{"title":"Data-independent acquisition in metaproteomics.","authors":"Enhui Wu, Guanyang Xu, Dong Xie, Liang Qiao","doi":"10.1080/14789450.2024.2394190","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Metaproteomics offers insights into the function of complex microbial communities, while it is also capable of revealing microbe-microbe and host-microbe interactions. Data-independent acquisition (DIA) mass spectrometry is an emerging technology, which holds great potential to achieve deep and accurate metaproteomics with higher reproducibility yet still facing a series of challenges due to the inherent complexity of metaproteomics and DIA data.</p><p><strong>Areas covered: </strong>This review offers an overview of the DIA metaproteomics approaches, covering aspects such as database construction, search strategy, and data analysis tools. Several cases of current DIA metaproteomics studies are presented to illustrate the procedures. Important ongoing challenges are also highlighted. Future perspectives of DIA methods for metaproteomics analysis are further discussed. Cited references are searched through and collected from Google Scholar and PubMed.</p><p><strong>Expert opinion: </strong>Considering the inherent complexity of DIA metaproteomics data, data analysis strategies specifically designed for interpretation are imperative. From this point of view, we anticipate that deep learning methods and de novo sequencing methods will become more prevalent in the future, potentially improving protein coverage in metaproteomics. Moreover, the advancement of metaproteomics also depends on the development of sample preparation methods, data analysis strategies, etc. These factors are key to unlocking the full potential of metaproteomics.</p>","PeriodicalId":50463,"journal":{"name":"Expert Review of Proteomics","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Review of Proteomics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1080/14789450.2024.2394190","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/21 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Metaproteomics offers insights into the function of complex microbial communities, while it is also capable of revealing microbe-microbe and host-microbe interactions. Data-independent acquisition (DIA) mass spectrometry is an emerging technology, which holds great potential to achieve deep and accurate metaproteomics with higher reproducibility yet still facing a series of challenges due to the inherent complexity of metaproteomics and DIA data.
Areas covered: This review offers an overview of the DIA metaproteomics approaches, covering aspects such as database construction, search strategy, and data analysis tools. Several cases of current DIA metaproteomics studies are presented to illustrate the procedures. Important ongoing challenges are also highlighted. Future perspectives of DIA methods for metaproteomics analysis are further discussed. Cited references are searched through and collected from Google Scholar and PubMed.
Expert opinion: Considering the inherent complexity of DIA metaproteomics data, data analysis strategies specifically designed for interpretation are imperative. From this point of view, we anticipate that deep learning methods and de novo sequencing methods will become more prevalent in the future, potentially improving protein coverage in metaproteomics. Moreover, the advancement of metaproteomics also depends on the development of sample preparation methods, data analysis strategies, etc. These factors are key to unlocking the full potential of metaproteomics.
引言元蛋白质组学有助于深入了解复杂微生物群落的功能,同时还能揭示微生物与微生物、宿主与微生物之间的相互作用。数据独立获取(DIA)质谱技术是一项新兴技术,它在实现深度、准确、可重复性更高的元蛋白质组学方面具有巨大潜力,但由于元蛋白质组学和 DIA 数据固有的复杂性,它仍面临着一系列挑战:本综述概述了 DIA 元蛋白质组学方法,涉及数据库建设、搜索策略和数据分析工具等方面。文章介绍了当前几个 DIA 元蛋白质组学研究案例,以说明相关程序。同时还强调了当前面临的重要挑战。还进一步讨论了用于元蛋白质组学分析的 DIA 方法的未来前景。引用的参考文献是从谷歌学术和PubMed上搜索和收集的:考虑到 DIA 元蛋白质组学数据固有的复杂性,专门设计用于解读的数据分析策略势在必行。从这个角度来看,我们预计深度学习方法和从头测序方法在未来会越来越普遍,从而有可能提高元蛋白质组学的蛋白质覆盖率。此外,元蛋白质组学的发展还取决于样品制备方法、数据分析策略等的发展。这些因素是充分释放元蛋白质组学潜力的关键。
期刊介绍:
Expert Review of Proteomics (ISSN 1478-9450) seeks to collect together technologies, methods and discoveries from the field of proteomics to advance scientific understanding of the many varied roles protein expression plays in human health and disease.
The journal coverage includes, but is not limited to, overviews of specific technological advances in the development of protein arrays, interaction maps, data archives and biological assays, performance of new technologies and prospects for future drug discovery.
The journal adopts the unique Expert Review article format, offering a complete overview of current thinking in a key technology area, research or clinical practice, augmented by the following sections:
Expert Opinion - a personal view on the most effective or promising strategies and a clear perspective of future prospects within a realistic timescale
Article highlights - an executive summary cutting to the author''s most critical points.