{"product_id":"borzoi-ai-skill-for-genome-wide-functional-track-prediction","title":"Borzoi: AI Skill for Genome-Wide Functional Track Prediction","description":"\u003ch3\u003eProduct Overview\u003c\/h3\u003e\n\u003cp\u003eBorzoi is an AI skill designed for DNA sequence analysis, specifically focused on predicting genome-wide functional tracks such as RNA-seq, CAGE, DNase, and ChIP. This skill supports developers working with AI coding agents like Claude Code, Cursor, and Codex by providing a mechanism to score and evaluate the regulatory effects of DNA variants.\u003c\/p\u003e\n\n\u003ch3\u003eWhat this skill does\u003c\/h3\u003e\n\u003cul\u003e\n  \u003cli\u003ePredicts functional tracks from DNA sequences using a pre-trained model.\u003c\/li\u003e\n  \u003cli\u003eAllows scoring of regulatory effects on expression and accessibility by analyzing variants.\u003c\/li\u003e\n  \u003cli\u003eGenerates predicted coverage tracks for specific genomic loci.\u003c\/li\u003e\n  \u003cli\u003eAssists in prioritizing non-coding variants based on predicted track differences.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eWho it is for\u003c\/h3\u003e\n\u003cp\u003eThis skill is ideal for bioinformatics developers and research teams who need to incorporate DNA sequence analysis into their workflows. Teams using AI coding platforms like Claude Code, Cursor, or Codex will significantly benefit from the automation of functional track predictions.\u003c\/p\u003e\n\n\u003ch3\u003eUse cases\u003c\/h3\u003e\n\u003cul\u003e\n  \u003cli\u003eEvaluating the potential impact of genomic variants in regulatory regions on gene expression.\u003c\/li\u003e\n  \u003cli\u003eGenerating coverage predictions for distinct loci to support research and analysis in genomics.\u003c\/li\u003e\n  \u003cli\u003ePrioritizing and studying non-coding DNA variants by observing changes in predicted functional tracks.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eTechnical details\u003c\/h3\u003e\n\u003cul\u003e\n  \u003cli\u003eRequires Python 3.10 or newer, with a preference for version 3.11.\u003c\/li\u003e\n  \u003cli\u003eCompatible with CUDA 12.1 or newer, requiring at least 24 GB of GPU VRAM for optimal performance.\u003c\/li\u003e\n  \u003cli\u003eLeverages a pre-trained model available on Hugging Face, requiring either pre-cached weights or egress to huggingface.co.\u003c\/li\u003e\n  \u003cli\u003eOutputs predictions in the format of a `(B, T, L)` tensor consisting of tracks, length bins, and batch size that reflect assay and biosample metadata.\u003c\/li\u003e\n  \u003cli\u003eUsage example: Using a DNA input, the Borzoi model will produce binned predictions across 7,611 human tracks, with optional mouse track analysis.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eThis AI skill allows genomic analysis without making claims about enhancing business outcomes or guaranteeing specific results, focusing solely on its specialized function of genomic track prediction.\u003c\/p\u003e\n\u003c!-- mcpcart:static-blocks:start --\u003e\n\u003chr\u003e\n\u003ch3\u003eSource \u0026amp; Licence\u003c\/h3\u003e\n\u003cp\u003eThis package is built on open-source work published by \u003cstrong\u003eaipoch\u003c\/strong\u003e (\u003ca href=\"https:\/\/github.com\/aipoch\/open-science\" rel=\"nofollow noopener\" target=\"_blank\"\u003eaipoch\/open-science\u003c\/a\u003e) and distributed under \u003cstrong\u003eApache-2.0\u003c\/strong\u003e. The original licence text and copyright notice are included in your download.\u003c\/p\u003e\n\u003cp\u003ePersonal and commercial use, modification and redistribution are permitted under the Apache License 2.0, which also includes an express patent grant. Attribution and any NOTICE file must be retained.\u003c\/p\u003e\n\u003cp\u003eYour purchase covers curation, licence verification, packaging, documentation and instant delivery. It does not grant exclusive rights to the underlying open-source code, which remains available under its original licence.\u003c\/p\u003e\n\u003ch3\u003eDelivery \u0026amp; Support\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eDelivery:\u003c\/strong\u003e instant — a secure download link is emailed to you as soon as payment is confirmed.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFormat:\u003c\/strong\u003e ZIP archive containing the skill files, documentation and the original licence.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSupport:\u003c\/strong\u003e \u003ca href=\"mailto:support@mcpcart.com\"\u003esupport@mcpcart.com\u003c\/a\u003e — we aim to reply within 2 business days.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUpdates:\u003c\/strong\u003e updates are included only where stated on this page.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch3\u003eRefunds\u003c\/h3\u003e\n\u003cp\u003eThis is a digital product delivered immediately after purchase. By completing your order you request immediate delivery and acknowledge that, once the download has been accessed, the statutory right to cancel no longer applies to the extent permitted by law. Refund requests are handled in accordance with our published Refund Policy.\u003c\/p\u003e\n\u003cp style=\"font-size:0.85em;color:#666;\"\u003eClaude, Codex, Gemini and Cursor are trademarks of their respective owners. MCP Cart is an independent marketplace and is not affiliated with, endorsed by, or sponsored by any of them. Compatibility references describe interoperability only.\u003c\/p\u003e\n\u003c!-- mcpcart:static-blocks:end --\u003e","brand":"MCP Cart","offers":[{"title":"Default Title","offer_id":53014680502583,"sku":"MCP-AIPOCH-OPEN-SCIENCE-BORZOI","price":3.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0981\/3950\/4951\/files\/0XvbBwQdoQoG782X-G054_56b9a9eaaa3947108f535b497266acfb.jpg?v=1789898800","url":"https:\/\/mcpcart.com\/products\/borzoi-ai-skill-for-genome-wide-functional-track-prediction","provider":"SPF PRO","version":"1.0","type":"link"}