README

Please note: This repo is in full development, please handle with care.

In human genetics, an important task is validating mutations that have been found using exome or whole genome sequencing with a PCR spanning the abnormal region. This task is not difficult, but surprisingly laborious when done manually.

This workflow automates the design of PCR and qPCR primers. As input, it takes either:

• an HGVS-formatted mutation, e.g. NM_000546.6:c.215C>G (for PCR)
• a gene name and exon number, e.g. NM_007294.4::14 (for qPCR)

The workflow will:

• validate HGVS syntax
• for mutations in coding regions try to span the exon
• constrain primers to desired parameters
• construct a second backup pair of primers that does not overlap the first pair
• avoid placing primers across known common variants
• validate all primers to not produce unspecific amplicons

Setup

NAME=primer4
conda create -y -n $NAME -c bioconda -c conda-forge mamba python=3.8
conda activate $NAME

mamba install -y -c bioconda -c conda-forge \
    pandas \
    numpy \
    click \
    pip \
    pytest \
    streamlit=1.8.1 \
    hgvs=1.5.1 \
    blast=2.12.0 \
    pyfaidx=0.6.3.1 \
    gffutils=0.10.1 \
    pysam=0.17.0 \
    primer3-py=0.6.1 \
    pygenometracks=3.7

pip install cdot==0.2.2
# streamlit-aggrid==0.3.3

# conda list -n $NAME | grep -v '#' > requirements.txt

git clone https://github.com/phiweger/primer4 && cd primer4 && pip install -e .

Test

pytest --config config.json

Run

streamlit run primer4/stream.py -- --config config.json
# Args after "--":
# https://discuss.streamlit.io/t/command-line-arguments/386/4

You should see something like this (modify tracks.empty.ini to adjust the plot; additional options are documented in tracks.annotation.ini):

Data

See how to obtain or create the required data.