Desktop Genetics

Figures & data

Figure 1.  Machine learning fuels Desktop Genetics.

Desktop Genetics uses data from CRISPR experiments and literature to fuel our cognitive machine learning algorithms. In concert with the moon shot goals of personal genomics initiatives, this artificial intelligence system will efficiently design CRISPR therapeutics tailored to the needs of individual patients.

Table 1.  Desktop Genetics CRISPR library design parameters.

Score	Purpose
Doench (2014)/(2016)	Predicted on-target score
Chari (2015)	Predicted on-target score
Xu (2015)	Predicted on-target score
Hsu (2013)	Predicted on-target score
Percent peptide score	Target location in coding DNA sequence
RGEN selection	Cas9 orthologs, Cpf1 with varying PAM sequences

A snapshot of only a few of the scores and other thresholds used by Desktop Genetics to design CRISPR guide RNA libraries. These literature-based parameters address some of the fundamental concerns facing the CRISPR field.

PAM: Protospacer adjacent motif; RGEN: RNA-guided endonuclease.