This project uses Collagen 1 sequence data downloaded from databases (e.g., NCBI protein) to semi-automate the identification process of Zoo Archaelology by Mass Spectrometry (ZooMS) Peptide Mass Fingerprint (PMF) data. In order to do this there are two pipelines for this process:
- Theoretical peptide m/z values for species are generated from COL1α1 and COL1α2 sequences downloaded as FASTA files from a database. A README describes the process within the folder
- The experimental m/z value file (created in software such as mMass) is compared to the theoretical peptide m/z values for matches. A README describes the process within the folder
To briefly explain the pipelines, the COL1α1 and COL1α2 sequences are downloaded as a fasta file from a database for a group of species (e.g., mammals). The COL1α1 and COL1α2 sequences are cleaned and merged to form COL1. The COL1 sequences undergo in silico trypsin digest to form peptides allowing for up to one missed cleavage. The m/z values for the peptides are calculated based off their contistuent amino acids masses. These m/z values need to account for all combinations of post-translational modifications (PTMs). These PTMs are filtered based on the likely PTMs from previous liquid chromatography mass spectrometry mass spectrometry ZooMS Mascot results to reduce the number of possible m/z values. The output for each species is a csv file containing the species name and the m/z values for all the peptides. Peak picking from the ZooMS experimental PMF leads to a text file with experimental m/z values (this needs to be done with external software). The experimental m/z values and the species theoretical m/z values are compared for matches within a certain threshold (e.g., +- 0.2). The species with the most matches is the most closely related species to the sample.
Currently still in development and not published as a package on PyPi. Therefore, currently the repository has to be installed directly from git.
Option 1 - Pip
As the package is not currently published you must use pip to install directly from the github repository. Note: the package requires python v3.12 or above.
pip install git+https://github.com/TobyL98/RP1_m-z_speciesidentify.git
Option 2 - Conda
Unfortunately the package is not available in conda. However a conda environment with the correct dependencies is installed using the yaml file. You will need to clone the git repository to use the yaml file or download the yaml file seperately.
conda env create -f species_identify.yml
Clone this repository
If you would like to use the data from within the repository you can download it directly or clone the repository.
git clone https://github.com/TobyL98/RP1_m-z_speciesidentify.git
If you do not have git installed you can instead in the github repository select 'Code' and 'Download Zip'. Then save and unzip the foldr in the location you choose.
For further instructions on how to generate the theoretical peptide m/z values from COL1 peptide sequences and to use these to predict the identity of species from its ZooMS fingerprint look at:
docs/generate_peptides.md
and
docs/predict_species.md
Contributions are welcomed for this repository!
Please raise any issues that you encounter