{"id":"CU27053","slug":"deep-learning-platform-for--CU27053","source":{"id":"CU27053","dataset":"techtransfer","title":"Deep learning platform for predicting CRISPR-Cas13 on-target and off-target activity","description_":"<p>This technology is a deep-learning guide-design platform that identifies CRISPR-Cas13 guide RNAs capable of achieving desired levels of gene knockdown for more precise RNA targeting and gene regulation.</p>\r\r<h2>Unmet Need: Reliable design of Cas13 guides with predictable activity and specificity</h2>\r\r<p>Current CRISPR-Cas13 guide design approaches are primarily optimized to identify highly active, perfectly matched guide RNAs. However, they have limited ability to predict the effects of guide-target mismatches, assess potential off-target activity, or select guides that produce intermediate levels of RNA knockdown. These limitations make it difficult to precisely control transcript abundance when maximal gene suppression is not desired.</p>\r\r<h2>The Technology: Predictive Cas13 guide design for tunable gene silencing and off-target assessment</h2>\r\r<p>This technology is a deep-learning framework to predict CRISPR-Cas13 guide RNA activity based on guide-target sequence and RNA-context features. By modeling the effects of sequence mismatches and other target-specific characteristics, the platform can support selection of guides with high activity, assessment of potential off-target activity, or predetermined intermediate levels of RNA knockdown. This enables guide selection based on the desired degree of gene suppression rather than maximizing activity alone.</p>\r\r<p>The technology has been validated across multiple Cas13d screens, target genes, and cell lines, including experimental demonstration of tunable gene knockdown using mismatch-containing guides.</p>\r\r<h2>Applications:</h2>\r\r<ul>\r<li>CRISPR-Cas13 guide RNA design for RNA-targeting therapeutics</li>\r<li>Functional genomics and target validation</li>\r<li>Gene expression and dosage studies</li>\r<li>Synthetic biology and cell engineering</li>\r<li>Development of gene-specific knockdown research tools</li>\r</ul>\r\r<h2>Advantages:</h2>\r\r<ul>\r<li>Enables tunable gene expression beyond maximal knockdown</li>\r<li>Supports prediction of mismatch-dependent and potential off-target activity</li>\r<li>Enables guide selection for predetermined levels of RNA suppression</li>\r<li>Accounts for RNA-context features beyond sequence complementarity alone</li>\r<li>Supports both highly active and partially active guide selection</li>\r</ul>\r\r<h2>Lead Inventor:</h2>\r\r<p><a href=\"https://www.engineering.columbia.edu/faculty-staff/directory/david-knowles\">David A. Knowles, Ph.D.</a></p>\r\r<h2>Patent Information:</h2>\r\r<p>Patent Pending</p>\r\r<h2>Related Publications:</h2>\r\r<ul>\r<li><p><a href=\"https://pubmed.ncbi.nlm.nih.gov/40730819/\">Schertzer MD, Stirn A, Isaev K, et al. “Cas13d-mediated isoform-specific RNA knockdown with a unified computational and experimental toolbox.” Nature Communications. 2025 Jul 29; 16: 6948.</a></p></li>\r<li><p><a href=\"https://pubmed.ncbi.nlm.nih.gov/37400521/\">Wessels HH, Stirn A, Méndez-Mancilla A, Kim EJ, Hart SK, Knowles DA, Sanjana NE. “Prediction of on-target and off-target activity of CRISPR-Cas13d guide RNAs using deep learning.” Nature Biotechnology. 2024 Apr; 42(4): 628-637.</a></p></li>\r</ul>\r\r<h2>Tech Ventures Reference:</h2>\r\r<ul>\r<li><p>IR CU27053</p></li>\r<li><p>Licensing Contact: <a href=\"mailto:techtransfer@columbia.edu\">Joan Martinez</a> </p></li>\r</ul>\r","tags":["CRISPR","Deep learning","Functional genomics","Gene expression","Synthetic biology"],"file_number":"CU27053","collections":[],"meta_description":"Deep-learning Cas13 guide design predicts on/off-target activity and tunable knockdown for precise RNA targeting.","apriori_judge_output":"{\"scores\":{\"novelty\":4.0,\"potential_impact\":4.0,\"readiness\":3.0,\"scalability\":4.0,\"timeliness\":4.0},\"weighted_score\":3.75,\"risks\":[\"Limited public data on Cas13 off-targets could affect generalizability\",\"Regulatory considerations for RNA-targeting therapies not addressed\",\"Potential competition from protein-level or alternative RNA-targeting methods\"],\"one_sentence_take\":\"Strong novelty and impact with solid readiness and scalability, but regulatory and data limitations warrant caution and mitigation.\"}","inventors":["David A. Knowles Ph.D."],"manager":"Joan Martinez","depts":["Computer Science"],"divs":["Fu Foundation School of Engineering and Applied Science (SEAS)"],"date_released":"2026-10-02"},"highlight":{},"matched_queries":null,"score":0.0}