GT-WGS: an efficient and economic tool for large-scale WGS analyses based on the AWS cloud service

BACKGROUND: Whole-genome sequencing (WGS) plays an increasingly important role in clinical practice and public health. Due to the big data size, WGS data analysis is usually compute-intensive and IO-intensive. Currently it usually takes 30 to 40 h to finish a 50× WGS analysis task, which is far from...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Yiqi, Li, Gen, Ma, Mark, He, Fazhong, Song, Zhuo, Zhang, Wei, Wu, Chengkun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5780748/
https://www.ncbi.nlm.nih.gov/pubmed/29363427
http://dx.doi.org/10.1186/s12864-017-4334-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5780748/
https://www.ncbi.nlm.nih.gov/pubmed/29363427
http://dx.doi.org/10.1186/s12864-017-4334-x

Ejemplares similares