Orthologous CRISPR–Cas9 enzymes for combinatorial genetic screens

Cytochrome c and dATP-Dependent Formation of Apaf-1/Caspase-9 Complex Initiates an Apoptotic Protease Cascade

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Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy

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Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9

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Mitochondrial membrane permeabilization in cell death

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An inhibitor of Bcl-2 family proteins induces regression of solid tumours

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Discovery and saturation analysis of cancer genes across 21 tumour types

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ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets

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Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation

Gilbert, L. A., Horlbeck, M. A., Adamson, B., Villalta, J. E., Chen, Y., Whitehead, E. H., Guimaraes, C., Panning, B., Ploegh, H. L., Bassik, M. C., et al. (2014) · Cell · doi:10.1016/j.cell.2014.09.029 · Cited by 2,728

In vivo genome editing using Staphylococcus aureus Cas9

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Global Mapping of the Yeast Genetic Interaction Network

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Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls

Shen, J. P., & Ideker, T. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.06.026 · Cited by 27

Paralog Explorer: A resource for mining information about paralogs in common research organisms

Hu, Y., Ewen‐Campen, B., Comjean, A., Rodiger, J., Mohr, S. E., & Perrimon, N. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.041 · Cited by 28

Synthetic lethal gene pairs: Experimental approaches and predictive models

Tang, S., Gökbağ, B., Fan, K., Shao, S., Huo, Y., Wu, X., Cheng, L., & Li, L. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.961611 · Cited by 28

High-throughput genetic screens using CRISPR–Cas9 system

Kweon, J., & Kim, Y. (2018) · Archives of Pharmacal Research · doi:10.1007/s12272-018-1029-z · Cited by 27

Quantification of the affinities of CRISPR–Cas9 nucleases for cognate protospacer adjacent motif (PAM) sequences

Mekler, V. M., Kuznedelov, K. D., & Severinov, K. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.ra119.012239 · Cited by 29

Development of multiplexed orthogonal base editor (MOBE) systems

Cowan, Q. T., Gu, S., Gu, W., Ranzau, B. L., Simonson, T. S., & Komor, A. C. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02240-0 · Cited by 26

Pooled Genome-Scale CRISPR Screens in Single Cells

Schraivogel, D., Steinmetz, L. M., & Parts, L. (2023) · Annual Review of Genetics · doi:10.1146/annurev-genet-072920-013842 · Cited by 28

Deciphering pathogenicity of variants of uncertain significance with CRISPR-edited iPSCs

Guo, H., Liu, L., Nishiga, M., Cong, L., & Wu, J. C. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.08.009 · Cited by 32

Opportunities in biotechnology

Gartland, K. M. A., & Gartland, J. S. (2018) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2018.06.303 · Cited by 26

Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction

Fan, K., Tang, S., Gökbağ, B., Cheng, L., & Li, L. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2022.1103092 · Cited by 24

CROPseq-multi: a universal solution for multiplexed perturbation in high-content pooled CRISPR screensPreprint

Walton, R. T., Qin, Y., Kirby, B., Andrews, J. O., Taipale, M., Kang, B., & Blainey, P. C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.17.585235 · Cited by 27

Deciphering the mitophagy receptor network identifies a crucial role for OPTN (optineurin) in acute myeloid leukemia

Meyer, L. M., Koschade, S. E., Vischedyk, J. B., Thölken, M., Gubaš, A., Wegner, M., Başoğlu, M., Knapp, S., Kaulich, M., Eimer, S., et al. (2023) · Autophagy · doi:10.1080/15548627.2023.2230839 · Cited by 24

Generating, modeling and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges

Yaish, O., & Orenstein, Y. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae428 · Cited by 24

Applications of CRISPR-Cas in Bioengineering, Biotechnology, and Translational Research

Lau, C. (2018) · The CRISPR Journal · doi:10.1089/crispr.2018.0026 · Cited by 22

Highly Combinatorial Genetic Interaction Analysis Reveals a Multi-Drug Transporter Influence Network

