GEO uploader: simplifying the data deposition in the GEO repository.

IF 3.3 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

BMC Bioinformatics Pub Date : 2026-05-05 DOI:10.1186/s12859-026-06466-4

Ronald Domi, Falko Noé, Peter Leary, Hubert Rehrauer

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引用次数: 0

Abstract

Background: The Gene Expression Omnibus (GEO) (Clough and Barrett in: methods in molecular biology, Clifton, 2016) repository requires complex multistep submissions involving metadata preparation, FTP uploads, and MD5 validation. Current manual processes are error-prone, time-consuming, and require significant bioinformatics expertise, creating barriers for many researchers.

Results: We present GEO Uploader, a web-based tool that automates the entire GEO submission workflow through an intuitive interface. The application reduces the submission initiation time from 2-3 h to under 20 s by automating file uploads, MD5 calculations, and metadata template population. Key features include parallel processing of uploads and checksum calculations, automated error prevention through template-based metadata completion, real-time progress tracking, and support for complex submission structures. Deployment across 30 + users with 50 + upload sessions, including datasets exceeding hundreds of gigabytes, demonstrates practical utility and reliability in research environments.

Conclusion: GEO Uploader significantly reduces the technical barrier for GEO submissions while minimizing errors through comprehensive automation. The tool supports data sharing by enabling researchers without specialized bioinformatics expertise to complete submissions independently. Available as open-source software with multiuser deployment capabilities, GEO Uploader represents a substantial improvement in research data sharing accessibility and supports broader adoption of open science practices in the genomics community.

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GEO上传器：简化GEO存储库中的数据存储。

背景：基因表达Omnibus （Gene Expression Omnibus, Clough and Barrett in: methods in molecular biology, Clifton, 2016）存储库需要复杂的多步骤提交，包括元数据准备、FTP上传和MD5验证。目前的手工过程容易出错，耗时，并且需要大量的生物信息学专业知识，为许多研究人员创造了障碍。结果：我们提出GEO Uploader，一个基于web的工具，通过一个直观的界面自动化整个GEO提交工作流程。该应用程序通过自动化文件上传、MD5计算和元数据模板填充，将提交启动时间从2-3小时减少到20秒以下。主要特性包括并行处理上传和校验和计算、通过基于模板的元数据完成自动预防错误、实时进度跟踪以及对复杂提交结构的支持。部署在30多个用户和50多个上传会话中，包括超过数百gb的数据集，在研究环境中展示了实用性和可靠性。结论：GEO Uploader通过全面的自动化大大降低了GEO提交的技术障碍，同时最大限度地减少了错误。该工具支持数据共享，使没有专门生物信息学专业知识的研究人员能够独立完成提交。作为具有多用户部署能力的开源软件，GEO Uploader代表了研究数据共享可访问性的实质性改进，并支持基因组学社区更广泛地采用开放科学实践。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Bioinformatics 生物-生化研究方法

CiteScore

5.70

自引率

3.30%

发文量

506

审稿时长

4.3 months

期刊介绍： BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.