easy-prime 1.1.3

Prime editor gRNA design tool

Easy-Prime: an optimized prime editor gRNA design tool based on gradient boosting trees

Easy-Prime provides optimized pegRNA and ngRNA combinations for efficient Prime editing design.

Summary

PE design involves carefully choosing a standard sgRNA, a RT template that contains the desired edits, a PBS that primes the RT reaction, and a ngRNA that nicks the non-edit strand. Usually thousands of combinations are available for one single disired edit. Therefore, it is overwhelming to select the most likely high-efficient candidate from the huge number of combinations.

Easy-Prime applies a machine learning model (i.e., XGboost) that learned important PE design features from public PE amplicon sequencing data to help researchers selecting the best candidate.

Installation

The most easiest way to install Easy-Prime is via conda.

conda create -n genome_editing -c cheng_lab easy_prime

source activate genome_editing

easy_prime -h

easy_prime_vis -h

Usage

git clone https://github.com/YichaoOU/easy_prime

cd easy_prime/test

easy_prime -h

easy_prime --version

## Please update the genome_fasta in config.yaml 

easy_prime -c config.yaml -f test.vcf

## Will output results to a folder

Easy-Prime also provides a dash application. [NOT WORKING]

Please have dash installed before running the dash application.

git clone https://github.com/YichaoOU/easy_prime

cd easy_prime/dash_app

python main.py

Input

A vcf file containing at least 5 columns. See test/test.vcf for examples.

Searching parameters for PE design

Default values are shown in the following yaml files.

genome_fasta: /path/to/genome.fa
scaffold: GTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGC
debug: 0
n_jobs: 4
min_PBS_length: 8
max_PBS_length: 17
min_RTT_length: 10
max_RTT_length: 25
min_distance_RTT5: 3
max_ngRNA_distance: 100
max_target_to_sgRNA: 10
sgRNA_length: 20
offset: -3
PAM: NGG

Output

The output folder contains:

• topX_pegRNAs.csv
• rawX_pegRNAs.csv.gz
• X_p_pegRNAs.csv.gz
• summary.csv

The top candidates are provided in topX_pegRNAs.csv. This is a rawX format file.

rawX format

X means the input to machine learning models. Here, rawX basically means the file before machine learning featurization. Specifically, rawX contains 11 + 1 columns. The first 5 columns are from the input vcf file: sample_ID, chr, pos, ref, alt, where sample_ID ends with _candidate_xxx, this indicates the N-th combination. The next 6 columns are genomic coordinates: type, seq, chr, start, end, strand, where the type could be sgRNA, PBS, RTT, or ngRNA. Since for one PE design, it has to have these 4 components, which means that for one unique sample_ID, it has 4 rows specifying the sequences for each of them. The 12-th column, which is optional, is the predicted efficiency; in other words, the Y for machine learning.

Both topX_pegRNAs.csv and rawX_pegRNAs.csv.gz use this format.

X format

X format is the numeric representation of rawX. X_p format appends the predicted efficiency to the last column of X.

Main results

The main results, which is the top condidates, is provided in topX_pegRNAs.csv.

PE design visualization

Users can visualize the predicted combinations using:

easy_prime_vis -f topX_pegRNAs.csv -s /path/to/genome_fasta.fa

This will output pdf files to a result dir.