Comparison of seven SNP calling pipelines for the next-generation sequencing data of chickens

Abstract

Single nucleotide polymorphisms (SNPs) are widely used in genome-wide association studies and population genetics analyses. Next-generation sequencing (NGS) has become convenient, and many SNP-calling pipelines have been developed for human NGS data. We took advantage of a gap knowledge in selecting the appropriated SNP calling pipeline to handle with high-throughput NGS data. To fill this gap, we studied and compared seven SNP calling pipelines, which include 16GT, genome analysis toolkit (GATK), Bcftools-single (Bcftools single sample mode), Bcftools-multiple (Bcftools multiple sample mode), VarScan2-single (VarScan2 single sample mode), VarScan2-multiple (VarScan2 multiple sample mode) and Freebayes pipelines, using 96 NGS data with the different depth gradients of approximately 5X, 10X, 20X, 30X, 40X, and 50X coverage from 16 Rhode Island Red chickens. The sixteen chickens were also genotyped with a 50K SNP array, and the sensitivity and specificity of each pipeline were assessed by comparison to the results of SNP arrays. For each pipeline, except Freebayes, the number of detected SNPs increased as the input read depth increased. In comparison with other pipelines, 16GT, followed by Bcftools-multiple, obtained the most SNPs when the input coverage exceeded 10X, and Bcftools-multiple obtained the most when the input was 5X and 10X. The sensitivity and specificity of each pipeline increased with increasing input. Bcftools-multiple had the highest sensitivity numerically when the input ranged from 5X to 30X, and 16GT showed the highest sensitivity when the input was 40X and 50X. Bcftools-multiple also had the highest specificity, followed by GATK, at almost all input levels. For most calling pipelines, there were no obvious changes in SNP numbers, sensitivities or specificities beyond 20X. In conclusion, (1) if only SNPs were detected, the sequencing depth did not need to exceed 20X; (2) the Bcftools-multiple may be the best choice for detecting SNPs from chicken NGS data, but for a single sample or sequencing depth greater than 20X, 16GT was recommended. Our findings provide a reference for researchers to select suitable pipelines to obtain SNPs from the NGS data of chickens or nonhuman animals.

Genotyping‐by‐sequencing reveals a high number and quality of single nucleotide polymorphisms in guinea pigs (Cavia porcellus) from the Peruvian Andes

“…In addition, different SNP calling tools may report different numbers of SNPs. In a recent comparison using the same dataset at 5-10× sequencing depth (Liu et al, 2022), bcftools, the SNP calling tool used in our study, reported more SNPs than gatk, the SNP calling pipeline used in the Erhualian pigs (Wang et al, 2017) and the Pekin ducks studies (Zhu et al, 2016). Having these considerations, our reported parameters will be an initial reference for future optimization of GBS-based genetic diversity studies on guinea pig populations.…”

Evaluation of 10 Different Pipelines for Bacterial Single-Nucleotide Variant Detection

“…18 Recognized as the industry standard for germline SNV detection, GATK enjoys widespread adoption and has earned a reputation for its reliability and accuracy. 19 Snippy uses a combination of powerful algorithms to analyze genomic data. Leveraging BWA for alignment, Freebayes for variant calling and SnpEff for annotation, Snippy can adeptly identify SNV presence between a haploid reference genome and one or more sequenced samples.…”

“…Notably, GATK exhibits proficiency in detecting both SNVs and insertions/deletions, ensuring comprehensive genomic variation analysis 18 . Recognized as the industry standard for germline SNV detection, GATK enjoys widespread adoption and has earned a reputation for its reliability and accuracy 19 . Snippy uses a combination of powerful algorithms to analyze genomic data.…”

Evaluating discordant somatic calls across mutation discovery approaches to minimize false negative drug-resistant findings

“…However, previous studies have shown that the aligner and the variant caller could heavily influence the results of variant detection (O'Rawe et al 2013, Pabinger et al 2014. Additionally, several studies indicated that different tool combinations often generate discordant variant detection results (Cornish et al 2015, Kumaran et al 2019, Liang et al 2019, Liu et al 2022, Wang et al 2019, Zhao et al 2020.…”