Variant Calling in Next Generation Sequencing Data

Variant Calling ◽

Quality Score ◽

Identification Accuracy ◽

Sequencing Error ◽

Next Generation ◽

Sequencing Data ◽

Base Quality Score ◽

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.

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):

Gene Mutations ◽

Variant Calling ◽

Amplicon Sequencing ◽

Supplementary Information ◽

Next Generation ◽

Sequencing Data ◽

Ras Gene ◽

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.

NGSphy: phylogenomic simulation of next-generation sequencing data

10.1101/197715 ◽

2017 ◽

Author(s):

Merly Escalona ◽

Sara Rocha ◽

David Posada

Keyword(s):

Variant Calling ◽

Gene Families ◽

Common Species ◽

Phylogenomic Analysis ◽

Next Generation ◽

Sequencing Data ◽

Sequencing Technologies ◽

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]

Correction: Coval: Improving Alignment Quality and Variant Calling Accuracy for Next-Generation Sequencing Data

PLoS ONE ◽

10.1371/annotation/cc88d2b5-36e8-441a-ab5f-58a9ed143d6b ◽

2014 ◽

Vol 9 (1) ◽

Author(s):

Shunichi Kosugi ◽

Satoshi Natsume ◽

Kentaro Yoshida ◽

Daniel MacLean ◽

Liliana Cano ◽

...

Keyword(s):

Variant Calling ◽

Alignment Quality ◽

Next Generation ◽

Sequencing Data ◽

Evaluating Variant Calling Tools for Non-Matched Next-Generation Sequencing Data

Scientific Reports ◽

10.1038/srep43169 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 101

Author(s):

Sarah Sandmann ◽

Aniek O. de Graaf ◽

Mohsen Karimi ◽

Bert A. van der Reijden ◽

Eva Hellström-Lindberg ◽

...

Keyword(s):

Variant Calling ◽

Next Generation ◽

Sequencing Data ◽

Coval: Improving Alignment Quality and Variant Calling Accuracy for Next-Generation Sequencing Data

PLoS ONE ◽

10.1371/journal.pone.0075402 ◽

2013 ◽

Vol 8 (10) ◽

pp. e75402 ◽

Cited By ~ 40

Author(s):

Shunichi Kosugi ◽

Satoshi Natsume ◽

Kentaro Yoshida ◽

Daniel MacLean ◽

Liliana Cano ◽

...

Keyword(s):

Variant Calling ◽

Alignment Quality ◽

Next Generation ◽

Sequencing Data ◽

Targeted variant detection using unaligned RNA-Seq reads

Life Science Alliance ◽

10.26508/lsa.201900336 ◽

2019 ◽

Vol 2 (4) ◽

pp. e201900336 ◽

Cited By ~ 4

Author(s):

Eric Olivier Audemard ◽

Patrick Gendron ◽

Albert Feghaly ◽

Vincent-Philippe Lavallée ◽

Josée Hébert ◽

...

Keyword(s):

Variant Calling ◽

The Cancer Genome Atlas ◽

Next Generation ◽

Sequencing Data ◽

Targeted Mutation ◽

Computationally Intensive ◽

And Performance ◽

Mutations identified in acute myeloid leukemia patients are useful for prognosis and for selecting targeted therapies. Detection of such mutations using next-generation sequencing data requires a computationally intensive read mapping step followed by several variant calling methods. Targeted mutation identification drastically shifts the usual tradeoff between accuracy and performance by concentrating all computations over a small portion of sequence space. Here, we present km, an efficient approach leveraging k-mer decomposition of reads to identify targeted mutations. Our approach is versatile, as it can detect single-base mutations, several types of insertions and deletions, as well as fusions. We used two independent cohorts (The Cancer Genome Atlas and Leucegene) to show that mutation detection by km is fast, accurate, and mainly limited by sequencing depth. Therefore, km allows the establishment of fast diagnostics from next-generation sequencing data and could be suitable for clinical applications.

