|
KMID : 1132720200180010008
|
|
Genomics & Informatics
2020 Volume.18 No. 1 p.8 ~ p.8
|
|
|
Bioinformatics services for analyzing massive genomic datasets
|
|
Ko Gun-Hwan
Kim Pan-Gyu
Cho Young-Bum
Jeong Seong-Mun
Kim Jae-Yoon
Kim Kyoung-Hyoun
Lee Ho-Yeon
Han Ji-Yeon
Yu Nam-Hee
Ham Seok-Jin
Jang In-Soon
Kang Byung-Hee
Shin Sung-Uk
Kim Li-An
Lee Seung-Won
Nam Doug-U
Kim Ji-Hyun
Kim Nam-Shin
Kim Seon-Young
Lee Sang-Hyuk
Roh Tae-Young
Lee Byung-Wook
|
|
|
Abstract
|
|
|
|
The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.
|
|
|
KEYWORD
|
|
|
analysis pipeline, cloud computing, genomic data, web server, workflow system
|
|
|
FullTexts / Linksout information
|
|
|
|
|
Listed journal information
|
|
 
|
|