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Abstract Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas genome-wide screens are powerful tools for unraveling genotype–phenotype relationships, enabling precise manipulation of genes to study and engineer industrially useful traits. Traditional genetic methods, such as random mutagenesis or RNA interference, often lack the specificity and scalability required for large-scale functional genomic screens. CRISPR systems overcome these limitations by offering precision gene targeting and manipulation, allowing for high-throughput investigations into gene function and interactions. Recent work has shown that CRISPR genome editing is widely adaptable to several yeast species, many of which have natural traits suited for industrial biotechnology. In this review, we discuss recent advances in yeast functional genomics, emphasizing advancements made with CRISPR tools. We discuss how the development and optimization of CRISPR genome-wide screens have enabled a host-first approach to metabolic engineering, which takes advantage of the natural traits of nonconventional yeast—fast growth rates, high stress tolerance, and novel metabolism—to create new production hosts. Lastly, we discuss future directions, including automation and biosensor-driven screens, to enhance high-throughput CRISPR-enabled yeast engineering.
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Dimensionality reduction methods for extracting functional networks from large‐scale CRISPR screens
Abstract CRISPR‐Cas9 screens facilitate the discovery of gene functional relationships and phenotype‐specific dependencies. The Cancer Dependency Map (DepMap) is the largest compendium of whole‐genome CRISPR screens aimed at identifying cancer‐specific genetic dependencies across human cell lines. A mitochondria‐associated bias has been previously reported to mask signals for genes involved in other functions, and thus, methods for normalizing this dominant signal to improve co‐essentiality networks are of interest. In this study, we explore three unsupervised dimensionality reduction methods—autoencoders, robust, and classical principal component analyses (PCA)—for normalizing the DepMap to improve functional networks extracted from these data. We propose a novel “onion” normalization technique to combine several normalized data layers into a single network. Benchmarking analyses reveal that robust PCA combined with onion normalization outperforms existing methods for normalizing the DepMap. Our work demonstrates the value of removing low‐dimensional signals from the DepMap before constructing functional gene networks and provides generalizable dimensionality reduction‐based normalization tools.
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- Award ID(s):
- 1818293
- PAR ID:
- 10465296
- Publisher / Repository:
- DOI PREFIX: 10.15252
- Date Published:
- Journal Name:
- Molecular Systems Biology
- ISSN:
- 1744-4292
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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