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.
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
Easy-Prime on AWS
Please use this URL for now: http://easy-prime-test-dev.us-west-2.elasticbeanstalk.com/
We will deploy it to St. Jude once we get approved from the IT department.
Tutorial
Input
- vcf input example
VCF headers will be ignored. Only the first 5 columns from the vcf file will be used; they are: chr, pos, name/id, ref, alt.
## comment line, will be ignored
chr9 110184636 FIG5G_HEK293T_HEK3_6XHIS G GCACCATCATCACCATCAT
chr1 185056772 FIG5E_U2OS_RNF2_1CG G C
chr1 173878832 rs5878 T C
chr11 22647331 FIG3C_FANCF_7AC_PE3B T G
chr19 10244324 EDFIG5B_DNMT1_dPAM G T
- fasta input example
To specify reference and alternative allele, you need two fasta sequences; _ref is a keyword that will be recognized as the reference allele and _alt is a keyword for target mutations.
>test_ref
AAAAAAAAAAAAAAAAAAAAAAAAAGGAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
>test_alt
AAAAAAAAAAAAAAAAAAAAAAAAAGGAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACGAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
Parameters
Genome: only support hg19 for now.
Results
The web output contain two parts:
- Sequence visualization
By default, the top prediction will be shown automatically. Users can input the sample ID (in the table below) to plot specific prediction.
- pegRNA table
In this result table, each predicted sgRNA/ngRNA/RTT/PBS configuration will be provided in 4 rows, they will have the same sample ID and predicted efficiency.
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.
