AbstractThe volume of public proteomics data is rapidly increasing, causing a computational challenge for large-scale reanalysis. Here, we introduce quantms (https://quant,ms.org/), an open-source cloud-based pipeline for massively parallel proteomics data analysis. We used quantms to reanalyze 83 public ProteomeXchange datasets, comprising 29,354 instrument files from 13,132 human samples, to quantify 16,599 proteins based on 1.03 million unique peptides.

摘要公共蛋白质组学数据量迅速增加，对大规模再分析造成了计算挑战。在这里，我们介绍quantms(https://quant。我们使用quantms重新分析了83个公共蛋白质组交换数据集，其中包括来自13132个人类样本的29354个仪器文件，以基于103万个独特肽量化16599个蛋白质。

quantms is based on standard file formats improving the reproducibility, submission and dissemination of the data to ProteomeXchange..

quantms基于标准文件格式，提高了数据的可重复性，提交和传播到ProteomeXchange。。

MainIn recent years, the field of proteomics has seen unprecedented growth in publicly available datasets, with a trend toward studies that analyze a more substantial number of samples. As of December 2023, the number of public datasets stored in the PRIDE database1 exceeded 25,000, including a remarkable increase in large datasets containing more than 100 instrument files, from 100 in 2014 to 4,435 submissions in 2024.

Main近年来，蛋白质组学领域在公开可用的数据集方面取得了前所未有的增长，并且有研究分析更多样本的趋势。截至2023年12月，PRIDE数据库1中存储的公共数据集数量超过25000个，其中包含100多个仪器文件的大型数据集显着增加，从2014年的100个增加到2024年的4435个提交。

In parallel, a range of transformative improvements in proteomic data processing software has enabled a deeper and more precise look into the proteome. Reprocessing old data with such new tools, therefore, yields additional biological and biomedical insights2,3. However, the increased size of individual datasets presents a significant computational bottleneck, making it challenging to reanalyze large experiments on conventional workstations.

与此同时，蛋白质组学数据处理软件的一系列变革性改进使人们能够对蛋白质组进行更深入和更精确的研究。因此，用这种新工具重新处理旧数据会产生额外的生物学和生物医学见解2,3。然而，单个数据集规模的增加带来了一个重大的计算瓶颈，使得在传统工作站上重新分析大型实验具有挑战性。

The automated analysis of publicly accessible quantitative proteomics data is further limited by the lack of metadata that characterizes the phenotypes, the samples and the instrument operation. Although some of these challenges are tackled in earlier studies4,5,6, many research groups still cannot perform automated large-scale quantitative analysis in the cloud and on distributed architectures.

由于缺乏表征表型，样品和仪器操作的元数据，公众可获得的定量蛋白质组学数据的自动分析进一步受到限制。尽管其中一些挑战在早期的研究中得到了解决[4,5,6]，但许多研究小组仍然无法在云和分布式体系结构中进行自动化的大规模定量分析。

To address this challenge, the field requires scalable bioinformatics solutions that leverage sample metadata to automatically quantify peptides and proteins, perform absolute or differential-expression analysis and provide extensive quality control output.Here we introduce quantms (https://quantms.org), an open-source cloud-based pipeline for massively parallel proteomic data reanalysis.

为了应对这一挑战，该领域需要可扩展的生物信息学解决方案，该解决方案利用样本元数据自动量化肽和蛋白质，执行绝对或差异表达分析，并提供广泛的质量控制输出。这里我们介绍quantms(https://quantms.org)，一个基于云的开源管道，用于大规模并行蛋白质组数据重新分析。

It supports three major types of experiment—data-dependent acquisition label-free (DDA-LFQ), isobaric tandem mass tag (TMT)-based (DDA-plex.

它支持三种主要类型的实验数据相关采集无标签（DDA-LFQ），基于等压串联质量标签（TMT）的（DDA-plex）。

Data availability

数据可用性

The datasets reanalyzed in the present study can be searched on the quantms web page (https://quantms.org/datasets). In addition, all the results can be found in the PRIDE database FTP (http://ftp.pride.ebi.ac.uk/pub/databases/pride/resources/proteomes/). Source data are provided with this paper.

本研究中重新分析的数据集可以在quantms网页上搜索(https://quantms.org/datasets)。此外，所有结果都可以在PRIDE数据库FTP中找到(http://ftp.pride.ebi.ac.uk/pub/databases/pride/resources/proteomes/)。本文提供了源数据。

Code availability

代码可用性

All software, algorithms and tools are available on GitHub: quantms at https://github.com/bigbio/quantms and pmultiqc at https://github.com/bigbio/pmultiqc. The full documentation of quantms is available at https://quantms.readthedocs.io/en/latest/.

所有软件、算法和工具都可以在GitHub上找到：quantmshttps://github.com/bigbio/quantms和pmultiqchttps://github.com/bigbio/pmultiqc.https://quantms.readthedocs.io/en/latest/.

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Download referencesAcknowledgementsY.P.-R. was funded by the EU H2020 project EPIC-XS (grant no. 823839), Wellcome grants (nos 208391/Z/17/Z, 223745/Z/21/Z) and EMBL core funding. M.B. and C.D. were funded by the National Key Research and Development Program of China (grant no. 2018YFA0507504).

