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awesome-proteomics

An awesome list of proteomics tools and resources
https://github.com/lyons89/awesome-proteomics

Last synced: 3 days ago
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  • 1A. Leaning Resources - Proteomics

  • 1. Leaning Resources

  • 2. Databases

    • Table of Contents

      • ProteomeXchange
      • massIVE
      • Pride database
      • Pastel BioScience
      • Biogrid - protein-protein interaction database
      • KEGG - biological pathway database
      • Reactome - nicer looking biological pathway database
      • CPTAC - python/R - API interaface to publically available cancer datasets - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00919)
      • ppx - python - Python interface to proteomics data repositories - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.1c00454)
      • Uniprot - Has all the information you will ever need to know for individual proteins and the go to for protein FASTA databases.

    • Table of Contents

      • Peptide-shaker
      • Fragpipe - Java - It's a very fast search engine with a nice GUI. The software is modular, it consists of [MSfragger](https://msfragger.nesvilab.org/) the database search algorithm, [Philosopher](https://philosopher.nesvilab.org/) that analyzes the database results, as well as others for PTM and TMT integration.
      • Comet - C++ - Free and open-source search engine, lately it's had several - [paper](https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/pmic.201200439)
      • DIA-NN - C/C++ - free and open source search tool for DIA data that uses neural networks, works using either a library or a FASTA database. - [paper](https://www.nature.com/articles/s41592-019-0638-x)
      • Sage - Rust - most likely the current fasest search engine, it's completely terminal based but if you learn to use it, it will be worth it - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.3c00486?ref=PDF)
      • MaxQuant - [paper](https://www.nature.com/articles/nbt.1511)
      • skyline - software for targeted proteomics - [paper](https://pubmed.ncbi.nlm.nih.gov/20147306/)

  • 4. Assorted pipeline Tools

    • Table of Contents

      • MaxQuant Live
      • RawBeans - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00956?goto=supporting-info)
      • DIAproteomics - python - a module that can be added to a openMS workflow for the analysis of DIA data - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.1c00123)
      • PAW_pipeline - python - a pretty much stock python raw file protoemics pipeline tool. It includes functions, to convert files, run comet, produce histograms. Can also do TMT - [paper](https://pubmed.ncbi.nlm.nih.gov/20157357/)
      • Ursgal - python - combines multiple search engine algorithms, postprocessing algorithms, and stastis on the output from multiple search engines - [paper1](https://pubs.acs.org/doi/10.1021/acs.jproteome.5b00860) [paper2](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00799)
      • DIAlignR - R - DIA retention time alignment of targetd MS data, including DIA and SWATH-MS - [paper](https://www.mcponline.org/article/S1535-9476(20)31843-0/fulltext)
      • Monocle - C# - for monoisotopic peak and accurate precursor m/z detection in shotgun proteomics experiments. - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00563)
      • mokapot - python - Semisupervised Learning for Peptide Detection - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c01010)
      • qcloud2 - cloud based quality control pipeline, can be integrated with nextflow and openMS - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00853)

  • 5. Raw Data Analysis

    • Table of Contents

      • pyteomics - python - proteomics framework tools - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.8b00576)
      • MSstatsQC - R - provides methods for multiple peptide monitoring using raw MS files, works for DDA and DIA data - [paper](https://pubs.acs.org/doi/full/10.1021/acs.jproteome.8b00732)
      • RforMassSpectrometry - R - a massive project that contains multiple helpful packages including RforMassSpectrometry, MsExperiment, Spectra, QFeatures, PSMatch, Chromatograms, MsCoreUtils, and MetaboCoreUtils.
      • MSnbase - R - provides MS data structures, allows you to process, quantify, visualize raw data - [paper](https://academic.oup.com/bioinformatics/article/28/2/288/199094?login=false)
      • MaRaCluster - C++ - clustering technique to identify fragment spectra stemming from the same peptide species - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.5b00749)
      • pyproteome - python - analyzes proteomics data, can filter, normalize, perform motif and pathway enrichment. Currently only supports ProteomeDiscoverer .msf search files - [paper](https://pyproteome.readthedocs.io/en/latest/)
      • RawTools - C# - quality control checking of raw files, can assist in method development and insturment quality control - [paper](https://pubs.acs.org/doi/full/10.1021/acs.jproteome.8b00721)
      • rawDiag - R - Package that can be used in conjustion with rawrr - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.8b00173)
      • COSS - java - user-friendly spectral library search tool - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.9b00743)
      • rawrr - R - A great package that can read in raw thermo files! Thats great to me, because I always find it tedious to convert a raw file into a mzML or mzXML file - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00866)
      • PSpecteR - R - User Friendly and Interactive for Visualizing the quality of Top-Down and Bottom-Up Proteomics - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00857)
      • mpwR - R - package that allows you to directly compare the output of raw search engines such as MQ, DIANN, spectronaut and I think PD. It's also helpful if you're testing out different settings within your search engine and you want to quickly see how each performs. - [paper](https://pubmed.ncbi.nlm.nih.gov/37267150/)

  • 7. Protein Pathway Enrichment

    • Table of Contents

      • lipidR - R - lipidomics data analysis - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00082)
      • fgsea - R - fast gene set enrichment analysis - [paper](https://www.biorxiv.org/content/10.1101/060012v2.full)
      • pathfindR - R - active subnetwork oriented pathway enrichment analyses that uses protein-protein ineteraction networks to enchance the standard pathway analysis method - [paper](https://www.frontiersin.org/articles/10.3389/fgene.2019.00858/full)
      • phosphoRWHN - R - pathway enrichment for phosphoproteomics data - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.1c00150)
      • leapR - R - package for multiple pathway analysis - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00963)
      • leapR - R - package for multiple pathway analysis - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00963)

