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 LeeTae Young Roh, Byungwook Lee

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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 down-stream analysis of genome data. Bio-Express web service is freely available at https://www. bioexpress.re.kr/.

Original languageEnglish
Article numbere8
JournalGenomics and Informatics
Volume18
Issue number1
DOIs
StatePublished - Mar 2020

Keywords

  • Analysis pipeline
  • Cloud computing
  • Genomic data
  • Web server
  • Workflow system

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