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A python-based pipeline for optimized sgRNA target-site identification in CRISPR-Cas9 gene knockout

Year 2025, Volume: 38 Issue: 3, 147 - 150, 16.12.2025
https://doi.org/10.29136/mediterranean.1674035

Abstract

This study presents the development of a novel Python-based pipeline for the identification of optimal single-guide RNA (sgRNA) target sites in CRISPR-Cas9 gene knockout applications. The pipeline offers a comprehensive analysis framework for targeting the Myostatin gene in Gallus gallus, assessing both the efficiency and specificity of potential sgRNA sites. By integrating state-of-the-art bioinformatics tools and databases during the design phase, the pipeline has been rigorously tested across various genetic models, demonstrating superior performance relative to existing software. Notably, our pipeline predicted a maximum efficiency of 100% in targeting the Myostatin gene, outperforming the CHOPCHOP and E-CRISP tools by identifying novel, high-potential target sites. Additionally, the pipeline’s user-friendly interface and interactive visualization capabilities enhance its accessibility, making it an invaluable resource for researchers and biotechnological applications in CRISPR-Cas9 genome editing. This work aims to advance gene editing precision, streamline workflows, and establish a new benchmark for genetic engineering technologies.

References

  • Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, Hsu PD, Wu X, Jiang W, Marraffini LA, Zhang F (2013) Multiplex genome engineering using CRISPR/Cas systems. Science 339(6121): 819-823.
  • Cui Y, Xu J, Cheng M, Liao X, Peng S (2018) Review of CRISPR/Cas9 sgRNA design tools. Interdisciplinary Sciences: Computational Life Sciences 10: 455-465.
  • Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R, Virgin HW, Listgarten J, Root DE (2016) Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology 34(2): 184-191.
  • Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E (2012) A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337: 816-821.
  • Kuan PF, Powers S, He S, Li K, Zhao X, Huang B (2017) A systematic evaluation of nucleotide properties for CRISPR sgRNA design. Bmc Bioinformatics 18: 1-9.
  • Labaj W, Papiez A, Polanski A, Polanska J (2017) Comprehensive analysis of MILE gene expression data set advances discovery of leukaemia type and subtype biomarkers. Interdisciplinary Sciences: Computational Life Sciences 9(1): 24-1935.
  • Mali P, Yang L, Esvelt KM, Aach J, Guell M, DiCarlo JE, Norville JE, Church GM (2013) RNA-guided human genome engineering via Cas9. Science 339(6121): 823-826.
  • McKenna A, Shendure J (2018) FlashFry: a fast and flexible tool for large-scale CRISPR target design. BMC Biology 16: 1-6.
  • Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ (2015) CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nature Methods 12(10): 982-988.
  • Park J, Kim JS, Bae S (2016) Cas-Database: web-based genome-wide guide RNA library design for gene knockout screens using CRISPR-Cas9. Bioinformatics 32(13): 2017-2023.
  • Prykhozhij SV, Rajan V, Gaston D, Berman JN (2015) CRISPR multitargeter: a web tool to find common and unique CRISPR single guide RNA targets in a set of similar sequences. PLOS One 10(3): e0119372.
  • Surridge C (2018) A crispr definition of genetic modification. Nature Plants 4(5): 233.
  • Symington LS, Gautier J (2011) Double-strand break end resection and repair pathway choice. Annual Review of Genetics 45: 247-271.
  • Tian S, Jiang L, Gao Q, Zhang J, Zong M, Zhang H, Ren Y, Guo S, Gong G, Liu F, Xu Y (2017) Efficient CRISPR/Cas9-based gene knockout in watermelon. Plant Cell Reports 36(3): 399-406.
  • Wang T, Birsoy K, Hughes NW, Krupczak KM, Post Y, Wei JJ, Lander ES, Sabatini DM (2015) Identification and characterization of essential genes in the human genome. Science 350(6264): 1096-1101.
  • Yennmalli RM, Kalra S, Srivastava PA, Garlapati, VK (2017) Computational tools and resources for CRISPR/Cas 9 genome editing method. MOJ Proteomics & Bioinformatics 5(4): 116-120.
  • Zhu LJ (2015) Overview of guide RNA design tools for CRISPR-Cas9 genome editing technology. Frontiers in Biology 10(4): 289-296.

