ArticleBMC bioinformatics2018
Closha: bioinformatics workflow system for the analysis of massive sequencing data.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A scoping review of approaches to evaluating workflow management systems for bioinformatics users.Briefings in bioinformatics · 2026Article
- KoNA: Korean Nucleotide Archive as A New Data Repository for Nucleotide Sequence Data.Genomics, proteomics & bioinformatics · 2024Article
- The NIH Comparative Genomics Resource: addressing the promises and challenges of comparative genomics on human health.BMC genomics · 2023Review
- Introduction of the Korea BioData Station (K-BDS) for sharing biological data.Genomics & informatics · 2023Article
- Bioinformatics in Plant Breeding and Research on Disease Resistance.Plants (Basel, Switzerland) · 2022Review
- GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure.BMC bioinformatics · 2022Article
- Evaluation of serverless computing for scalable execution of a joint variant calling workflow.PloS one · 2021Article
- Article
- SNAPS: Sensor Analytics Point Solutions for Detection and Decision Support Systems.Sensors (Basel, Switzerland) · 2019Review
- Bioinformatics Workflows With NoSQL Database in Cloud Computing.Evolutionary bioinformatics online · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhile next-generation sequencing (NGS) costs have fallen in recent years, the cost and complexity of computation remain substantial obstacles to the use of NGS in bio-medical care and genomic research. The rapidly increasing amounts of data available from the new high-throughput methods have made data processing infeasible without automated pipelines. The integration of data and analytic resources into workflow systems provides a solution to the problem by simplifying the task of data analysis.
resultsTo address this challenge, we developed a cloud-based workflow management system, Closha, to provide fast and cost-effective analysis of massive genomic data. We implemented complex workflows making optimal use of high-performance computing clusters. Closha allows users to create multi-step analyses using drag and drop functionality and to modify the parameters of pipeline tools. Users can also import the Galaxy pipelines into Closha. Closha is a hybrid system that enables users to use both analysis programs providing traditional tools and MapReduce-based big data analysis programs simultaneously in a single pipeline. Thus, the execution of analytics algorithms can be parallelized, speeding up the whole process. We also developed a high-speed data transmission solution, KoDS, to transmit a large amount of data at a fast rate. KoDS has a file transfer speed of up to 10 times that of normal FTP and HTTP. The computer hardware for Closha is 660 CPU cores and 800 TB of disk storage, enabling 500 jobs to run at the same time.
conclusionsClosha is a scalable, cost-effective, and publicly available web service for large-scale genomic data analysis. Closha supports the reliable and highly scalable execution of sequencing analysis workflows in a fully automated manner. Closha provides a user-friendly interface to all genomic scientists to try to derive accurate results from NGS platform data. The Closha cloud server is freely available for use from http://closha.kobic.re.kr/ .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.