awesome-proteomics

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

Last synced: 10 days ago
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10. Multi-Omics data analysis
- Table of Contents
  - moCluster - R - Integration of multiple omics datasets to identify patterns - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.5b00824)
  - MOGSA - R - Multiple omics data integrative clustering and gene set analysis - [paper](https://www.mcponline.org/article/S1535-9476(20)32768-7/fulltext)
1A. Leaning Resources - Proteomics
- Table of Contents
1B. Learning Resources - Programming
- Table of Contents
1. Leaning Resources
- Table of Contents
  - Review article
  - Tutorial videos - translational modifications and more.
  - Videos from Nikolai
  - Videos from Matthew Padula
  - Review article
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2. Databases
- Table of Contents
  - ProteomeXchange
  - massIVE
  - Pride database
  - Pastel BioScience
  - Uniprot - Has all the information you will ever need to know for individual proteins and the go to for protein FASTA databases.
  - 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)
  - ProteomeXchange
  - Pastel BioScience
3. Raw data search software/algorithms
- Table of Contents
  - MaxQuant - [paper](https://www.nature.com/articles/nbt.1511)
  - Peptide-shaker
  - skyline - software for targeted proteomics - [paper](https://pubmed.ncbi.nlm.nih.gov/20147306/)
  - 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)
4. Assorted pipeline Tools
- Table of Contents
  - MaxQuant Live
  - RawBeans - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00956?goto=supporting-info)
  - 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)
  - 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)
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/)
  - pyteomics - python - proteomics framework tools - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.8b00576)
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.
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)

Programming Languages

R 18 Python 9 C# 3 C++ 3 Java 2 HTML 2 Jupyter Notebook 2 Rust 2 JavaScript 1 Nextflow 1

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Awesome

awesome-proteomics

10. Multi-Omics data analysis

Table of Contents

1A. Leaning Resources - Proteomics

Table of Contents

1B. Learning Resources - Programming

Table of Contents

1. Leaning Resources

Table of Contents

2. Databases

Table of Contents

3. Raw data search software/algorithms

Table of Contents

4. Assorted pipeline Tools

Table of Contents

5. Raw Data Analysis

Table of Contents

6. Stastical Analysis

Table of Contents

7. Protein Pathway Enrichment

Table of Contents