Journal Article

Benchmarking variant identification tools for plant diversity discovery

Zhao, Hongyu, Heffelfinger, Christopher, Dellaporta, Stephen L., Wu, Xing

2019

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Animal Genetics and Genomics / Benchmarking / Biodiversity / Bioinformatics / Biomedical and Life Sciences / Computational biology / Crop improvement / Databases, Genetic / Datasets / Filtration / Genetic aspects / Genetic research / Genetic Variation / Genome, Plant - genetics / Genomes / Genomics / Genomics - methods / High-throughput screening (Biochemical assaying) / Imputation / Information processing / Learning algorithms / Life Sciences / Machine learning / Microarrays / Microbial Genetics and Genomics / Next-generation sequencing / Nucleotide sequence / Plant diversity / Plant Genetics and Genomics / Plant genomics / Proteomics / Read alignment / Research Article / Single-nucleotide polymorphism / Solanum / Variant calling / Variant filtering