hops_enrich

Tools for processing phage display data to allow machine learning using hops.

Installation

Pip install (recommended)

Open a terminal and type:

pip3 install hops_enrich

Manual installation

hops_enrich requires python3. It depends on numpy, scipy, matplotlib, and fast_dbscan. Once these are installed, open a terminal in a convenient location on your computer and run:

git clone https://github.com/harmslab/hops_enrich
cd hops_enrich/
sudo python3 setup.py install

Quick start

After installing, make a directory that has two fastq.gz files holding Illumina reads for the experiment done in the presence and absence of competitor. In the example, these are called alone.tar.gz and competitor.tar.gz. Then run:

hops_enrich alone.tar.gz competitor.tar.gz

This will calculate the enrichment of peptides in the competitor relative to the sample alone. For a full list of options, type:

hops_enrich -h

Scripts

This will install the following scripts in the console path:

hops_count: count peptides in an Illumina run hops_cluster: cluster peptides by sequence hops_enrich: calculate enrichment of peptides between runs