ziul-bio / Protegrin-1_Slay_and_ML

W3110_PG-1 Project Workflow

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Deep mutational scanning and machine learning uncover antimicrobial peptide features driving membrane selectivity

THIS IS A OLD VERSION OF THE REPOSITORY, PLEASE REFER TO THE UPDATED VERSION AT: LATEST VERSION

This workflow is based on the paper "Tucker et al., Cell. 2018. Discovery of Next-Generation
Antimicrobials through Bacterial Self-Screening of Surface-Displayed Peptide Libraries". Link

Some changes were applied in the pipeline to fit the new data.

Here we describe all the steps required to reproduce the analysis on the paper "Deep mutational scanning and machine learning uncover antimicrobial peptide features driving membrane selectivity", Author Justin R. Randall.

Workflow

This work flow is divided in 2 parts. Part1 - Deep mutational scanning and Part2 - machine learning.

Get Counts

One can run the script getCounts.sh to obtain the result we did.
To run this script you will need seqkit installed in a unix enviromet.

Differential Expression Analysis

The differential analysis was done in R with Deseq2, described in the notebook deseq2_analysis.rmd.

Compute Changes in the Peptides Sequence

We translated the peptide sequences with the biopython translate function, and compute the differences on the sequence with the reference protegrin-1 protein using a custom python script, described on translate_and_compute_changes_in_peptides.ipynb.

All the code are commented so feel free to change the parameters to suit your data and needs.

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W3110_PG-1 Project Workflow


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