score_cas9ranges.Rd
Score cas9ranges using ruleset1 (Doench 2014) or ruleset2 (Doench 2016)
score_contextseqs(contextseqs, ruleset = 1, verbose = TRUE, python = NULL, virtualenv = NULL, condaenv = NULL) score_cas9ranges(cas9ranges, ruleset = 1, verbose = TRUE, python = NULL, virtualenv = NULL, condaenv = NULL)
contextseqs | character vector with 4-23-3 contextseqs |
---|---|
ruleset | 1 (default) or 2 (only if python module github/MicrosoftResearch/azimuth is installed) |
verbose | logical(1) |
python | NULL (ruleset=1) or path to a python binary (ruleset=2). See details. |
virtualenv | NULL (ruleset=1) or directory containing python virtualenv (ruleset=2). See details. |
condaenv | NULL (ruleset=1) or name of condaenv (ruleset=2). See details. |
cas9ranges | GenomicRanges::GRanges |
numeric vector
ruleset1 is readily available. ruleset2 is accessible after installing the python module [azimuth](https://github.com/MicrosoftResearch/Azimuth), and specifying a value for either 'python' (python binary path), 'virtualenv' (python virtual environment dir) or 'condaenv' (python conda environment).
Doench 2014, Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nature Biotechnology, doi: 10.1038/nbt.3026
Doench 2016, Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology, doi: 10.1038/nbt.3437
# Get cas9ranges require(magrittr) bedfile <- system.file('extdata/SRF_sites.bed', package = 'multicrispr') bsgenome <- BSgenome.Mmusculus.UCSC.mm10::Mmusculus cas9ranges <- read_bed(bedfile, bsgenome) %>% flank_fourways() %>% find_cas9ranges()#>#>#>#>#>#>#>#>#>#>#>#>#># Score cas9ranges[1:3] %>% score_cas9ranges()#>#> [1] 0.01708623 0.72298446 0.06796563#>#> [1] 0.01708623 0.72298446 0.06796563