Prediction and visualization of on- and off-targets for CRISPR

python -m pavooc.pipeline

runs everything required to get you started. Additionally there is a docker-environment which should get you started with

docker-compose up

• All sequences are saved in gene-relative direction ('-'-strands are reverse-complemented).
• Ensembl gene IDs are used
• Exon are saved with paddings such that a sgRNA search can find all guides that would cut inside the exon. Forward strand: (16padding)(exon)(6padding), Backward strand: (6padding)(exon)(16padding)

What follows is a list of used datasets

http://hgdownload.soe.ucsc.edu/goldenPath/hg19/chromosomes/

Preprocessed Pfam data which maps protein domains to ucsc coordinates.

ftp://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/ucscGenePfam.{sql,txt.gz}

http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/refGene.txt.gz bin smallint(5) unsigned NOT NULL, name varchar(255) NOT NULL, chrom varchar(255) NOT NULL, strand char(1) NOT NULL, txStart int(10) unsigned NOT NULL, txEnd int(10) unsigned NOT NULL, cdsStart int(10) unsigned NOT NULL, cdsEnd int(10) unsigned NOT NULL, exonCount int(10) unsigned NOT NULL, exonStarts longblob NOT NULL, exonEnds longblob NOT NULL, score int(11) default NULL, name2 varchar(255) NOT NULL, cdsStartStat enum('none','unk','incmpl','cmpl') NOT NULL, cdsEndStat enum('none','unk','incmpl','cmpl') NOT NULL, exonFrames longblob NOT NULL,

http://www.uniprot.org/downloads

https://data.broadinstitute.org/ccle_legacy_data/dna_copy_number/CCLE_copynumber_byGene_2013-12-03.txt.gz