The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
TASSEL-GBS: A High Capacity Genotyping by Sequencing Analysis Pipeline
Open accessAbstract
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.
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TASSEL: software for association mapping of complex traits in diverse samples
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Self-citationAn integrated map of genetic variation from 1,092 human genomes
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A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species
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Self-citationRapid SNP Discovery and Genetic Mapping Using Sequenced RAD Markers
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Cited by
Pearl millet genome sequence provides a resource to improve agronomic traits in arid environments
Varshney, R. K., Shi, C., Thudi, M., Mariac, C., Wallace, J., Qi, P., Zhang, H., Zhao, Y., Wang, X., Rathore, A., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3943 · Cited by 513
Self-citationConstruction of the third-generation Zea mays haplotype map
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Improving grain yield, stress resilience and quality of bread wheat using large-scale genomics
Juliana, P., Poland, J., Huerta-Espino, J., Shrestha, S., Crossa, J., Crespo-Herrera, L., Toledo, F. H., Govindan, V., Mondal, S., Kumar, U., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0496-6 · Cited by 258
Zhang, X., Pérez-Rodríguez, P., Semagn, K., Beyene, Y., Babu, R., López-Cruz, M. A., San Vicente, F., Olsen, M., Buckler, E., Jannink, J.-L., et al. (2015) · Heredity · doi:10.1038/hdy.2014.99 · Cited by 212
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Metadata
- Authors
- Glaubitz, J. C., Casstevens, T. M., Lu, F., Harriman, J., Elshire, R. J., Sun, Q., & Buckler, E. S.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-02-28
- Volume
- 9
- Issue
- 2
- Pages
- e90346
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W1972216668
- Cited by
- 1,625
- References
- 60
- FWCI
- 67.76
- Type
- journal article
- Language
- en
- Open access
- gold
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