Bioinformatics services for analyzing massive genomic datasets
- Author:
Gunhwan KO
1
;
Pan-Gyu KIM
;
Youngbum CHO
;
Seongmun JEONG
;
Jae-Yoon KIM
;
Kyoung Hyoun KIM
;
Ho-Yeon LEE
;
Jiyeon HAN
;
Namhee YU
;
Seokjin HAM
;
Insoon JANG
;
Byunghee KANG
;
Sunguk SHIN
;
Lian KIM
;
Seung-Won LEE
;
Dougu NAM
;
Jihyun F. KIM
;
Namshin KIM
;
Seon-Young KIM
;
Sanghyuk LEE
;
Tae-Young ROH
;
Byungwook LEE
Author Information
- Publication Type:Original article
- From:Genomics & Informatics 2020;18(1):e8-
- CountryRepublic of Korea
- Language:English
- 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/.
