TASSEL-GBS: A High Capacity Genotyping by Sequencing Analysis Pipeline

TLDR

Genotyping by sequencing (GBS) is a next‑generation sequencing method that uses reduced representation to enable high‑throughput genotyping of many individuals at a large number of SNP markers, and it is straightforward, robust, cost‑effective, and widely applied across species. The authors present tassel‑gbs, a bioinformatics pipeline that efficiently converts raw GBS sequence data into SNP genotypes. Tassel‑gbs runs on modest computing resources, scales from small to millions of SNPs in up to 100,000 individuals, can use a reference or pseudo‑reference genome, and applies population‑genetic SNP filters that lowered the error rate to 0.0042 in a large maize study. The GBS assay together with tassel‑gbs provides robust tools for studying genomic diversity.

Abstract

Genotyping by sequencing (GBS) is a next generation sequencing based method that takes advantage of reduced representation to enable high throughput genotyping of large numbers of individuals at a large number of SNP markers. The relatively straightforward, robust, and cost-effective GBS protocol is currently being applied in numerous species by a large number of researchers. Herein we describe a bioinformatics pipeline, tassel-gbs, designed for the efficient processing of raw GBS sequence data into SNP genotypes. The tassel-gbs pipeline successfully fulfills the following key design criteria: (1) Ability to run on the modest computing resources that are typically available to small breeding or ecological research programs, including desktop or laptop machines with only 8–16 GB of RAM, (2) Scalability from small to extremely large studies, where hundreds of thousands or even millions of SNPs can be scored in up to 100,000 individuals (e.g., for large breeding programs or genetic surveys), and (3) Applicability in an accelerated breeding context, requiring rapid turnover from tissue collection to genotypes. Although a reference genome is required, the pipeline can also be run with an unfinished "pseudo-reference" consisting of numerous contigs. We describe the tassel-gbs pipeline in detail and benchmark it based upon a large scale, species wide analysis in maize (Zea mays), where the average error rate was reduced to 0.0042 through application of population genetic-based SNP filters. Overall, the GBS assay and the tassel-gbs pipeline provide robust tools for studying genomic diversity.