1.Bioinformatics services for analyzing massive genomic datasets
Gunhwan KO ; 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
Genomics & Informatics 2020;18(1):e8-
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/.
2.Bioinformatics services for analyzing massive genomic datasets
Gunhwan KO ; 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
Genomics & Informatics 2020;18(1):e8-
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/.