mutation for homocystinuria, but is not pathogenic when in cis with the insertion, c.844_845ins68, because of alternative splicing. Using simulated reads, the custom scaffolds method resolved all possible combinations with 100% accuracy and, based on > 60,000 clinical specimens, exceeded the performance of current approaches that only align reads to GRCh37/hg19 for the detection of c.833T>C alone or in cis with c.844_845ins68. Furthermore, analysis of two 1000 Genomes Project trios revealed that the c.[833T>C; 844_845ins68] complex variant had previously been undetected in these datasets, likely due to the alignment method used. This approach can be configured for existing workflows to detect other challenging and potentially underrepresented variants, thereby augmenting accurate variant calling in clinical NGS testing.

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

10.1101/130732 ◽

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.

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Pisces: An Accurate and Versatile Variant Caller for Somatic and Germline Next-Generation Sequencing Data

10.1101/291641 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tamsen Dunn ◽

Gwenn Berry ◽

Dorothea Emig-Agius ◽

Yu Jiang ◽

Serena Lei ◽

...

Keyword(s):

Next Generation Sequencing ◽

Gene Mutations ◽

Variant Calling ◽

Amplicon Sequencing ◽

Supplementary Information ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Ras Gene ◽

Generation Sequencing

AbstractMotivationNext-Generation Sequencing (NGS) technology is transitioning quickly from research labs to clinical settings. The diagnosis and treatment selection for many acquired and autosomal conditions necessitate a method for accurately detecting somatic and germline variants, suitable for the clinic.ResultsWe have developed Pisces, a rapid, versatile and accurate small variant calling suite designed for somatic and germline amplicon sequencing applications. Pisces accuracy is achieved by four distinct modules, the Pisces Read Stitcher, Pisces Variant Caller, the Pisces Variant Quality Recalibrator, and the Pisces Variant Phaser. Each module incorporates a number of novel algorithmic strategies aimed at reducing noise or increasing the likelihood of detecting a true variant.AvailabilityPisces is distributed under an open source license and can be downloaded from https://github.com/Illumina/Pisces. Pisces is available on the BaseSpace™ SequenceHub as part of the TruSeq Amplicon workflow and the Illumina Ampliseq Workflow. Pisces is distributed on Illumina sequencing platforms such as the MiSeq™, and is included in the Praxis™ Extended RAS Panel test which was recently approved by the FDA for the detection of multiple RAS gene [email protected] informationSupplementary data are available online.

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NGSphy: phylogenomic simulation of next-generation sequencing data

10.1101/197715 ◽

2017 ◽

Author(s):

Merly Escalona ◽

Sara Rocha ◽

David Posada

Keyword(s):

Next Generation Sequencing ◽

Variant Calling ◽

Gene Families ◽

Common Species ◽

Next Generation Sequencing Data ◽

Phylogenomic Analysis ◽

Next Generation ◽

Sequencing Data ◽

Sequencing Technologies ◽

Generation Sequencing

AbstractMotivationAdvances in sequencing technologies have made it feasible to obtain massive datasets for phylogenomic inference, often consisting of large numbers of loci from multiple species and individuals. The phylogenomic analysis of next-generation sequencing (NGS) data implies a complex computational pipeline where multiple technical and methodological decisions are necessary that can influence the final tree obtained, like those related to coverage, assembly, mapping, variant calling and/or phasing.ResultsTo assess the influence of these variables we introduce NGSphy, an open-source tool for the simulation of Illumina reads/read counts obtained from haploid/diploid individual genomes with thousands of independent gene families evolving under a common species tree. In order to resemble real NGS experiments, NGSphy includes multiple options to model sequencing coverage (depth) heterogeneity across species, individuals and loci, including off-target or uncaptured loci. For comprehensive simulations covering multiple evolutionary scenarios, parameter values for the different replicates can be sampled from user-defined statistical distributions.AvailabilitySource code, full documentation and tutorials including a quick start guide are available at http://github.com/merlyescalona/[email protected]. [email protected]

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