Journal: Journal of Proteome Research

Abbreviation

J. Proteome Res.

Publisher

American Chemical Society

Journal Volumes

ISSN

1535-3893
1535-3907

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Publications1 - 10 of 103
  • MultiOmicsAgent: Guided Extreme Gradient-Boosted Decision Trees-Based Approaches for Biomarker-Candidate Discovery in Multiomics Data
    Item type: Journal Article
    Settelmeier, Jens; Goetze, Sandra; Boshart, Julia; et al. (2025)
    Journal of Proteome Research
    MultiOmicsAgent (MOAgent) is an innovative, Python-based open-source tool for biomarker discovery, utilizing machine learning techniques, specifically extreme gradient-boosted decision trees, to process multiomics data. With its cross-platform compatibility, user-oriented graphical interface, and well-documented API, MOAgent not only meets the needs of both coding professionals and those new to machine learning but also addresses common data analysis challenges like normalization, data incompleteness, class imbalances and data leakage between disjoint data splits. MOAgent '' s guided data analysis strategy opens up data-driven insights from digitized clinical biospecimen cohorts, making advanced data analysis accessible and reliable for a wide audience.
  • Research on the Human Proteome Reaches a Major Milestone: >90% of Predicted Human Proteins Now Credibly Detected, According to the HUPO Human Proteome Project
    Item type: Journal Article
    Omenn, Gilbert S.; Lane, Lydie; Overall, Christopher M.; et al. (2020)
    Journal of Proteome Research
    According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020–01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the “missing proteins” (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019–01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020–01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases.
  • Proteomic Analyses Using Grifola frondosa Metalloendoprotease Lys-N
    Item type: Journal Article
    Hohmann, Laura; Sherwood, Carly; Eastham, Ashley; et al. (2009)
    Journal of Proteome Research
  • Biology/Disease-Driven Initiative on Protein-Aggregation Diseases of the Human Proteome Project: Goals and Progress to Date
    Item type: Review Article
    Boersema, Paul J.; Melnik, Andre; Hazenberg, Bouke P.C.; et al. (2018)
    Journal of Proteome Research
  • The 2024 Report on the Human Proteome from the HUPO Human Proteome Project
    Item type: Review Article
    Omenn, Gilbert S.; Orchard, Sandra; Lane, Lydie; et al. (2024)
    Journal of Proteome Research
    The Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify at least one isoform of every protein-coding gene and (2) to make proteomics an integral part of multiomics studies of human health and disease. The past year has seen major transitions for the HPP. neXtProt was retired as the official HPP knowledge base, UniProtKB became the reference proteome knowledge base, and Ensembl-GENCODE provides the reference protein target list. A function evidence FE1-5 scoring system has been developed for functional annotation of proteins, parallel to the PE1-5 UniProtKB/neXtProt scheme for evidence of protein expression. This report includes updates from neXtProt (version 2023-09) and UniProtKB release 2024_04, with protein expression detected (PE1) for 18138 of the 19411 GENCODE protein-coding genes (93%). The number of non-PE1 proteins ("missing proteins") is now 1273. The transition to GENCODE is a net reduction of 367 proteins (19,411 PE1-5 instead of 19,778 PE1-4 last year in neXtProt). We include reports from the Biology and Disease-driven HPP, the Human Protein Atlas, and the HPP Grand Challenge Project. We expect the new Functional Evidence FE1-5 scheme to energize the Grand Challenge Project for functional annotation of human proteins throughout the global proteomics community, including pi-HuB in China.
  • Proteomics of Pyrococcus furiosus (Pfu)
    Item type: Journal Article
    Wong, Catherine C.L.; Cociorva, Daniel; Miller, Christine A.; et al. (2013)
    Journal of Proteome Research
  • Identification of Trans-Golgi Network Proteins inArabidopsis thalianaRoot Tissue
    Item type: Journal Article
    Groen, Arnoud J.; Sancho Andrés, Gloria; Breckels, Lisa M.; et al. (2014)
    Journal of Proteome Research
    Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady-state location favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.
  • Quantification of ErbB Network Proteins in Three Cell Types Using Complementary Approaches Identifies Cell-General and Cell-Type-Specific Signaling Proteins
    Item type: Journal Article
    Kiel, Christina; Ebhardt, H. Alexander; Burnier, Julia; et al. (2014)
    Journal of Proteome Research
  • SAINT-MS1
    Item type: Journal Article
    Choi, Hyungwon; Glatter, Timo; Gstaiger, Mathias; et al. (2012)
    Journal of Proteome Research
  • Protein Expression Changes in Ovarian Cancer during the Transition from Benign to Malignant
    Item type: Journal Article
    Waldemarson, Sofia; Krogh, Morten; Alaiya, Ayodele; et al. (2012)
    Journal of Proteome Research
Publications1 - 10 of 103