CRISPR & Gene Editing Design
Synthego CRISPR Engineering Platform
by Synthego Corporation
End-to-end CRISPR engineering platform from guide RNA design through edited cell validation
Category
CRISPR & Gene Editing Design
Founded
2012
Headquarters
Redwood City, CA, USA
Overview
Synthego is a genome engineering company that provides an integrated platform spanning guide RNA design, synthetic guide RNA manufacturing, CRISPR reagent kits, and computational tools for experimental design and data analysis. The Synthego Platform includes the Inference of CRISPR Edits (ICE) analysis tool for quantifying editing outcomes from Sanger sequencing data, genome editing cell line services for generating knockout and knockin cell lines, and high-throughput synthetic RNA production at GMP-compatible quality standards for therapeutic applications. Biotech companies, pharmaceutical R&D organizations, and academic core facilities use Synthego to accelerate CRISPR experiments by outsourcing guide RNA synthesis and obtaining pre-validated, high-performance reagents rather than synthesizing guide RNAs in-house. The ICE analysis tool has become an industry standard for quantifying CRISPR editing efficiency from sequencing traces, cited in thousands of publications and used by researchers at most major research institutions. Synthego's business model integrates software and reagent services: researchers design guide RNAs using Synthego's free online tools, then order synthetic sgRNAs or crRNA:tracrRNA duplexes with rapid turnaround and guaranteed performance. The company's expansion into cell engineering services — where Synthego handles the entire process of generating edited cell lines — addresses the growing demand for validated research and therapeutic cell substrates. ICE software is freely available online and drives significant brand awareness among bench scientists.
Key Features
Multiplexed Editing Design
Design multi-guide strategies for simultaneous editing at multiple genomic loci.
HDR Template Design
Optimized homology-directed repair template design for precise sequence insertions.
Editing Efficiency Prediction
ML models predict editing efficiency for specific guide-target combinations across cell types.
Regulatory Documentation
Automated generation of regulatory-ready documentation packages for gene therapy IND applications.
Collaborative Project Management
Cloud-based tools for team collaboration on gene editing projects with version control.
Pros & Cons
Pros
- +Comprehensive off-target prediction algorithms evaluate billions of potential cleavage sites
- +AI-optimized guide RNA design maximizes on-target efficiency while minimizing off-target effects
- +Regulatory-ready documentation packages support IND applications for gene therapy programs
- +Integration with delivery system optimization (viral vectors, LNPs, electroporation)
- +Pre-validated guide libraries for common model organisms accelerate experimental design
- +Cloud-based design tools enable collaborative gene editing project management across teams
- +Multi-editor support covers CRISPR-Cas9, Cas12, base editing, and prime editing systems
Cons
- −Intellectual property landscape for CRISPR technology is complex with multiple competing patents
- −Delivery challenges limit efficient CRISPR component delivery to many tissue types in vivo
- −Off-target editing effects remain a safety concern especially for therapeutic applications
- −Regulatory pathways for gene-edited therapies are evolving and differ across jurisdictions
Use Cases
Research Workflow Optimization
AI-powered optimization of research workflows to accelerate discovery timelines and improve reproducibility.
Data Analysis & Insights
Machine learning analysis of complex biological datasets to extract actionable insights and identify patterns.
Collaboration & Knowledge Management
Platform-enabled collaboration across distributed research teams with integrated data sharing and knowledge capture.