This GitHub repository is really just a jumping point for different sets of tutorials. The goal here is to help people understand different topics within computational proteomics. The overall compendium will continue to grow with new topics as we create new materials.
We would love for others to contribute to this. Please email the Payne Lab to start the discussion.
These lessons are designed to serve as a soft introduction to the field of proteomics and the computational challenges therin. Our overall goal in these lessons is to approach proteomics from a computational perspective to help train and attract bioinformatics and computational students into the field. These tutorials were described in this publication. Currently-available lessons can be found in the Notebooks_Peptide-ID-and-Quant-of-MS-Data folder. You can run these .ipynb files locally by downloading these files or cloning the repository. If you would prefer to run the tutorials online through Google Colab, you can find the lessons linked below.
- Lesson 1: Measuring Mass
- Lesson 2: Measuring Mass in More Complex Mixtures
- Lesson 3: LC-MS
- Lesson 4: Identifying Peptide Sequences with MS/MS
- Lesson 5: Advanced Analysis in MS/MS
- Lesson 6: Peptide Quantitation
In proteomics, peptide sequences are a foundational data type. They are the basic thing we measure with mass spectrometry, and so they are often the at the center of various machine learning tasks. Peptide sequences are typically represented in text as a string (e.g. SAMPLER). However, ML requires inputs to be numeric. In order for ML to be able to use our data, we need to first convert the sequences into numeric representations-- embeddings. This series of notebooks covers the philosophical and technical details of peptide embeddings. Currently-available lessons can be found in the Notebooks_ML-for-Proteomics-Peptide-Embeddings folder. You can run these .ipynb files locally by downloading these files or cloning the repository. If you would prefer to run the tutorials online through Google Colab, you can find the lessons linked below.