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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

Research output: Contribution to journalArticlepeer-review

6 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

Bibliographical note

Publisher Copyright:
© 2020, Korea Genome Organization.

Keywords

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

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