VisVariant: A java program to visualise genetic variants in next-generation sequencing data

10.1101/2021.02.12.431037 ◽

2021 ◽

Author(s):

King Wai Lau ◽

Michelle Kleeman ◽

Caroline Reuter ◽

Attila Lorincz

Keyword(s):

Genetic Variants ◽

Variant Calling ◽

Sequence Information ◽

Next Generation ◽

Sequencing Data ◽

Java Program ◽

Ngs Data ◽

AbstractSummaryExtremely large datasets are impossible or very difficult for humans to comprehend by standard mental approaches. Intuitive visualization of genetic variants in genomic sequencing data could help in the review and confirmation process of variants called by automated variant calling programs. To help facilitate interpretation of genetic variant next-generation sequencing (NGS) data we developed VisVariant, a customizable visualization tool that creates a figure showing the overlapping sequence information of thousands of individual reads including the variant and flanking regions.Availability and implementationDetailed information on how to download, install and run VisVariant together with an example is available on our github website [https://github.com/hugging-biorxiv/visvariant].

Benchmarking Variant Identification Tools for Plant Diversity Discovery

10.21203/rs.2.9666/v2 ◽

2019 ◽

Author(s):

Xing Wu ◽

Christopher Heffelfinger ◽

Hongyu Zhao ◽

Stephen L. Dellaporta

Keyword(s):

High Throughput Sequencing ◽

Crop Improvement ◽

Variant Calling ◽

Next Generation ◽

Sequencing Data ◽

Variant Discovery ◽

Variant Filtering ◽

Abstract Background The ability to accurately and comprehensively identify genomic variations is critical for plant studies utilizing high-throughput sequencing. Most bioinformatics tools for processing next-generation sequencing data were originally developed and tested in human studies, raising questions as to their efficacy for plant research. A detailed evaluation of the entire variant calling pipeline, including alignment, variant calling, variant filtering, and imputation was performed on different programs using both simulated and real plant genomic datasets. Results A comparison of SOAP2, Bowtie2, and BWA-MEM found that BWA-MEM was consistently able to align the most reads with high accuracy, whereas Bowtie2 had the highest overall accuracy. Comparative results of GATK HaplotypCaller versus SAMtools mpileup indicated that the choice of variant caller affected precision and recall differentially depending on the levels of diversity, sequence coverage and genome complexity. A cross-reference experiment of S. lycopersicum and S. pennellii reference genomes revealed the inadequacy of single reference genome for variant discovery that includes distantly-related plant individuals. Machine-learning-based variant filtering strategy outperformed the traditional hard-cutoff strategy resulting in higher number of true positive variants and fewer false positive variants. A 2-step imputation method, which utilized a set of high-confidence SNPs as the reference panel, showed up to 60% higher accuracy than direct LD-based imputation. Conclusions Programs in the variant discovery pipeline have different performance on plant genomic dataset. Choice of the programs is subjected to the goal of the study and available resources. This study serves as an important guiding information for plant biologists utilizing next-generation sequencing data for diversity characterization and crop improvement.

Low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach

BMC Bioinformatics ◽

10.1186/s12859-021-04090-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jeffrey N. Dudley ◽

◽

Celine S. Hong ◽

Marwan A. Hawari ◽

Jasmine Shwetar ◽

...

Keyword(s):

Somatic Mosaicism ◽

Variant Calling ◽

Next Generation ◽

Sequencing Data ◽

Low Level ◽

Pathogenic Variants ◽

Segmental Overgrowth ◽

Abstract Background The widespread use of next-generation sequencing has identified an important role for somatic mosaicism in many diseases. However, detecting low-level mosaic variants from next-generation sequencing data remains challenging. Results Here, we present a method for Position-Based Variant Identification (PBVI) that uses empirically-derived distributions of alternate nucleotides from a control dataset. We modeled this approach on 11 segmental overgrowth genes. We show that this method improves detection of single nucleotide mosaic variants of 0.01–0.05 variant allele fraction compared to other low-level variant callers. At depths of 600 × and 1200 ×, we observed > 85% and > 95% sensitivity, respectively. In a cohort of 26 individuals with somatic overgrowth disorders PBVI showed improved signal to noise, identifying pathogenic variants in 17 individuals. Conclusion PBVI can facilitate identification of low-level mosaic variants thus increasing the utility of next-generation sequencing data for research and diagnostic purposes.