MassSpecCodingClub

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MassSpecCodingClub

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A community dedicated to education of computer coding applied to mass spectrometry applications.

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# Mass Spec Coding Club
The Mass Spec Coding Club (MSCC) is a community dedicated to education of computer coding applied to mass spectrometry applications. Our goal is to make coding accessible to mass spectrometry researchers and provide free resources and open-source examples.

As the community develops, we will continue to post more content, and we welcome contributions from anyone.

# Discord Server

Want to chat with community members and join meetings, join us on the [Mass Spec Coding Club Discord Server](https://discord.gg/24GupxGn3d). It's easy to set up, and you can run it from a browser if you'd like. We will pick a time soon and start hosting meetings/office hours there.

In the meantime, feel free to post questions to the text channels there, and people can answer.

# Learning Modules

## Module 0: Setting Up Python and Plotting A Spectrum

This series of lessons will cover how to set up Python from scratch and write a simple script to plot a mass spectrum. Skills and learning outcomes are outlined below each video

* [Lesson 0.0: Setting up Python from Scratch](https://youtu.be/BLaoo1S3ImU)
  * How to set up and run Python
  * Setting variables
  * Printing to the terminal
* [Lesson 0.1: Loading Data Into Python](https://youtu.be/vpbdUQp8m0U)
  * Importing libraries
  * Reading from text files into NumPy arrays
  * Intro to array slicing
* [Lesson 0.2: Plotting a Spectrum](https://youtu.be/88m4a9CEeBY)
  * Plotting a spectrum with MatPlotLib
  * Normalizing the y-axis
* [Lesson 0.3: Too Fast, Go Back - Review and Background from Module 0](https://youtu.be/V6alRhace2A)
  * Fundamentals of how computers work
  * Basics of code concepts
  * Discussion of variables, functions, and classes
  * How to define functions

The data files, Python code, and notes used in this module are available in the "Module 0" folder.

## Module 1: Calculating Masses

The goal of module 1 is to show how Python can be used to predict masses of various molecules, starting with proteins.

* [Lesson 1.0: Calculating Protein Masses](https://youtu.be/FFR1gg2cA6E)
  * Using a Dictionary
  * Creating a function
  * Looping through protein sequence to calculate the protein mass
* [Lesson 1.1: Improving Our Protein Mass Calculator](https://youtu.be/lxTrA_EPeNg)
  * String manipulation
  * Passing variables through functions
  * If/then statements
  * Monoisotopic mass calculation for protein
* [Lesson 1.2: Calculating Masses from Glycans, SMILES, and Formulas](https://youtu.be/XSgA7SODmSg)
  * Using Glypy to calculate masses from GlycoCT strings
  * Using molmass to calculate masses from formulas
  * Using RDKit to calculate masses from SMILES strings
* [Lesson 1.3: Too Fast, Go Back - For Loops, If/Then, and Function Options](https://youtu.be/xg1QxAzznkg)
  * Writing For loops
  * If/Then statements and Boolean tests
  * Passing options to functions
* Homework 1
  * For those who want to test their skills and calculate some RNA masses, check out homework1.py in Module 1.

## Module 2: Parsing Fasta and Excel Files

The goal of module 2 is to parse larger FASTA and Excel files to pull in the desired information and write it to an Excel file output.

* [Lesson 2.0: Importing FASTA Files](https://youtu.be/RL5JLfhsIQU)
  * Importing Pyteomics library
  * Importing our own functions from Module 1
  * Looping through a FASTA file to calculate masses
* [Lesson 2.1: Parsing FASTA Descriptions and Writing to Excel](https://youtu.be/LVvW81sCW8U)
  * Using string splitting to parse protein names and entries
  * Creating lists and adding to a Pandas DataFrame
  * Exporting DataFrames
* [Lesson 2.2: Reading, Parsing, and Exporting DataFrames - DDA to MRM](https://youtu.be/fFYpOr_u79o)
  * Reading Excel files to Pandas DataFrames
  * Parsing DataFrame rows
  * Extracting the position of the most abundant product ion
  * Exporting Transition List to Excel
* [Lesson 2.3: Too Fast, Go Back - NumPy Array Slicing and Boolean Indexing](https://youtu.be/A5iCCk-Q0lQ)
  * Slicing NumPy arrays
  * Using Boolean indexing for more sophisticated array slicing
* Homework 2
  * For those who want more practice, follow up on my suggestion from Lesson 2.2 to turn an MRM list into a PRM list.
There are several ways you could do this. You could pick the top N peaks in the product ion spectrum (np.sort might be useful here).
You could also pick any peak above a specific relative intensity threshold (Boolean indexing might be useful here). 
Just add each as a new row to the datalist with the rest of the things the same.

Check back for more videos, and reach out if you like these [mtmarty@utexas.edu](mailto:mtmarty@arizona.edu).

# Ideas for Future Tutorials

Here are some ideas that users have suggested. If you have other suggestions, please enter them in the ["What Projects Would You Like to See?"](https://github.com/michaelmarty/MassSpecCodingClub/discussions/3) discussion. If you would like to volunteer to make a module on one of these topics, please add your name here.

* Plotting multiple spectra with for loops and string parsing (Michael Marty)
* Reading vendor files
* Writing to different output files
* Exploring other Python MS packages
* How to use public databases (Ming?)
* Applications to polymers and oligonucleotides
* Ion mobility
* Using Git and GitHub
* Gasp, R!
  * There are a lot of great R resources for MS already, so maybe we could organize and link those here too.

# Funding

Funding is provided by the National Science Foundation: CHE-1845230.

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