Protospacer Workbench: CRISPR-Cas9 design
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Current features (beta)
  • Works on common operating systems. Executable binaries available for Mac OS X and Windows.
  • Tabulates Cas9 (NGG) target sites for any small to medium sized genome of interest.
  • Pre-calculated databases for several organisms.
  • Browse a genome's repertoire of Cas9 target-sites quickly.
  • Sub-select target sites based on distance from a point (e.g. the stop coordinate of a gene).
  • Sub-select target sites between two genomic coordinates.
  • Sub-select target sites within a gene.
  • Filter targets by the type of PAM motif (NGG, or NAG).
  • Filter targets by annotation: 'genic', 'intergenic', 'exonic', 'intronic', and 'UTR'.
  • Filter targets by their degree of uniqueness.
  • Sort targets by any information, in descending or ascending order.
  • Thoroughly compare a guide-RNA sequence to all potential sites in a genome.
  • Rank off-target comparisons by the number of mismatches or a likelihood score.
  • Likelihood scores are calculated by an adapted version of this algorithm, developed from results by Hsu et al, Nature Biotechnology 2013.
  • Identify all NGG/NAG sites in custom sequences less than 100,000 nt long.
  • Connect to and control the Integrative Genomics Viewer (IGV) for even more powerful analyses (observe local annotations, visualise target-sites in the context of any high-throughput sequencing data, view amino acid translations useful for the shield technique mentioned here). This was made possible from code developed by Brent Pedersen and made available on github. 
  • Progress bar during CPU intensive calculations.
  • Optimized data model (HDF5) and searching algorithms.
  • Fast indexing of large genomes.
  • Easily create your own database for any small to medium sized genome (already possible, just not easy).
  • Share your custom database with the community and/or collaborators.
  • GFF parser for the PDC - Protospacer Database Creator.
Medium to long term milestones (version 1.0)
  • Mark targets of interest and rank them according to predicted off-target activity.
  • Scripting access through Python version 2.x for large or complicated tasks.
  • Database summary tab and capability to store meta-information.
  • System requirements (database dependant).
  • Named searches for easier history tracking.
  • Save and load workflows / history.
  • Annotation and ID search engine allowing multiple nomenclatures (currently, genes are only allowed one ID).
  • Anchored search for optimized gRNA-pair design.
  • Plotting and report generation.
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