TECHNOLOGY
Bioinformatics
Integrated bioinformatics for high-confidence break nomination
Gene editing off-target mapping routinely generates 100s of nominated sites per experiment. The technical challenge is no longer simply detection, it’s discrimination of what’s critical.
The results? Weeks to months of analysis, fragmented insight and unclear next steps. As programs move toward IND-enabling studies, these limitations become critical.
Most current analysis approaches are:
• Loosely coupled to wet-lab methods
• Built as part of academic projects and are infrequently maintained
• Lack of actionable insights
• Dependent on specialist bioinformatics expertise
• Computationally intensive requiring complex local infrastructure
• Focused on data output rather than decision prioritization
Purpose built for INDUCE-seq®
INDUCE-seq® Analysis is the dedicated bioinformatics engine developed alongside the INDUCE-seq® assay. Together they form an end-to-end architecture designed specifically for break mapping at scale, from read structure through to statistical nomination.
Tight coupling between assay chemistry and analytical framework enables higher-confidence interpretation of genome-wide breaks.
What this enables
Interactive reports for rapid interpretation
Easy to understand yet detailed graphical outputs
No demand for specialist bioinformatics team
No local infrastructure required
Platform capabilities
Fully integrated and
user-friendly
On-demand cloud platform
Typical analysis time< 2 hours
Decision-ready outputs
Core analytical workflow
Precise break localization
INDUCE-seq® library reads map DNA breaks to exact genomic locations.
• FASTQ reads undergo QC and align to a reference genome
• Breaks are resolved at base-level precision
• Genome-wide break counts comparable across sites
Produces a genome-wide break frequency map.
Normalized analysis
Break sites are compared directly to matched controls.
• Sites are paired with equivalent control positions
• Treated:control ratios are calculated
• Background signal is filtered from endogenous breaks
This increases confidence in identifying true treatment-induced events.
Break site annotation
Each detected break site is annotated to provide biological context.
• Gene and repeat region overlap
• Reproducibility across biological replicates
• Proximity to guide-like sequences
This enables clear interpretation of break behavior.
Probabilistic nomination
Statistical modeling is applied to prioritize relevant sites.
• Distinguishes treatment-induced events from background
• Accounts for variation in high-background regions
• Prioritizes high-confidence candidate off-target sites
This supports focused downstream validation and risk assessment.
Report with confidence
Table 1: Showing nominated putative on- and off-target break sites resulting from treatment with CRISPR-Cas9 and an AAVS1 guide RNA. The rationale column demonstrates the criteria for nomination and the other columns include additional annotations that can be found in the full interactive report.
Figure 1: Showing the distribution and counts of breaks at the on-target site for an editing experiment using CRISPR-Cas9 and an EMX1 guide RNA. 1 base pair 5' overhang break structure can be observed.
Figure 2: Showing the distribution and counts of breaks at the on-target site for an editing experiment using CRISPR-Cas12a and an HPRT1 guide RNA. A broad 5' overhang break cutting pattern can be observed.