Benchmarking Variant Identification Tools for Plant Diversity Discovery

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.

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Benchmarking Variant Identification Tools for Plant Diversity Discovery

10.21203/rs.2.9666/v1 ◽

2019 ◽

Author(s):

Xing Wu ◽

Christopher Heffelfinger ◽

Hongyu Zhao ◽

Stephen L. Dellaporta

Keyword(s):

Next Generation Sequencing ◽

High Throughput Sequencing ◽

Crop Improvement ◽

Variant Calling ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Variant Discovery ◽

Variant Filtering ◽

Generation Sequencing

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. The 2-step imputation which utilized a set of high-confidence SNPs as the reference panel showed up to 60% higher accuracy than direct LD-based imputation method. 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.

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Improved variant discovery through local re-alignment of short-read next-generation sequencing data using SRMA

Genome Biology ◽

10.1186/gb-2010-11-10-r99 ◽

2010 ◽

Vol 11 (10) ◽

Cited By ~ 53

Author(s):

Nils Homer ◽

Stanley F Nelson

Keyword(s):

Next Generation Sequencing ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Short Read ◽

Variant Discovery ◽

Generation Sequencing

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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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