BoostMEC: predicting CRISPR-Cas9 cleavage efficiency through boosting models
Open accessAbstract
Background: In the CRISPR-Cas9 system, the efficiency of genetic modifications has been found to vary depending on the single guide RNA (sgRNA) used. A variety of sgRNA properties have been found to be predictive of CRISPR cleavage efficiency, including the position-specific sequence composition of sgRNAs, global sgRNA sequence properties, and thermodynamic features. While prevalent existing deep learning-based approaches provide competitive prediction accuracy, a more interpretable model is desirable to help understand how different features may contribute to CRISPR-Cas9 cleavage efficiency. Results: We propose a gradient boosting approach, utilizing LightGBM to develop an integrated tool, BoostMEC (Boosting Model for Efficient CRISPR), for the prediction of wild-type CRISPR-Cas9 editing efficiency. We benchmark BoostMEC against 10 popular models on 13 external datasets and show its competitive performance. Conclusions: BoostMEC can provide state-of-the-art predictions of CRISPR-Cas9 cleavage efficiency for sgRNA design and selection. Relying on direct and derived sequence features of sgRNA sequences and based on conventional machine learning, BoostMEC maintains an advantage over other state-of-the-art CRISPR efficiency prediction models that are based on deep learning through its ability to produce more interpretable feature insights and predictions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The BoostMEC model improves off-target prediction by considering binding energy, cleavage efficiency and other biophysical factors, reducing false positives and enhancing off-target site identification.
“The BoostMEC model further improves prediction by considering binding energy, cleavage efficiency, and other biophysical factors, reducing false positives and enhancing off-target site identification.”
From Hybrid Neural Networks for Off-Target Prediction in Gene Editing: Integrating Sequence and Biophysical Features (2025) · cites this paper as [6]
The Methods (Feature construction) describe thermodynamic features used to predict cleavage efficiency, so that part of the claim is supported. The Discussion explicitly says that gRNA–DNA binding energy was not used in BoostMEC. The paper describes cleavage-efficiency prediction, not reduced false positives or improved off-target site identification. (LLM, conf=0.90)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity
Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012) · Science · doi:10.1126/science.1225829 · Cited by 17,116 · see citing works
Multiplex Genome Engineering Using CRISPR/Cas Systems
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A., et al. (2013) · Science · doi:10.1126/science.1231143 · Cited by 15,339 · see citing works
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512 · see citing works
RNA-Guided Human Genome Engineering via Cas9
Mali, P., Yang, L., Esvelt, K. M., Aach, J., Guell, M., DiCarlo, J. E., Norville, J. E., & Church, G. M. (2013) · Science · doi:10.1126/science.1232033 · Cited by 9,210 · see citing works
Lorenz, R., Bernhart, S. H., Höner zu Siederdissen, C., Tafer, H., Flamm, C., Stadler, P. F., & Hofacker, I. L. (2011) · Algorithms for Molecular Biology · doi:10.1186/1748-7188-6-26 · Cited by 5,098 · see citing works
Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9
Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3437 · Cited by 4,637 · see citing works
Genetic screens in human cells using the CRISPR-Cas9 system
Wang, T., Wei, J. J., Sabatini, D. M., & Lander, E. S. (2014) · Science · doi:10.1126/science.1246981 · Cited by 2,856 · see citing works
HIV-1 Integration in the Human Genome Favors Active Genes and Local Hotspots
Schröder, A. R. W., Shinn, P., Chen, H., Berry, C., Ecker, J. R., & Bushman, F. (2002) · Cell · doi:10.1016/s0092-8674(02)00864-4 · Cited by 1,832 · see citing works
Haeussler, M., Schönig, K., Eckert, H., Eschstruth, A., Mianné, J., Renaud, J.-B., Schneider-Maunoury, S., Shkumatava, A., Teboul, L., Kent, J., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1012-2 · Cited by 1,756 · see citing works
Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation
Doench, J. G., Hartenian, E., Graham, D. B., Tothova, Z., Hegde, M., Smith, I., Sullender, M., Ebert, B. L., Xavier, R. J., & Root, D. E. (2014) · Nature Biotechnology · doi:10.1038/nbt.3026 · Cited by 1,617 · see citing works
High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities.
