DeepCRISPR-Typer: Accurate CRISPR-Cas Identification and Subtyping in Metagenomes via Integrated Deep Learning
TL;DR
DeepCRISPR-Typer is presented, a comprehensive computational framework integrating a large protein language model (TEMC-Cas), a deep sequence feature extractor (CRISPR-RepTyper), and an adaptive targeted HMM profiling strategy that significantly reduces computational overhead by dynamically invoking subtype-specific HMM subsets.
Abstract
Accurate identification and classification of CRISPR-Cas systems are crucial for understanding microbial immune mechanisms and developing novel genome-editing tools. However, traditional homology-based mining methods face severe computational bottlenecks and assembly fragmentation challenges when processing massive metagenomic data. Here, we present DeepCRISPR-Typer, a comprehensive computational framework integrating a large protein language model (TEMC-Cas), a deep sequence feature extractor (CRISPR-RepTyper), and an adaptive targeted HMM profiling strategy. DeepCRISPR-Typer integrates array and Cas evidence and significantly reduces computational overhead by dynamically invoking subtype-specific HMM subsets. In metagenomic dataset evaluations, DeepCRISPR-Typer achieved a classification accuracy of 94.26% and demonstrated a significant acceleration of approximately 1 orders of magnitude compared to existing mainstream tools. This research provides a robust and scalable engine for metagenome-scale CRISPR system discovery, significantly expanding the mining toolbox for genome engineering applications.