Celaj, A., Gebbia, M., Musa, L., Coté, A. G., Snider, J. D., Wong, V., Ko, M., Fong, T., Bansal, P., Mellor, J. C., et al. (2019) · Cell Systems · doi:10.1016/j.cels.2019.09.009 · Cited by 23

Synthetic lethality in cancer drug discovery: challenges and opportunities

Gonçalves, E., Ryan, C. J., & Adams, D. J. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-025-01273-7 · Cited by 27

Designing custom CRISPR libraries for hypothesis-driven drug target discovery

Iyer, V. S., Jiang, L., Shen, Y., Boddul, S. V., Panda, S. K., Kasza, Z., Schmierer, B., & Wermeling, F. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.009 · Cited by 21

Re-defining synthetic lethality by phenotypic profiling for precision oncology

Akimov, Y. A., & Aittokallio, T. A. (2021) · Cell chemical biology · doi:10.1016/j.chembiol.2021.01.026 · Cited by 20

A case of mistaken identity

Hanna, R. E., & Doench, J. G. (2018) · Nature Biotechnology · doi:10.1038/nbt.4208 · Cited by 21

Application of CHyMErA Cas9-Cas12a combinatorial genome-editing platform for genetic interaction mapping and gene fragment deletion screening

Aregger, M., Xing, K., & Gonatopoulos-Pournatzis, T. (2021) · Nature Protocols · doi:10.1038/s41596-021-00595-1 · Cited by 22

Engineering CRISPR/Cas9 therapeutics for cancer precision medicine

Sharma, A. K., & Giri, A. K. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1309175 · Cited by 23

Rhabdoid Tumors Are Sensitive to the Protein-Translation Inhibitor Homoharringtonine

Howard, T. P., Oberlick, E. M., Rees, M. G., Arnoff, T. E., Pham, M., Brenan, L., DoCarmo, M., Hong, A. L., Kugener, G., Chou, H.-C., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-2717 · Cited by 21

A systematic evaluation of data processing and problem formulation of CRISPR off-target site prediction

Yaish, O., Asif, M., & Orenstein, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac157 · Cited by 19

Amplified dosage of the NKX2-1 lineage transcription factor controls its oncogenic role in lung adenocarcinoma

Pulice, J. L., & Meyerson, M. M. (2023) · Molecular Cell · doi:10.1016/j.molcel.2025.03.001 · Cited by 24

Expanding the Scope of Bacterial CRISPR Activation with PAM-Flexible dCas9 Variants

Kiattisewee, C., Karanjia, A. V., Legut, M., Daniloski, Z., Koplik, S. E., Nelson, J., Kleinstiver, B. P., Sanjana, N. E., Carothers, J. M., & Zalatan, J. G. (2022) · ACS Synthetic Biology · doi:10.1021/acssynbio.2c00405 · Cited by 21

Developing SHP2-based combination therapy for KRAS-amplified cancer

Li, T., Kikuchi, O., Zhou, J. R., Wang, Y., Pokharel, B., Bastl, K., Gokhale, P. C., Knott, A., Zhang, Y., Doench, J. G., et al. (2023) · JCI Insight · doi:10.1172/jci.insight.152714 · Cited by 20

Massively parallel evaluation and computational prediction of the activities and specificities of 17 small Cas9s

Seo, S.-Y., Min, S., Lee, S., Seo, J. H., Park, J., Kim, H. K., Song, M., Baek, D., Cho, S., & Kim, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01875-2 · Cited by 21

CRISPR-Cas and CRISPR-based screening system for precise gene editing and targeted cancer therapy

Qin, M., Deng, C., Wen, L., Luo, G., & Meng, Y. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05235-2 · Cited by 20

Radioresistance, DNA Damage and DNA Repair in Cells With Moderate Overexpression of RPA1

Velegzhaninov, I. O., Belykh, E. S., Rasova, E. E., Pylina, Y. I., Шадрин, Д. М., & Klokov, D. Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00855 · Cited by 22

SLKB: synthetic lethality knowledge base

Gökbağ, B., Tang, S., Fan, K., Cheng, L., Yu, L., Zhao, Y., & Li, L. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad806 · Cited by 19