Hart, T., Chandrashekhar, M., Aregger, M., Steinhart, Z., Brown, K. R., MacLeod, G., Mis, M., Zimmermann, M., Fradet-Turcotte, A., Sun, S., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.015 · Cited by 1,571 · see citing works
CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo
Moreno-Mateos, M. A., Vejnar, C. E., Beaudoin, J.-D., Fernandez, J. P., Mis, E. K., Khokha, M. K., & Giraldez, A. J. (2015) · Nature Methods · doi:10.1038/nmeth.3543 · Cited by 1,290 · see citing works
Retroviral DNA Integration: ASLV, HIV, and MLV Show Distinct Target Site Preferences
Mitchell, R., Beitzel, B., Schroder, A. R. W., Shinn, P., Chen, H., Berry, C., Ecker, J., & Bushman, F. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020234 · Cited by 1,024 · see citing works
Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian cells
Wu, X., Scott, D. A., Kriz, A. J., Chiu, A. C., Hsu, P. D., Dadon, D. B., Cheng, A. W., Trevino, A. E., Konermann, S., Chen, S., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2889 · Cited by 944 · see citing works
Gagnon, J. A., Valen, E., Thyme, S. B., Huang, P., Ahkmetova, L., Pauli, A., Montague, T. G., Zimmerman, S., Richter, C., & Schier, A. F. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098186 · Cited by 861 · see citing works
DeepCRISPR: Optimized CRISPR guide RNA design by deep learning
Chuai, G., Ma, H., Yan, J., Chen, M., Hong, N., Xue, D., Zhou, C., Zhu, C., Chen, K., Duan, B., et al. (2018) · Genome biology · doi:10.1186/s13059-018-1459-4 · Cited by 641 · see citing works
Vellido, A. (2020) · Neural Computing and Applications · doi:10.1007/s00521-019-04051-w · Cited by 635 · see citing works
Sequence determinants of improved CRISPR sgRNA design
Xu, H., Xiao, T., Chen, C.-H., Li, W., Meyer, C. A., Wu, Q., Wu, D., Cong, L., Zhang, F., Liu, J. S., et al. (2015) · Genome Research · doi:10.1101/gr.191452.115 · Cited by 625 · see citing works
High-throughput gene targeting and phenotyping in zebrafish using CRISPR/Cas9Preprint
Varshney, G. K., Pei, W., LaFave, M. C., Idol, J., Xu, L., Gallardo, V. E., Carrington, B., Bishop, K. S., Jones, M. S., Li, M., et al. (2015) · Genome Research · doi:10.1101/gr.186379.114 · Cited by 510 · see citing works
Cornerstones of CRISPR–Cas in drug discovery and therapy
Fellmann, C., Gowen, B. G., Lin, P., Doudna, J. A., & Corn, J. E. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.238 · Cited by 442 · see citing works
Unraveling CRISPR-Cas9 genome engineering parameters via a library-on-library approach
Chari, R., Mali, P., Moosburner, M., & Church, G. M. (2015) · Nature Methods · doi:10.1038/nmeth.3473 · Cited by 394 · see citing works
In vivo high-throughput profiling of CRISPR-Cpf1 activity
Kim, H. K., Song, M., Lee, J., Menon, A. V., Jung, S., Kang, Y.-M., Choi, J. W., Woo, E., Koh, H. C., Nam, J.-W., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4104 · Cited by 383 · see citing works
WU-CRISPR: characteristics of functional guide RNAs for the CRISPR/Cas9 system
Wong, N. W., Liu, W., & Wang, X. (2015) · Genome biology · doi:10.1186/s13059-015-0784-0 · Cited by 374 · see citing works
Self-citationOptimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning
Wang, D., Zhang, C., Wang, B., Li, B., Wang, Q., Liu, D., Wang, H., Zhou, Y., Shi, L., Lan, F., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12281-8 · Cited by 331 · see citing works
A Genome-wide CRISPR Death Screen Identifies Genes Essential for Oxidative Phosphorylation
Arroyo, J. D., Jourdain, A. A., Calvo, S. E., Ballarano, C. A., Doench, J. G., Root, D. E., & Mootha, V. K. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.017 · Cited by 295 · see citing works
