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1. Reproducibility metrics for CRISPR screens

   https://doi.org/10.1101/2022.02.19.480892  

   Billmann, Maximilian; Ward, Henry N.; Aregger, Michael; Costanzo, Michael; Andrews, Brenda J.; Boone, C; Moffat, J; Myers, Chad L. (February 2022, bioRxiv)

   CRISPR screens are used extensively to systematically interrogate the phenotype-to-genotype problem. In contrast to early CRISPR screens, which defined core cell fitness genes, most current efforts now aim to identify context-specific phenotypes that differentiate a cell line, genetic background or condition of interest, such as a drug treatment. While CRISPR-related technologies have shown great promise and a fast pace of innovation, a better understanding of standards and methods for quality assessment of CRISPR screen results is crucial to guide technology development and application. Specifically, many commonly used metrics for quantifying screen quality do not accurately measure the reproducibility of context-specific hits. We highlight the importance of reporting reproducibility statistics that directly relate to the purpose of the screen and suggest the use of metrics that are sensitive to context-specific signal. 

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2. CRISPR‐Cas Biochemistry and CRISPR‐Based Molecular Diagnostics

   https://doi.org/10.1002/anie.202214987  

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3. CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation

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4. Reproducibility metrics for context-specific CRISPR screens

   https://doi.org/10.1016/j.cels.2023.04.003  

   Billmann, Maximilian; Ward, Henry N.; Aregger, Michael; Costanzo, Michael; Andrews, Brenda J.; Boone, Charles; Moffat, Jason; Myers, Chad L. (May 2023, Cell Systems)
5. Navigating Computational Resources for the CRISPR Classroom

   https://doi.org/10.1007/978-3-031-73734-3_11  

   Andersen, Linnea; Goller, Carlos; Samsa, Leigh Ann; Sengupta, Arnab (January 2025, Springer Nature Switzerland)

   What You Will Learn in This Chapter In this chapter, instructors will develop foundational knowledge about how to select and use computational tools to teach CRISPR-Cas technologies. Broadly speaking, CRISPR-Cas is a sequence-based technology. Computational resources provide a platform for managing and interacting with these sequences. With appropriate instructional design, computational tools are a valuable complement to lessons about CRISPR-Cas technologies and are essential support tools for CRISPR-Cas experiments. With an ever-growing suite of computational tools available, in this chapter, instructors will learn to navigate the landscape of these tools to select the most appropriate tools for their classroom or laboratory needs. Instructors will learn to identify when computational resources are appropriate for use in their classroom (and when they are not appropriate), then how to select the most appropriate tools for their unique needs. Additionally, we introduce instructors to best practices in instructional design for using CRISPR-Cas computational tools in the classroom. Throughout, instructors will learn both the rationale and principle behind selection so they can evaluate tools discussed in this chapter and new ones as they become available. 

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