Cyberinfrastructure for Life Sciences - iAnimal Resources for Genomics and Other Data Driven Biology

Reecy, J.; Carson, J.; McCarthy, F.; Koltes, J.; Fritz-Waters, E.; Williams, J.; Lyons, E.; Baes, Christine F.; Vaughn, M. (2014). Cyberinfrastructure for Life Sciences - iAnimal Resources for Genomics and Other Data Driven Biology In: WCGALP 2014 : Proceedings of the 10th World Congress on Genetics Applied to Livestock Production. Vancouver. 17.-22.08.2014.

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Whole genome sequence, SNPs, copy number variation, phenotypes and other “-omics” data underlie evidence-based estimations of breeding value. Unfortunately, the computational resources (data storage, high-performance computing, analysis pipelines, etc.) that exploit this knowledge are limited in availability – many investigations are therefore restricted to the commercial sector or well-funded academic programs. Cyberinfrastructure developed by the iPlant Collaborative (NSF-#DBI0735191) and its extension iAnimal (USDA-#2013-67015-21231) provides the animal breeding community a comprehensive and freely available platform for the storage, sharing, and analyses of large datasets – from genomes to phenotype data. iPlant/iAnimal tools support a variety of genotype-phenotype related analyses in a platform that accommodates every level of user – from breeder to bioinformatician. These tools have been used to develop scalable, accessible versions of common workflows required for applying sequencing to livestock genomics.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

School of Agricultural, Forest and Food Sciences > Abteilung Agronomie

Name:

Reecy, J.; Carson, J.; McCarthy, F.; Koltes, J.; Fritz-Waters, E.; Williams, J.; Lyons, E.; Baes, Christine F. and Vaughn, M.

Subjects:

Q Science > QH Natural history > QH426 Genetics
S Agriculture > S Agriculture (General)

Language:

English

Submitter:

Admin import user

Date Deposited:

10 Mar 2020 11:29

Last Modified:

18 May 2020 15:24

Related URLs:

Uncontrolled Keywords:

bioinformatics; breeding; analysis pipeline; high-performance computing; next generation sequencing; variant calling

ARBOR DOI:

10.24451/arbor.7399

URI:

https://arbor.bfh.ch/id/eprint/7399

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