Progress Identifying and Analyzing the Human Proteome: 2021 Metrics from the HUPO Human Proteome Project

Omenn, Gilbert S.; Lane, Lydie; Overall, Christopher M.; Paik, Young Ki; Cristea, Ileana M.; Corrales, Fernando J.; Lindskog, Cecilia; Weintraub, Susan T.; Roehrl, Michael H. A.; Liu, Siqi; Bandeira, Nuno; Srivastava, Sudhir; Chen, Yu‐Ju; Aebersold, Ruedi; Moritz, Robert L.; Deutsch, Eric W.

doi:10.1021/acs.jproteome.1c00590

Cited by 35 publications

(30 citation statements)

References 109 publications

(178 reference statements)

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“…In this Review, we provide a perspective on efforts emerging in the community as a contribution from the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), one of the key initiatives of the HUPO Human Proteome Project (HPP). − We begin by reviewing recent technology advancements in plasma proteomics, especially in the context of affinity-based platforms and MS workflows. We then present the latest results from the compendium of reanalyzed MS data sets in the PeptideAtlas resource, which has been split into plasma samples and blood-derived extracellular vesicle (EV) samples.…”

Section: Introductionmentioning

confidence: 99%

Advances and Utility of the Human Plasma Proteome

et al. 2021

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The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.

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Section: Introductionmentioning

confidence: 99%

Advances and Utility of the Human Plasma Proteome

et al. 2021

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“…One of the goals of the Human Proteome Project [60][61][62] is to secure confident detections via mass spectrometry of all predicted human proteins and has relied on PeptideAtlas and TPP to provide this compendium since its inception in 2012. In order to ensure the highest quality and minimal false positives, a set of guidelines, known as the HPP Mass Spectrometry Data Interpretation Guidelines 3.0 63 were developed to guide the publication of new detections as well as the PeptideAtlas and TPP process for reanalysis of datasets.…”

Section: Namementioning

confidence: 99%

The Trans-Proteomic Pipeline: Robust Mass Spectrometry-based Proteomics Data Analysis Suite

Deutsch

Mendoza

Shteynberg

et al. 2022

Preprint

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The Trans-Proteomic Pipeline mass spectrometry data analysis suite has been in continual development and refinement since its first tools PeptideProphet and ProteinProphet were published twenty years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from datasets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.

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“…For example, even through tremendous effort focused on the human proteome, the fraction of unobserved proteins has been pushed just below 10%. 26,27 It remains unclear why certain proteins remain undetected, though machine learning has been applied to explore which properties most strongly influence detectability (as reviewed within). 28 One can compute a set of properties for a proteome and then train a model using those properties based on real world observations of the proteins that are detected and the proteins that aren't detected.…”

Section: Data Typesmentioning

confidence: 99%

ProteomicsML: An Online Platform for Community-Curated Datasets and Tutorials for Machine Learning in Proteomics

Rehfeldt

Gabriels

Bouwmeester

et al. 2022

Preprint

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Dataset acquisition and curation are often the hardest and most time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based LC-IM-MS datasets, due to the high-throughput data structure with high levels of noise and complexity between raw and machine learning-ready formats. While predictive proteomics is a field on the rise, when predicting peptide behavior in LC-IM-MS setups, each lab often uses unique and complex data processing pipelines in order to maximize performance, at the cost of accessibility and reproducibility. For this reason we introduce ProteomicsML, an online resource for proteomics-based datasets and tutorials across most of the currently explored physicochemical peptide properties. This community-driven resource makes it simple to access data in easy-to-process formats, and contains easy-to-follow tutorials that allow new users to interact with even the most advanced algorithms in the field. ProteomicsML provides datasets that are useful for comparing state-of-the-art (SOTA) machine learning algorithms, as well as providing introductory material for teachers and newcomers to the field alike. The platform is freely available on https://www.proteomicsml.org/ and we welcome the entire proteomics community to contribute to the project at https://github.com/proteomicsml/.

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Progress Identifying and Analyzing the Human Proteome: 2021 Metrics from the HUPO Human Proteome Project

Cited by 35 publications

References 109 publications

Advances and Utility of the Human Plasma Proteome

Advances and Utility of the Human Plasma Proteome

The Trans-Proteomic Pipeline: Robust Mass Spectrometry-based Proteomics Data Analysis Suite

ProteomicsML: An Online Platform for Community-Curated Datasets and Tutorials for Machine Learning in Proteomics

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