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gpart: human genome partitioning and visualization of high-density SNP data by identifying haplotype blocks

SUMMARY: For the analysis of high-throughput genomic data produced by next-generation sequencing (NGS) technologies, researchers need to identify linkage disequilibrium (LD) structure in the genome. In this work, we developed an R package gpart which provides clustering algorithms to define LD block...

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Detalles Bibliográficos
Autores principales: Kim, Sun Ah, Brossard, Myriam, Roshandel, Delnaz, Paterson, Andrew D, Bull, Shelley B, Yoo, Yun Joo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6821423/
https://www.ncbi.nlm.nih.gov/pubmed/31070701
http://dx.doi.org/10.1093/bioinformatics/btz308
Descripción
Sumario:SUMMARY: For the analysis of high-throughput genomic data produced by next-generation sequencing (NGS) technologies, researchers need to identify linkage disequilibrium (LD) structure in the genome. In this work, we developed an R package gpart which provides clustering algorithms to define LD blocks or analysis units consisting of SNPs. The visualization tool in gpart can display the LD structure and gene positions for up to 20 000 SNPs in one image. The gpart functions facilitate construction of LD blocks and SNP partitions for vast amounts of genome sequencing data within reasonable time and memory limits in personal computing environments. AVAILABILITY AND IMPLEMENTATION: The R package is available at https://bioconductor.org/packages/gpart. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.