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      CRISPR-COPIES: an in silico platform for discovery of neutral integration sites for CRISPR/Cas-facilitated gene integration

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          Abstract

          The CRISPR/Cas system has emerged as a powerful tool for genome editing in metabolic engineering and human gene therapy. However, locating the optimal site on the chromosome to integrate heterologous genes using the CRISPR/Cas system remains an open question. Selecting a suitable site for gene integration involves considering multiple complex criteria, including factors related to CRISPR/Cas-mediated integration, genetic stability, and gene expression. Consequently, identifying such sites on specific or different chromosomal locations typically requires extensive characterization efforts. To address these challenges, we have developed CRISPR-COPIES, a COmputational Pipeline for the Identification of CRISPR/Cas-facilitated int Egration Sites. This tool leverages ScaNN, a state-of-the-art model on the embedding-based nearest neighbor search for fast and accurate off-target search, and can identify genome-wide intergenic sites for most bacterial and fungal genomes within minutes. As a proof of concept, we utilized CRISPR-COPIES to characterize neutral integration sites in three diverse species: Saccharomyces cerevisiae, Cupriavidus necator, and HEK293T cells. In addition, we developed a user-friendly web interface for CRISPR-COPIES ( https://biofoundry.web.illinois.edu/copies/). We anticipate that CRISPR-COPIES will serve as a valuable tool for targeted DNA integration and aid in the characterization of synthetic biology toolkits, enable rapid strain construction to produce valuable biochemicals, and support human gene and cell therapy applications.

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          BLAST+: architecture and applications

          Background Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications. Results We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site. Conclusion The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.
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            Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9

            CRISPR-Cas9-based genetic screens are a powerful new tool in biology. By simply altering the sequence of the single-guide RNA (sgRNA), Cas9 can be reprogrammed to target different sites in the genome with relative ease, but the on-target activity and off-target effects of individual sgRNAs can vary widely. Here, we use recently-devised sgRNA design rules to create human and mouse genome-wide libraries, perform positive and negative selection screens and observe that the use of these rules produced improved results. Additionally, we profile the off-target activity of thousands of sgRNAs and develop a metric to predict off-target sites. We incorporate these findings from large-scale, empirical data to improve our computational design rules and create optimized sgRNA libraries that maximize on-target activity and minimize off-target effects to enable more effective and efficient genetic screens and genome engineering.
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              MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets

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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                12 April 2024
                13 February 2024
                13 February 2024
                : 52
                : 6
                : e30
                Affiliations
                Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                School of Biomolecular Science and Engineering, Vidyasirimedhi Institute of Science and Technology, Wangchan Valley , Rayong 21210, Thailand
                Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                Department of Bioengineering, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                Department of Bioengineering, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
                Author notes
                To whom correspondence should be addressed. Tel: +1 217 333 2631; Fax: +1 217 333 5052; Email: zhao5@ 123456illinois.edu
                Author information
                https://orcid.org/0000-0002-9069-6739
                Article
                gkae062
                10.1093/nar/gkae062
                11014336
                38346683
                ad269057-7e39-411c-b694-d5c94680b16b
                © The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 19 January 2024
                : 09 January 2024
                : 15 July 2023
                Page count
                Pages: 17
                Funding
                Funded by: U.S. Department of Energy, DOI 10.13039/100000015;
                Award ID: DE-SC0018420
                Categories
                AcademicSubjects/SCI00010
                Narese/1
                Narese/29
                Narese/24
                Methods

                Genetics
                Genetics

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