{"id":16509211,"url":"https://github.com/pwilmart/dilution_series","last_synced_at":"2026-05-13T09:31:48.450Z","repository":{"id":94498874,"uuid":"161097026","full_name":"pwilmart/Dilution_series","owner":"pwilmart","description":"Tandem Mass Tag (TMT) dilution series analysis","archived":false,"fork":false,"pushed_at":"2019-03-27T00:13:06.000Z","size":11315,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-12T18:33:22.023Z","etag":null,"topics":["jupyter-notebook","paw-pipeline","proteomics","r","tmt","tmt-data-analyses"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pwilmart.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}},"created_at":"2018-12-10T00:56:02.000Z","updated_at":"2019-07-25T23:57:07.000Z","dependencies_parsed_at":"2023-05-10T13:15:55.024Z","dependency_job_id":null,"html_url":"https://github.com/pwilmart/Dilution_series","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pwilmart%2FDilution_series","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pwilmart%2FDilution_series/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pwilmart%2FDilution_series/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pwilmart%2FDilution_series/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pwilmart","download_url":"https://codeload.github.com/pwilmart/Dilution_series/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241465113,"owners_count":19967243,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["jupyter-notebook","paw-pipeline","proteomics","r","tmt","tmt-data-analyses"],"created_at":"2024-10-11T15:49:21.325Z","updated_at":"2026-05-13T09:31:43.429Z","avatar_url":"https://github.com/pwilmart.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Analysis of a dilution series\n\nYou can view the notebook in your browser at [this link](https://pwilmart.github.io/TMT_analysis_examples/MAN1353_peptides_proteins.html) or\nclick on the Jupyter notebook file (_MAN1353_peptides_proteins.ipynb_) to see the notebook in your browser. It may take a minute for the page to render and load, so please be patient.\n\n---\n\nThe sample is a mouse brain prep that was digested, split into 6 aliquots, digested, and TMT labeled. A mixture was created with\nrelative volumes of 25 to 20 to 15 to 10 to 5 to 2.5. The mixture was run on a Thermo Fusion using the synchronous precursor scan\nMS3 method:\n\n\u003e McAlister, G.C., Nusinow, D.P., Jedrychowski, M.P., Wühr, M., Huttlin, E.L., Erickson, B.K., Rad, R., Haas, W. and Gygi, S.P., 2014. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Analytical chemistry, 86(14), pp.7150-7158.\n\nThe data were processed with MSConvert from Proteowizard to extract the MS2 and MS3 scans. The MS2 scans were processed with Comet\nto identify the peptides. Accurate mass and target/decoy methods were used to filter PSMs to a 1% FDR. Parsimonious protein inference\nand protein grouping were used to create protein and peptide reports. Shared or unique peptide status was determined based on the final\nlist of identified proteins. Unique peptides were used for quantification.\n\n\u003e Chambers, M.C., Maclean, B., Burke, R., Amodei, D., Ruderman, D.L., Neumann, S., Gatto, L., Fischer, B., Pratt, B., Egertson, J.\nand Hoff, K., 2012. A cross-platform toolkit for mass spectrometry and proteomics. Nature biotechnology, 30(10), p.918.\n\n\u003e Eng, J.K., Jahan, T.A. and Hoopmann, M.R., 2013. Comet: an open‐source MS/MS sequence database search tool.\nProteomics, 13(1), pp.22-24.\n\n\u003e Wilmarth, P.A., Riviere, M.A. and David, L.L., 2009. Techniques for accurate protein identification in shotgun proteomic studies of\nhuman, mouse, bovine, and chicken lenses. Journal of ocular biology, diseases, and informatics, 2(4), pp.223-234.\n\nThe analysis looks at the properties (mostly variance) of the reporter ion data at three levels: PSMs, peptides, and proteins. The\nPSMs are the raw MS3 scan data; peptide sequences have not been determined. The PSMs will include non-identifiable scans, contaminants,\ndecoys, incorrect scans, and correct scans. The peptides are much cleaner. They are derived from 1% FDR PSMs. Contaminants and decoys\nhave been removed, and shared peptides have been excluded. Peptides also have some limited degree of aggregation. Multiple MS2 scans and\ndifferent charge states have been combined.\n\nReporter ions for proteins are aggregated (summed) from the reporter ions of all constituent peptides. Only the unique peptides are\nused in the sums. There is a great reduction in the number of proteins compared to the number of peptides or PSMs. This greatly\nsimplifies analyses. The analysis also demonstrates that the aggregation improves the quality of the reporter ion measurements.\n\nAn interesting aside is looking at the normalization functions in edgeR. edgeR is a useful RNA-Seq statistical package that I have\nused in other work in this and related repositories. The normalization factors that edgeR reports can seem confusing and they are\nexplained here.\n\n\u003e Robinson, M.D., McCarthy, D.J. and Smyth, G.K., 2010. edgeR: a Bioconductor package for differential expression analysis of\ndigital gene expression data. Bioinformatics, 26(1), pp.139-140.\n\n\u003e Robinson, M.D. and Oshlack, A., 2010. A scaling normalization method for differential expression analysis of RNA-seq data.\nGenome biology, 11(3), p.R25.\n\nThe January 2019 update has some better notebook organization, better R scripting, and more use of [tidyverse](https://www.tidyverse.org/) and [ggplot2](https://ggplot2.tidyverse.org/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpwilmart%2Fdilution_series","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpwilmart%2Fdilution_series","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpwilmart%2Fdilution_series/lists"}