A python-based pipeline for optimized sgRNA target-site identification in CRISPR-Cas9 gene knockout

Year 2025, Volume: 38 Issue: 3, 147 - 150, 16.12.2025
https://doi.org/10.29136/mediterranean.1674035

Abstract

This study presents the development of a novel Python-based pipeline for the identification of optimal single-guide RNA (sgRNA) target sites in CRISPR-Cas9 gene knockout applications. The pipeline offers a comprehensive analysis framework for targeting the Myostatin gene in Gallus gallus, assessing both the efficiency and specificity of potential sgRNA sites. By integrating state-of-the-art bioinformatics tools and databases during the design phase, the pipeline has been rigorously tested across various genetic models, demonstrating superior performance relative to existing software. Notably, our pipeline predicted a maximum efficiency of 100% in targeting the Myostatin gene, outperforming the CHOPCHOP and E-CRISP tools by identifying novel, high-potential target sites. Additionally, the pipeline’s user-friendly interface and interactive visualization capabilities enhance its accessibility, making it an invaluable resource for researchers and biotechnological applications in CRISPR-Cas9 genome editing. This work aims to advance gene editing precision, streamline workflows, and establish a new benchmark for genetic engineering technologies.

References

  • Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, Hsu PD, Wu X, Jiang W, Marraffini LA, Zhang F (2013) Multiplex genome engineering using CRISPR/Cas systems. Science 339(6121): 819-823.
  • Cui Y, Xu J, Cheng M, Liao X, Peng S (2018) Review of CRISPR/Cas9 sgRNA design tools. Interdisciplinary Sciences: Computational Life Sciences 10: 455-465.
  • Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R, Virgin HW, Listgarten J, Root DE (2016) Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology 34(2): 184-191.
  • Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E (2012) A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337: 816-821.
  • Kuan PF, Powers S, He S, Li K, Zhao X, Huang B (2017) A systematic evaluation of nucleotide properties for CRISPR sgRNA design. Bmc Bioinformatics 18: 1-9.
  • Labaj W, Papiez A, Polanski A, Polanska J (2017) Comprehensive analysis of MILE gene expression data set advances discovery of leukaemia type and subtype biomarkers. Interdisciplinary Sciences: Computational Life Sciences 9(1): 24-1935.
  • Mali P, Yang L, Esvelt KM, Aach J, Guell M, DiCarlo JE, Norville JE, Church GM (2013) RNA-guided human genome engineering via Cas9. Science 339(6121): 823-826.
  • McKenna A, Shendure J (2018) FlashFry: a fast and flexible tool for large-scale CRISPR target design. BMC Biology 16: 1-6.
  • Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ (2015) CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nature Methods 12(10): 982-988.
  • Park J, Kim JS, Bae S (2016) Cas-Database: web-based genome-wide guide RNA library design for gene knockout screens using CRISPR-Cas9. Bioinformatics 32(13): 2017-2023.
  • Prykhozhij SV, Rajan V, Gaston D, Berman JN (2015) CRISPR multitargeter: a web tool to find common and unique CRISPR single guide RNA targets in a set of similar sequences. PLOS One 10(3): e0119372.
  • Surridge C (2018) A crispr definition of genetic modification. Nature Plants 4(5): 233.
  • Symington LS, Gautier J (2011) Double-strand break end resection and repair pathway choice. Annual Review of Genetics 45: 247-271.
  • Tian S, Jiang L, Gao Q, Zhang J, Zong M, Zhang H, Ren Y, Guo S, Gong G, Liu F, Xu Y (2017) Efficient CRISPR/Cas9-based gene knockout in watermelon. Plant Cell Reports 36(3): 399-406.
  • Wang T, Birsoy K, Hughes NW, Krupczak KM, Post Y, Wei JJ, Lander ES, Sabatini DM (2015) Identification and characterization of essential genes in the human genome. Science 350(6264): 1096-1101.
  • Yennmalli RM, Kalra S, Srivastava PA, Garlapati, VK (2017) Computational tools and resources for CRISPR/Cas 9 genome editing method. MOJ Proteomics & Bioinformatics 5(4): 116-120.
  • Zhu LJ (2015) Overview of guide RNA design tools for CRISPR-Cas9 genome editing technology. Frontiers in Biology 10(4): 289-296.
There are 17 citations in total.