The application of CRISPR/Cas9–based genome-wide screening to disease research

Chen, X., Zheng, M., Lin, S., Huang, M., Chen, S., & Chen, S. (2024) · Molecular and Cellular Probes · doi:10.1016/j.mcp.2024.102004 · Cited by 21

Improved detection of differentially represented DNA barcodes for high‐throughput clonal phenomics

Akimov, Y. A., Bulanova, D. R., Timonen, S., Wennerberg, K., & Aittokallio, T. A. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199195 · Cited by 19

Context-dependent genetic interactions in cancer

Henkel, L., Rauscher, B., & Boutros, M. (2019) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2019.03.004 · Cited by 20

Recent advances in regenerative medicine

Rajpoot, K., Safavi, M., Sreeharsha, N., & Tekade, R. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814455-8.00011-6 · Cited by 18

CRISPR-based methods for high-throughput annotation of regulatory DNA

Klann, T. S., Black, J. B., & Gersbach, C. A. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2018.02.004 · Cited by 19

A multilineage screen identifies actionable synthetic lethal interactions in human cancers

Fong, S. H., Kuenzi, B. M., Mattson, N. M., Lee, J. J. Y., Sanchez, K. S., Bojorquez-Gomez, A., Ford, K. M., Munson, B. P., Licon, K., Bergendahl, S., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01971-9 · Cited by 18

Reproducibility metrics for context-specific CRISPR screens

Billmann, M., Ward, H. N., Aregger, M., Costanzo, M., Andrews, B., Boone, C. M., Moffat, J., & Myers, C. L. (2023) · Cell Systems · doi:10.1016/j.cels.2023.04.003 · Cited by 16

Chromatin complex dependencies reveal targeting opportunities in leukemia

Najm, F. J., DeWeirdt, P. C., Moore, M. M., Bevill, S. M., Farran, C. A. E., Macias, K. A., Hegde, M., Waterbury, A. L., Liau, B. B., van Galen, P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36150-7 · Cited by 16

Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction

Fan, K., Gökbağ, B., Tang, S., Li, S., Huang, Y., Wang, L., Cheng, L., & Li, L. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae425 · Cited by 17

Covering the Combinatorial Design Space of Multiplex CRISPR/Cas Experiments in Plants

Huffel, K. V., Stock, M., Ruttink, T., & Bernard De Baets, C. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.907095 · Cited by 15

Analysis of combinatorial CRISPR screens with the Orthrus scoring pipeline

Ward, H. N., Aregger, M., Gonatopoulos-Pournatzis, T., Billmann, M., Ohsumi, T. K., Brown, K. R., Blencowe, B. J., Moffat, J., & Myers, C. L. (2021) · Nature Protocols · doi:10.1038/s41596-021-00596-0 · Cited by 17

An interpretable artificial intelligence framework for designing synthetic lethality-based anti-cancer combination therapies

Wang, J., Wen, Y., Zhang, Y., Wang, Z., Jiang, Y., Dai, C., Wu, L., Leng, D., He, S., & Bo, X. (2023) · Journal of Advanced Research · doi:10.1016/j.jare.2023.11.035 · Cited by 14

A next-generation dual guide CRISPR system for genetic interaction library screening

Burgold, T., Karakoç, E., Gonçalves, E., Barrio‐Hernandez, I., Dwane, L., Silva, R., Souster, E., Sharma, M., Beck, A., Koh, C. C., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-67256-9 · Cited by 13

AsCRISPR: A Web Server for Allele-Specific Single Guide RNA Design in Precision Medicine

Zhao, G., Li, J., & Tang, Y. (2020) · The CRISPR Journal · doi:10.1089/crispr.2020.0071 · Cited by 15

CRISPR Screens to Identify Regulators of Tumor Immunity

LaFleur, M. W., & Sharpe, A. H. (2021) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-070120-094725 · Cited by 16

Functional Genomics for Cancer Research: Applications In Vivo and In Vitro

O’Loughlin, T. A., & Gilbert, L. A. (2018) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-030518-055742 · Cited by 14

