{"id":47803038,"url":"https://github.com/scverse/202603_hackathon_proteomics","last_synced_at":"2026-04-03T17:15:01.809Z","repository":{"id":348017881,"uuid":"1192783167","full_name":"scverse/202603_hackathon_proteomics","owner":"scverse","description":null,"archived":false,"fork":false,"pushed_at":"2026-03-30T14:05:32.000Z","size":31,"stargazers_count":6,"open_issues_count":10,"forks_count":3,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-03-30T14:26:45.834Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/scverse.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2026-03-26T14:58:49.000Z","updated_at":"2026-03-30T14:05:56.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/scverse/202603_hackathon_proteomics","commit_stats":null,"previous_names":["scverse/202603_hackathon_proteomics"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/scverse/202603_hackathon_proteomics","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scverse%2F202603_hackathon_proteomics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scverse%2F202603_hackathon_proteomics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scverse%2F202603_hackathon_proteomics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scverse%2F202603_hackathon_proteomics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/scverse","download_url":"https://codeload.github.com/scverse/202603_hackathon_proteomics/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scverse%2F202603_hackathon_proteomics/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31365306,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-03T17:13:05.644Z","status":"ssl_error","status_checked_at":"2026-04-03T17:13:04.413Z","response_time":107,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-04-03T17:15:01.082Z","updated_at":"2026-04-03T17:15:01.788Z","avatar_url":"https://github.com/scverse.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# README\n\nRelated [GitHub Issue](https://github.com/scverse/mudata/issues/111)\n\n## Description\n\n\u003e Build a scverse-native data format that accounts for the hierarchical nature of quantification in MS-based proteomics.\n\n### Problem Statement\n\nIn LC/MS-proteomics, there is a naturally arising hierarchical feature structure\n\n- At the lowest level, mass spectrometers detect + quantify _fragments_ from charged peptides (_precursors_) in the mass spectrometry (MS) instruments. The precursor-level data is relatively large (N samples x ~100 000 features)\n- Proteomics search engines identify the precursor sequences and match them to their corresponding proteins. Ultimately, search engines derive _protein_-specific intensities (N samples x 3000-10000 features).\n\nThe key challenge is that there exists an N:M relationship between precursors and proteins, i.e. many precursors can map to one protein and sometimes a precursor could potentially be derived from different (homologous) proteins.\n\nThe main extension of the data format to existing data containers like mudata would be the formalization the relationship/mapping between the fundamental units of quantification in MS-proteomics (fragments, precursors) and high-level, biologically more relevant aggregated features (peptides, proteins, genes).\n\n- [x] Implement an RFC (see rfc/RFC.md).\n- [x] Implement the prototypes of the data structure that have been proposed in an scverse (i.e. anndata/mudata) compatible manner.\n- [ ] **Application**: Implement a related, simple downstream analysis that builds on the data format to get an intuition for the API (e.g. “Plot the distribution of all precursor intensities that correspond to a specific protein”)\n- [ ] **Data ingestion**: Implement one proof-of-principle reader from a quantification pipeline/search engine output (e.g. QuantMS, DIANN, alphadia) to the data container.\n- [ ] **Application**: Aggregate a low-level feature level (e.g. precursors) to a higher-level feature level (e.g. genes)\n\n## Support\n\n## Get started\n\nWe recommend contributors to make themselves familiar with the [mudata](https://mudata.readthedocs.io/stable/notebooks/nuances.html) documentation and API.\n\nSee also [QFeatures](https://rformassspectrometry.github.io/QFeatures/articles/QFeatures.html) for a conceptually similar R-package and [alphaquant](https://github.com/MannLabs/alphaquant.git) for a potential future application of the mapping approach.\n\n## Example data\n\nUse the download script to obtain example PSM reports.\n\n```shell\nbash download.sh\n\n# download real world data\nbash data/download.sh -o data/ albrecht2025\n\n# download a minimal dataset\nbash data/download.sh -o data/ minimal\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscverse%2F202603_hackathon_proteomics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fscverse%2F202603_hackathon_proteomics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscverse%2F202603_hackathon_proteomics/lists"}