Abstract
BackgroundThe latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using sequence-based features and traditional machine learning that can compete with deep learning models.ResultsIn this paper, we present CRISPRpred(SEQ), a method for sgRNA on-target activity prediction that leverages only traditional machine learning techniques and hand-crafted features extracted from sgRNA sequences. We compare the results of CRISPRpred(SEQ) with that of DeepCRISPR, the current state-of-the-art, which uses a deep learning pipeline. Despite using only traditional machine learning methods, we have been able to beat DeepCRISPR for the three out of four cell lines in the benchmark dataset convincingly (2.174%, 6.905% and 8.119% improvement for the three cell lines).ConclusionCRISPRpred(SEQ) has been able to convincingly beat DeepCRISPR in 3 out of 4 cell lines. We believe that by exploring further, one can design better features only using the sgRNA sequences and can come up with a better method leveraging only traditional machine learning algorithms that can fully beat the deep learning models.
Highlights
The latest works on Clustered regularly inter-spaced palindromic repeats (CRISPR) genome editing tools mainly employs deep learning techniques
CRISPRpred(SEQ) has improved upon the results of DeepCRISPR by 2.174%, 6.905% and 8.119% for the cells HCT116, HeLa and HL60 respectively
The results were compared with DeepCRISPR [21], single guide RNA (sgRNA) Designer [10], SSC [27], CHOP-CHOP [17], CRISPR MultiTargeter [28], E-CRISP [15], sgRNA Scorer [29], CasDesigner [30] and WU-CRISPR [18] (Fig. 2)
Summary
The latest works on CRISPR genome editing tools mainly employs deep learning techniques. Deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using sequence-based features and traditional machine learning that can compete with deep learning models. One of the more widely used genome editing technologies is CRISPR-Cas (Clustered Regularly Inter-spaced Short Palindromic Repeats-CRISPRassociated protein 9). CRISPR-Cas is preferred over other technologies because of its higher degree of flexibility and accuracy in cutting and pasting genes. It is more cost-efficient than other methods. It allows removing more than one gene at a time. By using CRISPR-Cas, we are able to manipulate multiple genes in plant and
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