Easy-Prime provides optimized pegRNA and ngRNA combinations for efficient PE design. Visit: http://easy-prime.cc/
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 learns important PE design features from multiple published PE data sources to help researchers selecting the best candidate.
The simplest way to use easy-prime is to use our dockerized version.
docker pull liyc1989/easy_prime
docker run -p 80:80 easy_prime
ref: https://github.com/YichaoOU/easy_prime_docker
The most easiest way to install Easy-Prime is via conda (version >=4.9).
conda create -n genome_editing -c cheng_lab easy_prime
source activate genome_editing
easy_prime -h
easy_prime_vis -h
See https://easy-prime.readthedocs.io/en/latest/content/Installation.html for step-by-step installation screenshots.
## Make sure you have installed Easy-Prime before running the commands below
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, otherwise an error may occur!
easy_prime -c config.yaml -f test.vcf
## Will output results to a folder
## to output visualization, go to this output folder and run:
## Please make sure you have provided the correct path to genome fasta using -s
easy_prime_vis -f topX_pegRNAs.csv -s ~/Data/Human/hg19/fasta/hg19.fa
Easy-Prime also provides a dash application.
Please have dash installed before running the dash application.
## Make sure you have installed Easy-Prime before running the commands below
git clone https://github.com/YichaoOU/easy_prime
cd easy_prime/dash_app
## Please update the genome_fasta in config.yaml, otherwise an error may occur!
## Please also update the genome_fasta variable in line 56 in utils2.py
python application.py
Please use this URL: http://easy-prime.cc/
New to the AWS version, we added linker sequence from pegLIT prediction. It may take several minutes to get the linker sequence. See the example below.
See https://easy-prime.readthedocs.io/en/latest/content/AWS.html for step-by-step tutorial with screenshots.
- 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.
>rs2251964_ref
GTTACCAAAGCAAATGACATCTTGTGAAAGGGGAGGTCTGAAAAAAAAAAACAAGTGGGTGGGTTTTTTCAAAGTAGGCCACCGGGCCTGAGATGACCAGAATTCAAATTAGGATGACAGTGTAGTAGGGGAAGCAACCAGAATCGGACCT
>rs2251964_alt
GTTACCAAAGCAAATGACATCTTGTGAAAGGGGAGGTCTGAAAAAAAAAAACAAGTGGGTGGGTTTTTTCAAAGTAGGCCACCGGGCCTGAGATAACCAGAATTCAAATTAGGATGACAGTGTAGTAGGGGAAGCAACCAGAATCGGACCT
The PrimeDesign format input is only supported in the Easy-Prime web server. The vcf file separated by space is only supported in the Easy-Prime web server.
Genome: only support hg19 for now.
The web output contain two parts:
- pegRNA table
In this result table, each predicted sgRNA/ngRNA/RTT/PBS configuration will be provided in 4 rows, they will have the same variant ID and predicted efficiency.
- Sequence visualization
By default, the top prediction will be shown automatically.
A vcf file containing at least 5 columns. See test/test.vcf for examples.
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
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.
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 is the numeric representation of rawX. X_p format appends the predicted efficiency to the last column of X.
The main results, which is the top condidates, is provided in topX_pegRNAs.csv.
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.
Error: The requested fasta database file (/Users/yli11/Data/hg19.fa) could not be opened. Exiting!
Reading fasta file: test.vcf
{}
no valid sequences in: test.vcf
Exit...
Can't read test.vcf as vcf or fasta. Please check input. Exit...
This error, as it outputs, is caused by the genome_fasta parameter in the config.yaml file. Make sure you have the correct path to the input genome fasta.