TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline.

Publication Dbxref
PMID:24587335
Title
TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline.
Publication Type
Journal Article
Additional Publication Type(s)
Research Support, U.S. Gov't, Non-P.H.S.
Series Name
PloS one
Volume
9
Publication Year
2014
Issue
2
Page Numbers
e90346
DOI
10.1371/journal.pone.0090346
Journal Abbreviation
PLoS One
EISSN
1932-6203
Publication Date
2014
Unique Local Identifier

Glaubitz JC, Casstevens TM, Lu F, Harriman J, Elshire RJ, Sun Q, Buckler ES. TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline.. PloS one. 2014; 9(2):e90346.

Citation
Glaubitz JC, Casstevens TM, Lu F, Harriman J, Elshire RJ, Sun Q, Buckler ES. TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline.. PloS one. 2014; 9(2):e90346.
ISSN
1932-6203
Language Abbr
eng
Publication Model
Electronic-eCollection
Authors
Glaubitz JC, Casstevens TM, Lu F, Harriman J, Elshire RJ, Sun Q, Buckler ES
Language
English
Elocation
10.1371/journal.pone.0090346
Journal Country
United States
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.

Database Reference Annotations
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