NDLI: CRISPRpred(SEQ): a sequence-based method for sgRNA on target activity prediction using traditional machine learning

Content Provider	Springer Nature : BioMed Central
Author	Muhammad Rafid, Ali Haisam Toufikuzzaman, Md. Rahman, Mohammad Saifur Rahman, M. Sohel
Abstract	Background The 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. Results In 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). Conclusion CRISPRpred(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.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-020-3531-9.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-020-3531-9
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2020-06-01
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms CRISPR sgRNA Machine learning Deep learning Cas9 Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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