University of Minnesota MinneapolisMinnesota55455 USA.
Institute for Systems Biology SeattleWashington98109 USA.
J Biomol Tech. 2023 Aug 7;34(3). doi: 10.7171/3fc1f5fe.a058bad4. eCollection 2023 Sep 30.
Metaproteomics research using mass spectrometry data has emerged as a powerful strategy to understand the mechanisms underlying microbiome dynamics and the interaction of microbiomes with their immediate environment. Recent advances in sample preparation, data acquisition, and bioinformatics workflows have greatly contributed to progress in this field. In 2020, the Association of Biomolecular Research Facilities Proteome Informatics Research Group launched a collaborative study to assess the bioinformatics options available for metaproteomics research. The study was conducted in 2 phases. In the first phase, participants were provided with mass spectrometry data files and were asked to identify the taxonomic composition and relative taxa abundances in the samples without supplying any protein sequence databases. The most challenging question asked of the participants was to postulate the nature of any biological phenomena that may have taken place in the samples, such as interactions among taxonomic species. In the second phase, participants were provided a protein sequence database composed of the species present in the sample and were asked to answer the same set of questions as for phase 1. In this report, we summarize the data processing methods and tools used by participants, including database searching and software tools used for taxonomic and functional analysis. This study provides insights into the status of metaproteomics bioinformatics in participating laboratories and core facilities.
基于质谱数据的宏蛋白质组学研究已成为理解微生物组动态及其与周围环境相互作用机制的有力策略。近年来,样品制备、数据采集和生物信息学工作流程的进步极大地推动了该领域的发展。2020 年,生物分子研究设施协会蛋白质组信息学研究组开展了一项合作研究,以评估用于宏蛋白质组学研究的生物信息学选择。该研究分两个阶段进行。在第一阶段,参与者提供了质谱数据文件,并被要求在不提供任何蛋白质序列数据库的情况下识别样品中的分类组成和相对丰度。向参与者提出的最具挑战性的问题是推测可能在样品中发生的任何生物现象的性质,例如分类物种之间的相互作用。在第二阶段,参与者提供了由样品中存在的物种组成的蛋白质序列数据库,并被要求回答与第一阶段相同的一组问题。在本报告中,我们总结了参与者使用的数据处理方法和工具,包括用于分类和功能分析的数据库搜索和软件工具。这项研究深入了解了参与实验室和核心设施中宏蛋白质组学生物信息学的现状。