下载referencesAcknowledgementsY。P、 -R.由欧盟H2020项目EPIC-XS（拨款号823839），惠康拨款（编号208391/Z/17/Z，223745/Z/21/Z）和EMBL核心资金资助。M、 B.和C.D.由中国国家重点研究发展计划（批准号2018YFA0507504）资助。

V.D. was supported by the Federal Ministry of Education and Research (BMBF), as part of the National Research Initiatives for Mass Spectrometry in Systems Medicine (’MSCoreSy’), under grant agreement no. 161L0221.FundingOpen access funding provided by European Molecular Biology Laboratory (EMBL).Author informationAuthor notesThese authors contributed equally: Chengxin Dai, Julianus Pfeuffer.Authors and AffiliationsChongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, ChinaChengxin Dai, Hong Wang, Ping Zheng & Mingze BaiState Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, ChinaChengxin Dai & Mingze BaiAlgorithmic Bioinformatics, Freie Universität Berlin, Berlin, GermanyJulianus PfeufferScience for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, SwedenLukas KällDepartment of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, GermanyTimo Sachsenberg & Oliver KohlbacherInstitute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, GermanyTimo Sachsenberg & Oliver KohlbacherCharité—Universitätsmedizin Berlin, Berlin, GermanyVadim DemichevInstitute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, GermanyOliver KohlbacherEuropean Molecular.

五、 D.得到了联邦教育和研究部（BMBF）的支持，作为国家系统医学质谱研究计划（“MSCoreSy”）的一部分，根据第161L0221号赠款协议。资助开放获取资金由欧洲分子生物学实验室（EMBL）提供。作者信息作者注意到这些作者做出了同样的贡献：戴承新，朱利安娜普费弗。作者和单位重庆邮电大学大数据生物智能重点实验室重庆市戴成新、王红、郑平和明泽百州蛋白质组学重点实验室，北京蛋白质组研究中心，国家蛋白质科学中心（北京），北京生命组学研究所，北京，中国成新戴和明泽百州生物信息学，柏林弗雷大学，德国朱利安斯·普弗斯生命科学实验室，化学、生物技术与健康工程科学学院，瑞典皇家理工学院，斯德哥尔摩蒂宾根大学生物信息学和医学信息学研究所，蒂宾根，蒂宾根，德国蒂莫·萨赫森堡和奥利弗·科尔巴赫·哈里特大学，柏林，德国图宾根大学医院德国瓦迪姆·德米切文转化生物信息学研究所，图宾根，GermanyOliver KohlbacherEuropean Molecular。

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PubMed Google ScholarContributionsC.D., J.P. and Y.P.-R. developed the quantms workflow. H.W., C.D., J.P. and Y.P.-R. developed the pmultiqc library and web application, P.Z., H.W., C.D. and Y.P.-R. developed the quantms.org web page to present the results of the quantitative analysis.

PubMed谷歌学术贡献中心。D、 ，J.P.和Y.P.-R.开发了quantms工作流程。H、 W.，C.D.，J.P.和Y.P.-R.开发了pmultiqc库和web应用程序，P.Z.，H.W.，C.D.和Y.P.-R.开发了quantms.org网页来展示定量分析的结果。

T.S. and J.P. developed the algorithms and tools in OpenMS for DDA-plex and LFQ-DDA workflow. V.D. contributed to the development of the algorithm parallelization of the DIA-NN tool and the LFQ-DIA workflow. C.D., J.P. and Y.P.-R. performed the annotations of the datasets and the data analysis. L.K.

T、 S.和J.P.在OpenMS中为DDA plex和LFQ-DDA工作流开发了算法和工具。五、 D.为DIA-NN工具和LFQ-DIA工作流程的算法并行化开发做出了贡献。C、 D.，J.P.和Y.P.-R.对数据集进行了注释和数据分析。五十、 K。

designed and developed the stand-in decoy. C.D., J.P., T.S., V.D., M.B., O.K. and Y.P.-R. wrote the paper and contributed to the design of the workflow and quantms.org project.Corresponding authorCorrespondence to.

设计并开发了替身诱饵。C、 D.，J.P.，T.S.，V.D.，M.B.，O.K.和Y.P.-R.撰写了这篇论文，并为工作流程和quantms.org项目的设计做出了贡献。对应作者对应。

Yasset Perez-Riverol.Ethics declarations

Yasset Perez Riverol。道德宣言

Competing interests

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The authors declare no competing interests.

作者声明没有利益冲突。

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Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Allison Doerr, in collaboration with the Nature Methods team.

Nature Methods感谢匿名审稿人对这项工作的同行评审做出的贡献。同行评审报告可用。主要处理编辑：Allison Doerr，与Nature Methods团队合作。

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Reprints and permissionsAbout this articleCite this articleDai, C., Pfeuffer, J., Wang, H. et al. quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data.

转载和许可本文引用本文Dai，C.，Pfeuffer，J.，Wang，H。等人。quantms：基于云的定量蛋白质组学管道可以重新分析公共蛋白质组学数据。

Nat Methods (2024). https://doi.org/10.1038/s41592-024-02343-1Download citationReceived: 12 May 2023Accepted: 03 June 2024Published: 04 July 2024DOI: https://doi.org/10.1038/s41592-024-02343-1Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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