Details

Primary Language English
Subjects Zootechny (Other)
Journal Section Research Article
Authors

Berkant Ismail Yıldız 0000-0001-8965-6361

Kemal Eskioglu 0000-0001-9387-1136

Demir Ozdemir 0000-0003-2160-6485

Submission Date April 11, 2025
Acceptance Date July 1, 2025
Publication Date December 16, 2025
Published in Issue Year 2025 Volume: 38 Issue: 3

Cite

APA Yıldız, B. I., Eskioglu, K., & Ozdemir, D. (2025). A python-based pipeline for optimized sgRNA target-site identification in CRISPR-Cas9 gene knockout. Mediterranean Agricultural Sciences, 38(3), 147-150. https://doi.org/10.29136/mediterranean.1674035
AMA Yıldız BI, Eskioglu K, Ozdemir D. A python-based pipeline for optimized sgRNA target-site identification in CRISPR-Cas9 gene knockout. Mediterranean Agricultural Sciences. December 2025;38(3):147-150. doi:10.29136/mediterranean.1674035
Chicago Yıldız, Berkant Ismail, Kemal Eskioglu, and Demir Ozdemir. “A Python-Based Pipeline for Optimized SgRNA Target-Site Identification in CRISPR-Cas9 Gene Knockout”. Mediterranean Agricultural Sciences 38, no. 3 (December 2025): 147-50. https://doi.org/10.29136/mediterranean.1674035.
EndNote Yıldız BI, Eskioglu K, Ozdemir D (December 1, 2025) A python-based pipeline for optimized sgRNA target-site identification in CRISPR-Cas9 gene knockout. Mediterranean Agricultural Sciences 38 3 147–150.
IEEE B. I. Yıldız, K. Eskioglu, and D. Ozdemir, “A python-based pipeline for optimized sgRNA target-site identification in CRISPR-Cas9 gene knockout”, Mediterranean Agricultural Sciences, vol. 38, no. 3, pp. 147–150, 2025, doi: 10.29136/mediterranean.1674035.
ISNAD Yıldız, Berkant Ismail et al. “A Python-Based Pipeline for Optimized SgRNA Target-Site Identification in CRISPR-Cas9 Gene Knockout”. Mediterranean Agricultural Sciences 38/3 (December2025), 147-150. https://doi.org/10.29136/mediterranean.1674035.
JAMA Yıldız BI, Eskioglu K, Ozdemir D. A python-based pipeline for optimized sgRNA target-site identification in CRISPR-Cas9 gene knockout. Mediterranean Agricultural Sciences. 2025;38:147–150.
MLA Yıldız, Berkant Ismail et al. “A Python-Based Pipeline for Optimized SgRNA Target-Site Identification in CRISPR-Cas9 Gene Knockout”. Mediterranean Agricultural Sciences, vol. 38, no. 3, 2025, pp. 147-50, doi:10.29136/mediterranean.1674035.
Vancouver Yıldız BI, Eskioglu K, Ozdemir D. A python-based pipeline for optimized sgRNA target-site identification in CRISPR-Cas9 gene knockout. Mediterranean Agricultural Sciences. 2025;38(3):147-50.

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