Package: easy-prime
Easy-Prime: an optimized prime editor gRNA design tool based on gradient boosting treesEasy-Prime provides optimized pegRNA and ngRNA combinations for efficient PE design.SummaryPE 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.InstallationThe 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 -hSeehttps://easy-prime.readthedocs.io/en/latest/content/Installation.htmlfor step-by-step installation screenshots.Usage## 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 folderEasy-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 application.pyEasy-Prime on AWSPlease use this URL:http://easy-prime.cc/TutorialInputvcf input exampleVCF 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	Tfasta input exampleTo specify reference and alternative allele, you need two fasta sequences;_refis a keyword that will be recognized as the reference allele and_altis a keyword for target mutations.>rs2251964_ref
GTTACCAAAGCAAATGACATCTTGTGAAAGGGGAGGTCTGAAAAAAAAAAACAAGTGGGTGGGTTTTTTCAAAGTAGGCCACCGGGCCTGAGATGACCAGAATTCAAATTAGGATGACAGTGTAGTAGGGGAAGCAACCAGAATCGGACCT
>rs2251964_alt
GTTACCAAAGCAAATGACATCTTGTGAAAGGGGAGGTCTGAAAAAAAAAAACAAGTGGGTGGGTTTTTTCAAAGTAGGCCACCGGGCCTGAGATAACCAGAATTCAAATTAGGATGACAGTGTAGTAGGGGAAGCAACCAGAATCGGACCTThe PrimeDesign format input is only supported in the Easy-Prime web server.ParametersGenome: only support hg19 for now.ResultsThe web output contain two parts:pegRNA tableIn 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 visualizationBy default, the top prediction will be shown automatically.InputA vcf file containing at least 5 columns. Seetest/test.vcffor examples.Searching parameters for PE designDefault values are shown in the following yaml files.genome_fasta:/path/to/genome.fascaffold:GTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCdebug:0n_jobs:4min_PBS_length:8max_PBS_length:17min_RTT_length:10max_RTT_length:25min_distance_RTT5:3max_ngRNA_distance:100max_target_to_sgRNA:10sgRNA_length:20offset:-3PAM:NGGOutputThe output folder contains:topX_pegRNAs.csvrawX_pegRNAs.csv.gzX_p_pegRNAs.csv.gzsummary.csvThe top candidates are provided intopX_pegRNAs.csv. This is a rawX format file.rawX formatX 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 thetypecould be sgRNA, PBS, RTT, or ngRNA. Since for one PE design, it has to have these 4 components, which means that for one uniquesample_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.BothtopX_pegRNAs.csvandrawX_pegRNAs.csv.gzuse this format.X formatX format is the numeric representation of rawX.X_pformat appends the predicted efficiency to the last column of X.Main resultsThe main results, which is the top condidates, is provided intopX_pegRNAs.csv.PE design visualizationUsers can visualize the predicted combinations using:easy_prime_vis-ftopX_pegRNAs.csv-s/path/to/genome_fasta.faThis will output pdf files to a result dir.