Fast and Efficient Analysis of CRISPR-Cas9 Data Using GPU-Accelerated ML

EasyChair Preprint 14051

Abstract

The CRISPR-Cas9 system has revolutionized genetic research, enabling precise gene editing with wide-ranging applications in medicine, agriculture, and biotechnology. However, the rapid and accurate analysis of CRISPR-Cas9 data presents significant computational challenges due to the large volume and complexity of the data generated. Traditional analytical methods are often slow and computationally intensive, hindering timely insights and applications. This paper explores the implementation of GPU-accelerated machine learning (ML) techniques to enhance the efficiency and speed of CRISPR-Cas9 data analysis. By leveraging the parallel processing capabilities of GPUs, our approach significantly reduces the time required for data processing and increases the accuracy of detecting and quantifying gene edits. We demonstrate the effectiveness of various GPU-accelerated ML models in tasks such as off-target effect prediction, efficiency prediction of guide RNAs, and high-throughput screening of CRISPR libraries. Our results show a remarkable improvement in performance compared to CPU-based methods, highlighting the potential of GPU-accelerated ML to transform CRISPR-Cas9 data analysis. This advancement not only facilitates faster research cycles but also opens new possibilities for real-time applications in gene editing and synthetic biology.

Keyphrases: Accelerated sequence analysis, Genomic data processing, Machine learning in computational biology

@booklet{EasyChair:14051,
  author    = {Abi Cit},
  title     = {Fast and Efficient Analysis of CRISPR-Cas9 Data Using GPU-Accelerated ML},
  howpublished = {EasyChair Preprint 14051},
  year      = {EasyChair, 2024}}