Kim, H. K., Kim, Y., Lee, S., Min, S., Bae, J. Y., Choi, J. W., Park, J., Jung, D., Yoon, S., & Kim, S. (2019) · Science Advances · doi:10.1126/sciadv.aax9249 · Cited by 235 · see citing works
Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning
Xiang, X., Corsi, G. I., Anthon, C., Qu, K., Pan, X., Liang, X., Han, P., Dong, Z., Liu, L., Zhong, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23576-0 · Cited by 209 · see citing works
Self-citationPrediction of CRISPR sgRNA Activity Using a Deep Convolutional Neural Network
Xue, L., Tang, B., Chen, W., & Luo, J. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00368 · Cited by 111 · see citing works
Correction of muscular dystrophies by CRISPR gene editing
Chemello, F., Bassel‐Duby, R., & Olson, E. N. (2020) · Journal of Clinical Investigation · doi:10.1172/jci136873 · Cited by 101 · see citing works
Rahman, M. K., & Rahman, M. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181943 · Cited by 79 · see citing works
CRISPR/Cas9 cleavage efficiency regression through boosting algorithms and Markov sequence profiling
Peng, H., Zheng, Y., Blumenstein, M., Tao, D., & Li, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty298 · Cited by 54 · see citing works
Kaur, K., Gupta, A., Rajput, A., & Kumar, M. (2016) · Scientific Reports · doi:10.1038/srep30870 · Cited by 49 · see citing works
Rafid, A. H. M., Toufikuzzaman, M., Rahman, M. S., & Rahman, M. S. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3531-9 · Cited by 47 · see citing works
Wang, J., Xiang, X., Bolund, L., Zhang, X., Cheng, L., & Luo, Y. (2019) · Journal of Molecular Cell Biology · doi:10.1093/jmcb/mjz116 · Cited by 40 · see citing works
Self-citationIn Silico Predictive Modeling of CRISPR/Cas9 guide efficiencyPreprint
Fusi, N., Smith, I. C. P., Doench, J. G., & Listgarten, J. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/021568 · Cited by 38 · see citing works
Translatable gene therapy for lung cancer using Crispr CAS9—an exploratory review
Nair, J., Nair, A., Veerappan, S., & Sen, D. (2019) · Cancer Gene Therapy · doi:10.1038/s41417-019-0116-8 · Cited by 36 · see citing works
CRISPRedict: a CRISPR-Cas9 web tool for interpretable efficiency predictions
Konstantakos, V., Nentidis, A., Krithara, A., & Παλιούρας, Γ. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac466 · Cited by 29 · see citing works
PINCER: improved CRISPR/Cas9 screening by efficient cleavage at conserved residues
Veeneman, B. A., Gao, Y., Grant, J., Fruhling, D., Ahn, J. H., Bosbach, B., Bienkowska, J. R., Follettie, M. T., Arndt, K. T., Myers, J. S., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa645 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [38]Lorenz R, Bernhart SH, Honer Zu Siederdissen C, Tafer H, Flamm C, Stadler PF, et al. ViennaRNA Package 20. Algorithms Mol Biol. 2011;6:26. PMC3319429 22115189
Cited by
Sorted by citations
Sherkatghanad, Z., Abdar, M., Charlier, J., & Makarenkov, V. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad131 · Cited by 117 · see citing works
Abbasi, A. F., Asim, M. N., & Dengel, A. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-024-06013-w · Cited by 58 · see citing works
Factors affecting the cleavage efficiency of the CRISPR-Cas9 system
Jung, W. J., Park, S.-J., Cha, S., & Kim, K. (2024) · Animal Cells and Systems · doi:10.1080/19768354.2024.2322054 · Cited by 39 · see citing works
Microbial production systems and optimization strategies of antimicrobial peptides: a review