A method for characterizing Cas9 variants via a one-million target sequence library of self-targeting sgRNAs

Tálas, A., Huszár, K., Kulcsár, P. I., Varga, J. K., Varga, É., Tóth, E., Welker, Z., Erdős, G., Pach, P. F., Welker, Á., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1220 · Cited by 13

Highly efficient multiplex base editing: One-shot deactivation of eight genes in Shewanella oneidensis MR-1

Chen, Y., Cheng, M., Li, Y., Wang, L., Fang, L., Cao, Y., & Song, H. (2022) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2022.09.005 · Cited by 15

A dual sgRNA library design to probe genetic modifiers using genome-wide CRISPRi screens

Guna, A., Page, K. R., Replogle, J. M., Esantsi, T. K., Wang, M. L., Weissman, J. S., & Voorhees, R. M. (2023) · BMC Genomics · doi:10.1186/s12864-023-09754-y · Cited by 14

Common computational tools for analyzing CRISPR screens

Colic, M., & Hart, T. (2021) · Emerging Topics in Life Sciences · doi:10.1042/etls20210222 · Cited by 13

Engineering of a compact, high-fidelity EbCas12a variant that can be packaged with its crRNA into an all-in-one AAV vector delivery system

Wang, H., Zhou, J., Lei, J., Mo, G., Wu, Y., Liu, H., Pang, Z., Du, M., Zhou, Z., Paek, C., et al. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002619 · Cited by 17

Mapping the Genetic Interaction Network of PARP inhibitor ResponsePreprint

Simpson, D., Jia, L., Jing, Y., & Adamson, B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.19.553986 · Cited by 16

Paralog-based synthetic lethality: rationales and applications

Xin, Y., & Zhang, Y. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1168143 · Cited by 12

Modular vector assembly enables rapid assessment of emerging CRISPR technologies

McGee, A. V., Liu, Y. V., Griffith, A. L., Szegletes, Z. M., Wen, B., Kraus, C., Miller, N. W., Steger, R. J., Velasco, B. E., Bosch, J. A., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100519 · Cited by 12

CEBPA repression by MECOM blocks differentiation to drive aggressive leukemias

Fleming, T. J., Antoszewski, M., Lambo, S., Gundry, M. C., Piussi, R., Wahlster, L., Shah, S. B., Reed, F. E., Dong, K. D., Paulo, J. A., et al. (2025) · Blood · doi:10.1182/blood.2025028954 · Cited by 11

Large-scale datasets uncovering cell signalling networks in cancer: context matters

Sharma, S., & Petsalaki, E. (2019) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2019.05.001 · Cited by 10

HIF-2α drives an intrinsic vulnerability to ferroptosis in clear cell renal cell carcinomaPreprint

Zou, Y., Palte, M. J., Deik, A., Li, H., Eaton, J. K., Wang, W., Tseng, Y., Deasy, R., Alimova, M., Dančík, V., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/388041 · Cited by 11

Chemogenetic interactions in human cancer cells

Colic, M., & Hart, T. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.09.006 · Cited by 10

Manipulating the NKG2D Receptor-Ligand Axis Using CRISPR: Novel Technologies for Improved Host Immunity

Alves, E., McLeish, E., Blancafort, P., Coudert, J. D., & Gaudieri, S. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.712722 · Cited by 10

Towards a CRISPeR understanding of homologous recombination with high-throughput functional genomics

Hayward, S. B., & Ciccia, A. (2021) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2021.08.006 · Cited by 9

Pathway-driven analysis of synthetic lethal interactions in cancer using perturbation screens

Karimpour, M., Totonchi, M., Behmanesh, M., & Montazeri, H. (2023) · Life Science Alliance · doi:10.26508/lsa.202302268 · Cited by 8

Functional Genomics Approaches to Elucidate Vulnerabilities of Intrinsic and Acquired Chemotherapy Resistance

Çetin, R., Quandt, E., & Kaulich, M. (2021) · Cells · doi:10.3390/cells10020260 · Cited by 10

