Suppr超能文献

通过标准化的数据和元数据收集来推进微生物组研究:介绍微生物组研究数据工具包。

Advancing microbiome research through standardized data and metadata collection: introducing the Microbiome Research Data Toolkit.

机构信息

Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, University of Cape Town, Rondebosch, Cape Town 7701, South Africa.

Department of Biology, Chemistry and Physics, Faculty of Health, Natural Resources and Applied Sciences, Namibia University of Science and Technology, Private Bag 13388, 13 Jackson Kaujeua Street, Windhoek, Namibia.

出版信息

Database (Oxford). 2024 Aug 21;2024. doi: 10.1093/database/baae062.

Abstract

Microbiome research has made significant gains with the evolution of sequencing technologies. Ensuring comparability between studies and enhancing the findability, accessibility, interoperability and reproducibility of microbiome data are crucial for maximizing the value of this growing body of research. Addressing the challenges of standardized metadata reporting, collection and curation, the Microbiome Working Group of the Human Hereditary and Health in Africa (H3Africa) consortium aimed to develop a comprehensive solution. In this paper, we present the Microbiome Research Data Toolkit, a versatile tool designed to standardize microbiome research metadata, facilitate MIxS-MIMS and PhenX reporting, standardize prospective collection of participant biological and lifestyle data, and retrospectively harmonize such data. This toolkit enables past, present and future microbiome research endeavors to collaborate effectively, fostering novel collaborations and accelerating knowledge discovery in the field. Database URL: https://doi.org/10.25375/uct.24218999.v2.

摘要

随着测序技术的发展,微生物组研究取得了重大进展。确保研究之间的可比性,并提高微生物组数据的可发现性、可访问性、互操作性和可重复性,对于最大限度地发挥这一不断增长的研究领域的价值至关重要。为了解决标准化元数据报告、收集和管理方面的挑战,人类遗传和健康在非洲(H3Africa)联盟的微生物组工作组旨在开发一个全面的解决方案。在本文中,我们介绍了微生物组研究数据工具包,这是一个通用工具,旨在标准化微生物组研究元数据,促进 MIxS-MIMS 和 PhenX 报告,标准化参与者生物和生活方式数据的前瞻性收集,并回顾性地协调此类数据。该工具包使过去、现在和未来的微生物组研究工作能够有效地合作,促进新的合作,并加速该领域的知识发现。数据库网址:https://doi.org/10.25375/uct.24218999.v2.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e71/11338178/5d9e27d1eff3/baae062f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验