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--- |
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license: mit |
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pretty_name: >- |
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hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 |
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proteomic experiments. |
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repo: https://github.com/KDrewLab/huMAP3.0_analysis |
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--- |
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# hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments. |
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Proteins interact with each other and organize themselves into macromolecular machines (ie. complexes) |
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to carry out essential functions of the cell. We have a good understanding of a few complexes such as |
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the proteasome and the ribosome but currently we have an incomplete view of all protein complexes as |
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well as their functions. The hu.MAP attempts to address this lack of understanding by integrating several |
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large scale protein interaction datasets to obtain the most comprehensive view of protein complexes. |
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In hu.MAP 3.0 we integrated large scale affinity purification mass spectrometry (AP/MS) datasets from Bioplex, |
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Bioplex2.0, Bioplex3.0, Boldt et al. and Hein et al., large scale biochemical fractionation data (Wan et al.), |
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proximity labeling data (Gupta et al., Youn et al.), and RNA hairpin pulldown data (Treiber et al.) to produce |
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a complex map with over 15k complexes. |
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## Funding |
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NIH R00, NSF/BBSRC |
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## Citation |
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Samantha N. Fischer, Erin R Claussen, Savvas Kourtis, Sara Sdelci, Sandra Orchard, Henning Hermjakob, Georg Kustatscher, Kevin Drew hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments BioRxiv https://doi.org/10.1101/2024.10.11.617930 |
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## References |
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Kevin Drew, John B. Wallingford, Edward M. Marcotte hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies Mol Syst Biol (2021)17:e10016https://doi.org/10.15252/msb.202010016 |
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Kevin Drew, Chanjae Lee, Ryan L Huizar, Fan Tu, Blake Borgeson, Claire D McWhite, Yun Ma, John B Wallingford, Edward M Marcotte Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes. Molecular Systems Biology (2017) 13, 932. DOI 10.15252/msb.20167490 |
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Huttlin et al. Dual proteome-scale networks reveal cell-specific remodeling of the human interactome Cell. 2021 May 27;184(11):3022-3040.e28. doi: 10.1016/j.cell.2021.04.011. |
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Huttlin et al. Architecture of the human interactome defines protein communities and disease networks. Nature. 2017 May 25;545(7655):505-509. DOI: 10.1038/nature22366. |
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Treiber et al. A Compendium of RNA-Binding Proteins that Regulate MicroRNA Biogenesis.. Mol Cell. 2017 Apr 20;66(2):270-284.e13. doi: 10.1016/j.molcel.2017.03.014. |
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Boldt et al. An organelle-specific protein landscape identifies novel diseases and molecular mechanisms. Nat Commun. 2016 May 13;7:11491. doi: 10.1038/ncomms11491. |
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Youn et al. High-Density Proximity Mapping Reveals the Subcellular Organization of mRNA-Associated Granules and Bodies. Mol Cell. 2018 Feb 1;69(3):517-532.e11. doi: 10.1016/j.molcel.2017.12.020. |
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Gupta et al. A Dynamic Protein Interaction Landscape of the Human Centrosome-Cilium Interface. Cell. 2015 Dec 3;163(6):1484-99. doi: 10.1016/j.cell.2015.10.065. |
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Wan, Borgeson et al. Panorama of ancient metazoan macromolecular complexes. Nature. 2015 Sep 17;525(7569):339-44. doi: 10.1038/nature14877. Epub 2015 Sep 7. |
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Hein et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell. 2015 Oct 22;163(3):712-23. doi: 10.1016/j.cell.2015.09.053. Epub 2015 Oct 22. |
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Huttlin et al. The BioPlex Network: A Systematic Exploration of the Human Interactome. Cell. 2015 Jul 16;162(2):425-40. doi: 10.1016/j.cell.2015.06.043. |
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Reimand et al. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 2016 Jul 8;44(W1):W83-9. doi: 10.1093/nar/gkw199. |
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## Associated code |
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Code examples using the hu.MAP 3.0 model and downstream analysis can be found on our |
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[GitHub](https://github.com/KDrewLab/huMAP3.0_analysis) |
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All feature matrices and associated files can be found in the [sfisch/hu.MAP3.0](https://huggingface.co/datasets/sfisch/hu.MAP3.0) datasets |
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repo |
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# Usage |
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## Accessing the model |
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hu.MAP 3.0 was built using the auto-ML tool [AutoGluon](https://auto.gluon.ai/stable/index.html) and the [TabularPredictor](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html) |
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module is used train, test, and make predictions with the model. |
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This can be downloaded using the following: |
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$ pip install autogluon==0.4.0 |
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Then it can be imported as: |
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>>> from autogluon.tabular import TabularPredictor |
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Note that to perform operations with our model the **0.4.0 version** must be used |
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Our trained model can be downloaded through Huggingface using [huggingface_hub](https://huggingface.co/docs/hub/index) |
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>>> from huggingface_hub import snapshot_download |
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>>> model_dir = snapshot_download(repo_id="sfisch/hu.MAP3.0_AutoGluon") |
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>>> predictor = TabularPredictor.load(f"{model_dir}/huMAP3_20230503_complexportal_subset10kNEG_notScaled_accuracy") |
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To use the model and make predictions, we show two full code examples using the [full feature matrix](https://github.com/KDrewLab/huMAP3.0_analysis/blob/main/huMAP3.0_model_devel/generating_predictions_w_hu.MAP3.0.ipynb) |
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and the [test feature matrix](https://github.com/KDrewLab/huMAP3.0_analysis/blob/main/huMAP3.0_model_devel/humap3_test_20230503.pairsWprob) in jupyter notebooks. |
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All feature matrices can be pulled using the 'datasets' module from HuggingFace and examples of that are seen on our [GitHub](https://github.com/KDrewLab/huMAP3.0_analysis/tree/main/huMAP3.0_model_devel) |
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and on our HuggingFace dataset repo [sfisch/hu.MAP3.0](https://huggingface.co/datasets/sfisch/hu.MAP3.0) |
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## Model card authors |
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Samantha Fischer (sfisch6@uic.edu) |