In Vivo Cancer-Based Functional Genomics

Li, C., & Kasinski, A. L. (2020) · Trends in cancer · doi:10.1016/j.trecan.2020.07.004 · Cited by 9

A systematic analysis of genetic interactions and their underlying biology in childhood cancer

Daub, J. T., Amini, S., Kersjes, D. J. E., Ma, X., Jäger, N., Zhang, J., Pfister, S. M., Holstege, F. C. P., & Kemmeren, P. (2021) · Communications Biology · doi:10.1038/s42003-021-02647-4 · Cited by 9

DNA barcode-guided lentiviral CRISPRa tool to trace and isolate individual clonal lineages in heterogeneous cancer cell populationsPreprint

Akimov, Y. A., Bulanova, D. R., Abyzova, M. S., Wennerberg, K., & Aittokallio, T. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/622506 · Cited by 7

Efficient gene knockout and genetic interactions: the IN4MER CRISPR/Cas12a multiplex knockout platformPreprint

Anvar, N. E., Lin, C., Ma, X., Wilson, L., Steger, R., Sangree, A. K., Colic, M., Wang, S. H., Doench, J. G., & Hart, T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.03.522655 · Cited by 8

Global genetic interaction network of a human cell maps conserved principles and informs functional interpretation of gene co-essentiality profiles

Billmann, M., Costanzo, M., Zhang, X., Hassan, A. Z., Rahman, M., Brown, K. R., Chan, K. S., Tong, A. H. Y., Pons, C., Ward, H. N., et al. (2026) · Cell · doi:10.1016/j.cell.2026.03.044 · Cited by 17

Activating UCHL1 through the CRISPR activation system promotes cartilage differentiation mediated by HIF ‐1α/ SOX9

Shi, S., Yang, G., Yan, Q., Wan, S., Li, M., & Li, M. (2024) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.70051 · Cited by 7

Functional buffering via cell-specific gene expression promotes tissue homeostasis and cancer robustness

Lin, H.-K., Cheng, J., Wu, C., Hsieh, F., Dunlap, C. A., & Chen, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-06813-4 · Cited by 6

Genetic landscape of T cells identifies synthetic lethality for T-ALL

O’Meara, C. P., Guerri, L., Lawir, D.-F., Mateos, F., Iconomou, M., Iwanami, N., Soza‐Ried, C., Sikora, K., Siamishi, I., Giorgetti, O. B., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-02694-x · Cited by 7

Synthetic Lethality in Lung Cancer—From the Perspective of Cancer Genomics

Shimomura, I., Yamamoto, Y., & Ochiya, T. (2019) · Medicines · doi:10.3390/medicines6010038 · Cited by 8

A systematic genetic interaction map of human solute carriers assigns a role to SLC25A51/MCART1 in mitochondrial NAD uptakePreprint

Girardi, E., Fiume, G., Goldmann, U., Sin, C., Müller, F., Lindinger, S., Sedlyarov, V., Srndic, I., Agerer, B., Kartnig, F., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.31.275818 · Cited by 7

Multi-locus CRISPRi targeting with a single truncated guide RNA

Moore, M. M., Wekhande, S., Issner, R., Collins, A., Cruz, A. J., Liu, Y. V., Javed, N. M., Casaní-Galdón, S., Buenrostro, J. D., Epstein, C. B., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56144-x · Cited by 6

An in-library ligation strategy and its application in CRISPR/Cas9 screening of high-order gRNA combinations

Lu, Z., Ni, K., Wang, Y., Zhou, Y., Li, Y., Yan, J., Song, Q., Liu, M., Xu, Y., Yu, Z., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac458 · Cited by 6

Lentiviral Transduction-based CRISPR/Cas9 Editing of Schistosoma mansoni Acetylcholinesterase

Du, X., McManus, D. P., French, J. D., Sivakumaran, H., Johnston, R. L., Kondrashova, O., Fogarty, C. E., Jones, M. K., & You, H. (2023) · Current Genomics · doi:10.2174/1389202924666230823094608 · Cited by 6

CSSLdb: Discovery of cancer-specific synthetic lethal interactions based on machine learning and statistic inference