Lou, M., Ji, S., Wu, R., Zhu, Y., Wu, J., & Zhang, J. (2025) · World Journal of Microbiology and Biotechnology · doi:10.1007/s11274-025-04278-x · Cited by 32 · see citing works
From Code to Life: The AI‐Driven Revolution in Genome Editing
Li, Z., Khan, W. U., Bai, G., Chao, D., Wang, J., Zhang, Y., Wang, C., Zhang, H., Wang, W., Luo, M., et al. (2025) · Advanced Science · doi:10.1002/advs.202417029 · Cited by 31 · see citing works
Self-citationLi, J., Wu, P., Cao, Z., Huang, G., Lu, Z., Yan, J., Zhang, H., Zhou, Y., Liu, R., Chen, H., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113765 · Cited by 26 · see citing works
An Overview and Comparative Analysis of CRISPR-SpCas9 gRNA Activity Prediction Tools
Yuan, H., Song, C., Xu, H., Sun, Y., Anthon, C., Bolund, L., Lin, L., Benabdellah, K., Lee, C. M., Hou, Y., et al. (2025) · The CRISPR Journal · doi:10.1089/crispr.2024.0058 · Cited by 13 · see citing works
Narra, M., Ray, A., Polley, B., Yang, H., & Bhowmik, P. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1718810 · Cited by 14 · see citing works
Chakraborty, S. S., Dutta, J. R., Ganesan, R., & Mináry, P. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4079-1_17 · Cited by 11 · see citing works
Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review
Kong, D., Qian, J., Gao, C., Wang, Y., Shi, T., & Ye, C. (2025) · Applied Biochemistry and Biotechnology · doi:10.1007/s12010-025-05260-x · Cited by 11 · see citing works
gRNA Design: How Its Evolution Impacted on CRISPR/Cas9 Systems Refinement
Motoche-Monar, C., Ordoñez, J. E., Chang, O., & Gonzales-Zubiate, F. A. (2023) · Biomolecules · doi:10.3390/biom13121698 · Cited by 11 · see citing works
Verma, P. (2025) · International Journal of High School Research · doi:10.36838/v7i2.113 · Cited by 1 · see citing works
Vadakkethil, A. A., Panda, S., Mitra, A., Dash, M., Baig, M. J., Angadi, U. B., Kumar, D. B. U., Jaiswal, S., Iquebal, M. A., & Molla, K. A. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf575 · Cited by 1 · see citing works
CRISPR System and AI Applications
Asim, M. N., Ahmed, S., & Dengel, A. (2025) · Springer eBooks · doi:10.1007/978-3-031-90450-9_7
Deep Neural Network-based Optimization of Guide RNA Design for CRISPR-Cas COVID-19 Diagnostics
Sulakshana, R., Charan, S., & Lakshmi, R. P. (2025) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici65217.2025.11252736
Hristova, E., Mirceva, G., & Ackovska, N. (2026) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa67686.2026.11467818
CrisprFusion: A feature fusion model with multi-type input features for sgRNA activity prediction
Chen, H., & Jiang, Z. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109137
AI-guided CRISPR in crop yield enhancement
Musazade, E., Mrisho, I. I., Zhang, D., Gao, J., Cheng, Z., & Feng, X. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44079-3.00005-x
- Authors
- Zarate, O. A., Yang, Y., Wang, X., & Wang, J.-P.
- Affiliations
- Northwestern University (US)
- Venue
- BMC Bioinformatics
- Published
- 2022-10-26
- Volume
- 23
- Issue
- 1
- Pages
- 446-446
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC9597963
- OpenAlex
- W4307561054
- Cited by
- 17
- References
- 38
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 3 · 2023: 2
- FWCI
- 1.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Keywords
- CRISPR/Cas9, single guide RNA, gradient boosting, LightGBM, model interpretability, sgRNA design, sequence features, genome editing
- MeSH terms
- Machine Learning, Gene Editing, RNA, Small Untranslated, CRISPR-Cas Systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
