OPENMENDEL: a cooperative programming project for statistical genetics

Autor: Jing Zhai, Benjamin B. Chu, Janet S. Sinsheimer, Gordon D. Mosher, Christopher A. German, Kenneth Lange, Jin Zhou, Juhyun Kim, Eric M. Sobel, Douglas M. Bates, Hua Zhou, Sarah S. Ji, Jeanette C. Papp, Kevin L. Keys, Seyoon Ko
Jazyk: angličtina
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
Computer science
Big data
Cloud computing
Genome-wide association study
Software
Models
GWAS
Genetics (clinical)
Genetics & Heredity
0303 health sciences
Genome
Data manipulation language
030305 genetics & heredity
Single Nucleotide
Statistical
Open source
Variety (cybernetics)
Networking and Information Technology R&D
Networking and Information Technology R&D (NITRD)
Statistical genetics
q-bio.GN
Algorithms
Human
Collaborative programming
Context (language use)
Statistics - Applications
Polymorphism
Single Nucleotide
Article
Paediatrics and Reproductive Medicine
03 medical and health sciences
Complementary and Alternative Medicine
Genetics
Humans
Statistical genomics
Applications (stat.AP)
Quantitative Biology - Genomics
Polymorphism
stat.AP
030304 developmental biology
Genomics (q-bio.GN)
Models
Statistical
business.industry
Genome
Human
Human Genome
Computational Biology
Data science
Genetic epidemiology
FOS: Biological sciences
Computational statistics
Programming Languages
business
Genome-Wide Association Study
Zdroj: Zhou, Hua; Sinsheimer, Janet S; Bates, Douglas M; Chu, Benjamin B; German, Christopher A; Ji, Sarah S; et al.(2019). OPENMENDEL: a cooperative programming project for statistical genetics.. Human genetics. doi: 10.1007/s00439-019-02001-z. UCLA: Retrieved from: http://www.escholarship.org/uc/item/06k1386p
Human genetics, vol 139, iss 1
Hum Genet
DOI: 10.1007/s00439-019-02001-z.
Popis: Statistical methods for genomewide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDELproject (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project.
16 pages, 2 figures, 2 tables
Databáze: OpenAIRE
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