Dou, Y., Ren, Y., Zhao, X., Jin, J., Xiong, S., Luo, L., Xu, X., Yang, X., Yu, J., Guo, L., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108066 · Cited by 6

Computational Methods for Analysis of Large-Scale CRISPR Screens

Lin, X., Chemparathy, A., Russa, M. F. L., Daley, T., & Qi, L. S. (2020) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020520-113523 · Cited by 7

Tools for Decoding Ubiquitin Signaling in DNA Repair

Foster, B., Attwood, M., & Gibbs‐Seymour, I. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.760226 · Cited by 5

Fully accessible fitness landscape of oncogene-negative lung adenocarcinoma

Yousefi, M., Andrejka, L., Szamecz, M., Winslow, M. M., Petrov, D. A., & Boross, G. Z. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2303224120 · Cited by 4

One-step assembly of large CRISPR arrays enables multi-functional targeting and reveals constraints on array designPreprint

Liao, C., Ttofali, F., Slotkowski, R., Denny, S. R., Cecil, T. D., Leenay, R. T., Keung, A. J., & Beisel, C. L. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/312421 · Cited by 6

Up, down, and out: optimized libraries for CRISPRa, CRISPRi, and CRISPR-knockout genetic screensPreprint

Sanson, K. R., Hanna, R. E., Hegde, M., Donovan, K. F., Strand, C., Sullender, M. E., Vaimberg, E. W., Goodale, A. B., Root, D. E., Piccioni, F., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/356626 · Cited by 6

Expanding the CITE-seq tool-kit: Detection of proteins, transcriptomes, clonotypes and CRISPR perturbations with multiplexing, in a single assayPreprint

Mimitou, E. P., Cheng, A. Y., Montalbano, A., Hao, S., Stoeckius, M., Legut, M., Roush, T., Herrera, A. M., Papalexi, E., Ouyang, Z., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/466466 · Cited by 7

Using graph-based model to identify cell specific synthetic lethal effects

Pu, M., Cheng, K., Li, X., Xin, Y., Wei, L., Jin, S., Zheng, W., Peng, G., Tang, Q., Zhou, J., et al. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.10.011 · Cited by 5

Optimized metrics for orthogonal combinatorial CRISPR screens

Çetin, R., Wegner, M., Luwisch, L., Saud, S., Achmedov, T., Süsser, S., Vera-Guapi, A., Müller, K., Matthess, Y., Quandt, E., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-34597-8 · Cited by 5

Highly efficient multiplex editing: One-shot generation of 8x Nicotiana benthamiana and 12x Arabidopsis mutantsPreprint

Stuttmann, J., Barthel, K., Martin, P., Ordon, J., Erickson, J. L., Herr, R., Ferik, F., Kretschmer, C., Berner, T., Keilwagen, J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.31.018671 · Cited by 5

A global genetic interaction map of a human cell reveals conserved principles of genetic networksPreprint

Billmann, M., Costanzo, M., Zhang, X., Hassan, A. Z., Rahman, M., Brown, K. R., Chan, K., Tong, A. H. Y., Pons, C., Ward, H. N., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.30.662193 · Cited by 5

KG-SLomics: Synthetic Lethality Prediction Using Knowledge Graph and Cancer Type-Specific Multiomics Integrated Graph Neural Network

Lee, S., & Nam, H. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3631499 · Cited by 4

Genome-scale functional genomics screening highlights genes impacting protein fucosylation in Chinese hamster ovary cells

Barlan, K., Bhide, G. P., White, D. R., Lake, M. R., Lu, C., Rieder, S., Fan, L., & Hsieh, C.-L. (2023) · SLAS DISCOVERY · doi:10.1016/j.slasd.2023.10.004 · Cited by 4

Advancing the genetic engineering toolbox by combining AsCas12a knock-in mice with ultra-compact screening

Jin, W., Deng, Y., Marca, J. E. L., Lelliott, E. J., Diepstraten, S. T., Koenig, C., Tai, L., Snetkova, V., Dorighi, K. M., Hoberecht, L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56282-2 · Cited by 4