  • Miscellaneous

    • Table of Contents

      • cytoscape - visualizing protein-protein interaction netweorks
      • ProteoWizard - Great software for converting one MS file type to another. I mostly use it ot convert thermo .raw files to mzML - [paper](https://www.nature.com/articles/nbt.2377)
      • IPSC - Interactive Peptide Spectrum Annotator, web based utility for shotgun mass spectrum annotation - [paper](https://www.mcponline.org/article/S1535-9476(20)32771-7/fulltext)
      • PeCorA - R - peptide correlation analysis - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00602)
      • ProteaseGuru - C# - tool for In Silico Database Digestion, optimize bottom up experiments - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00954)
      • DeepLC - python - predicts retention times for peptides that have unseen modifications - [paper](https://www.nature.com/articles/s41592-021-01301-5)

  • 1B. Learning Resources - Programming

  • 6. Stastical Analysis

    • Table of Contents

      • MSstats - R - DDA/shotgun, bottom-up, SRM, DIA - [paper](https://academic.oup.com/bioinformatics/article/30/17/2524/2748156?login=false)
      • PaDuA - python - proteomics and phosphoproteomics data analysis - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.8b00576)
      • MSstatsTMT - R - TMT shotgun proteomics - [paper](https://www.mcponline.org/article/S1535-9476(20)35114-8/fulltext)
      • proteiNorm - R - TMT and unlabeled, has multiple options for normalization and statistical analysis - [paper](https://pubs.acs.org/doi/10.1021/acsomega.0c02564)
      • DEqMS - R - Developed ontop of limma, but takes into account variability in PSMs. Works on both labelled and unlabelled samples - [paper](https://www.mcponline.org/article/S1535-9476(20)34997-5/fulltext)
      • MSstatsPTM - labeled and unlabeled PTM data analysis - [paper](https://www.mcponline.org/article/S1535-9476(22)00285-7/fulltext)
      • PermFDP - R - Package to perform multiple hypothesis correction using permutation based FDP. One of the better performing methods for multiple test corrections. - [paper](https://pubs.acs.org/doi/full/10.1021/acs.analchem.2c03719?casa_token=4CgZMMnAmjgAAAAA%3A-8SyKwz2Hs3L-yRXXQDkq45ZBPW8nexcpqaYzDB5Ok-Kgp0C_W9KPscLE-zUfN2nZUv8uiNYZZcCxy-7) the paper isn't on the tool, it's just a paper that uses it and compares it to other methods.

  • 8. Kinase Motif/Activity Analysis

    • Table of Contents

      • KSEAapp - R - Kinase substrate enrichment analysis. I would recommend using with a freshly downloaded kinase-substarte database from phosphositeplus - [paper](https://pubmed.ncbi.nlm.nih.gov/28655153/)
      • rnotifx - R - motif enrichment analyssis of PTMs on proteins, probably mostly used for phosphorylation - [paper](https://pubmed.ncbi.nlm.nih.gov/26572964/)
      • KSEAapp - R - Kinase substrate enrichment analysis. I would recommend using with a freshly downloaded kinase-substarte database from phosphositeplus - [paper](https://pubmed.ncbi.nlm.nih.gov/28655153/)

  • 9. Top down data analysis

    • Table of Contents

      • ClipsMS - python - analysis of terminal and internal fragments in top-down mass spectrometry data - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00952)

  • 10. Multi-Omics data analysis

    • Table of Contents

      • MOGSA - R - Multiple omics data integrative clustering and gene set analysis - [paper](https://www.mcponline.org/article/S1535-9476(20)32768-7/fulltext)
      • moCluster - R - Integration of multiple omics datasets to identify patterns - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.5b00824)
Programming Languages
R 19 Python 9 C# 3 C++ 3 Java 2 HTML 2 Jupyter Notebook 2 Rust 2 JavaScript 1 Nextflow 1
Categories
1A. Leaning Resources - Proteomics 49 1. Leaning Resources 24 5. Raw Data Analysis 12 2. Databases 10 4. Assorted pipeline Tools 9 6. Stastical Analysis 7 3. Raw data search software/algorithms 7 7. Protein Pathway Enrichment 6 Miscellaneous 6 1B. Learning Resources - Programming 5 8. Kinase Motif/Activity Analysis 3 10. Multi-Omics data analysis 2 9. Top down data analysis 1
Sub Categories
Table of Contents 141
Keywords
proteomics 8 mass-spectrometry 7 bioinformatics 4 r 4 proteomics-data 3 python 3 tmt 2 orbitrap-ms 2 fast 2 multiplatform 2 rpackage 2 machine-learning 2 omics 1 mzml 1 ms-data 1 bioconda 1 python3 1 protein-inference 1 protein-identification 1 comet 1 tutorial 1 algebra 1 calculus 1 search-engine 1 linear-algebra 1 mathematics 1 pipeline 1 matrices 1 gui 1 retention-time 1 peptides 1 peptide-modification 1 deep-learning 1 subnetwork 1 pathway-enrichment-analysis 1 pathway 1 enrichment 1 active-subnetworks 1 quantitative-proteomic-analysis 1 limma 1 tmt-data-analysis 1 msstats 1 labeling 1 visualization 1 orbitrap 1 mass-spectrometry-data 1 csharp-code 1 visualisation 1 bioconductor 1 percolator 1