Shuffle-Seq: En masse combinatorial encoding for n -way genetic interaction screensPreprint

Dixit, A. C., Kuksenko, O., Feldman, D., & Regev, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/861443 · Cited by 4

The lncRNA EPB41L4A-AS1 regulates gene expression in the nucleus and exerts cell type-dependent effects on cell cycle progressionPreprint

Samdal, H., Hegre, S. A., Chawla, K., Liabakk, N., Aas, P. A., Sporsheim, B., & Sætrom, P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.10.430566 · Cited by 4

Automated Design of Oligopools and Rapid Analysis of Massively Parallel Barcoded Measurements

Hossain, A., Cetnar, D. P., LaFleur, T. L., McLellan, J. R., & Salis, H. M. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00661 · Cited by 3

Leveraging CRISPR/Cas9 in notable bacteria for the production of industrially valuable compounds

Karim, M. D., Abuhena, M., Rahman, L., & Al-Rashid, J. (2025) · Systems Microbiology and Biomanufacturing · doi:10.1007/s43393-024-00326-z · Cited by 4

Evaluation of Cas13d as a tool for genetic interaction mapping

Kassem, G. E., Hillmer, J., & Boettcher, M. (2025) · Nature Communications · doi:10.1038/s41467-025-56747-4 · Cited by 3

CRISPR screening in cancer stem cells

Wang, B., & Wong, A. S. L. (2022) · Essays in Biochemistry · doi:10.1042/ebc20220009 · Cited by 4

Methods and Techniques to Select Efficient Guides for CRISPR-Mediated Genome Editing in Plants

D’Orso, F., Forte, V., Baima, S., Possenti, M., Palma, D., & Morelli, G. (2023) · Springer eBooks · doi:10.1007/978-3-031-46150-7_6 · Cited by 3

HyCas9-12aGEP: an efficient genome editing platform for Corynebacterium glutamicum

Zhang, F., Wang, J., Li, C.-L., & Zhang, W.-G. (2024) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2024.1327172 · Cited by 3

Decoding gene regulation with CRISPR perturbations

Oberlin, S., & McManus, M. T. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02222-2 · Cited by 3

Combinatorial In Vivo Genome Editing Identifies Widespread Epistasis and an Accessible Fitness Landscape During Lung Tumorigenesis

Hebert, J. D., Tang, Y. J., Szamecz, M., Andrejka, L., Lopez, S. S., Petrov, D. A., Boross, G. Z., & Winslow, M. M. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf023 · Cited by 4

Generation of Monoclonal iPSC Lines with Stable Cas9 Expression and High Cas9 Activity

Liao, J. Q., Zhou, G., & Zhou, Y. (2020) · Methods in molecular biology · doi:10.1007/7651_2020_304 · Cited by 3

Comparative optimization of combinatorial CRISPR screens

Li, R., Klingbeil, O., Monducci, D., Young, M. J., Rodriguez, D. J., Bayyat, Z., Dempster, J. M., Kesar, D., Yang, X., Zamanighomi, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30196-9 · Cited by 37

Abstract 2950: CRISPR/saCas9 and CRISPR/spCas9 systems for combinatorial genetic screens (CRISPR-KO, CRISPRa, CRISPRi)

Isachenko, N., Aleksanyan, G., Diehl, P., & Tedesco, D. (2024) · Cancer Research · doi:10.1158/1538-7445.am2024-2950

PrePAIRing Cas9s for screening success

Zhou, Z. L., & Wei, W. (2018) · Nature Biotechnology · doi:10.1038/nbt.4075 · Cited by 2

Generation of dual-gRNA library for combinatorial CRISPR screening of synthetic lethal gene pairs

Tang, S., Wu, X., Liu, J., Zhang, Q., Wang, X., Shao, S., Gökbağ, B., Fan, K., Liu, X., Li, F., et al. (2022) · STAR Protocols · doi:10.1016/j.xpro.2022.101556 · Cited by 14

Big Papi uncovers genetic interactions

(2018) · Nature Methods · doi:10.1038/nmeth.4589