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      Systematic discovery of recombinases for efficient integration of large DNA sequences into the human genome

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          Abstract

          Large serine recombinases (LSRs) are DNA integrases that facilitate the site-specific integration of mobile genetic elements into bacterial genomes. Only a few LSRs, such as Bxb1 and PhiC31, have been characterized to date, with limited efficiency as tools for DNA integration in human cells. In this study, we developed a computational approach to identify thousands of LSRs and their DNA attachment sites, expanding known LSR diversity by >100-fold and enabling the prediction of their insertion site specificities. We tested their recombination activity in human cells, classifying them as landing pad, genome-targeting or multi-targeting LSRs. Overall, we achieved up to seven-fold higher recombination than Bxb1 and genome integration efficiencies of 40–75% with cargo sizes over 7 kb. We also demonstrate virus-free, direct integration of plasmid or amplicon libraries for improved functional genomics applications. This systematic discovery of recombinases directly from microbial sequencing data provides a resource of over 60 LSRs experimentally characterized in human cells for large-payload genome insertion without exposed DNA double-stranded breaks.

          Abstract

          Screening recombinases identifies tools for inserting large sequences into the human genome.

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          Most cited references106

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

              We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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                Author and article information

                Contributors
                bassik@stanford.edu
                lbintu@stanford.edu
                asbhatt@stanford.edu
                pdhsu@berkeley.edu
                Journal
                Nat Biotechnol
                Nat Biotechnol
                Nature Biotechnology
                Nature Publishing Group US (New York )
                1087-0156
                1546-1696
                10 October 2022
                10 October 2022
                2023
                : 41
                : 4
                : 488-499
                Affiliations
                [1 ]Arc Institute, Palo Alto, CA USA
                [2 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Department of Bioengineering, , University of California, Berkeley, ; Berkeley, CA USA
                [3 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Genetics, , Stanford University, ; Stanford, CA USA
                [4 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, University of California, Berkeley—University of California, San Francisco Graduate Program in Bioengineering, ; Berkeley, CA USA
                [5 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Bioengineering, , Stanford University, ; Stanford, CA USA
                [6 ]GRID grid.168010.e, ISNI 0000000419368956, Cancer Biology Program, , Stanford University, ; Stanford, CA USA
                [7 ]GRID grid.250671.7, ISNI 0000 0001 0662 7144, Laboratory of Molecular and Cell Biology, Salk Institute for Biological Studies, ; La Jolla, CA USA
                [8 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Medicine (Hematology), , Stanford University, ; Stanford, CA USA
                [9 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Innovative Genomics Institute, , University of California, Berkeley, ; Berkeley, CA USA
                [10 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Center for Computational Biology, , University of California, Berkeley, ; Berkeley, CA USA
                Author information
                http://orcid.org/0000-0003-2652-0541
                http://orcid.org/0000-0003-0809-7073
                http://orcid.org/0000-0001-5185-8427
                http://orcid.org/0000-0001-5443-6633
                http://orcid.org/0000-0001-8099-2975
                http://orcid.org/0000-0002-9380-2648
                Article
                1494
                10.1038/s41587-022-01494-w
                10083194
                36217031
                2eb424e1-690e-40bb-af08-c8bde667dccc
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 November 2021
                : 1 September 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: DGE-1656518
                Award ID: 2019284848
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: F99DK126120
                Award ID: 5UM1HG009436-02
                Award ID: 1DP2HD084a06901
                Award ID: R01AI143757
                Award ID: R01AI148623
                Award ID: DP5OD021369
                Award ID: R01GM131073
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000057, U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS);
                Award ID: R35M128947
                Award Recipient :
                Categories
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                © Springer Nature America, Inc. 2023

                Biotechnology
                gene delivery,genetics,genetic engineering,mobile elements
                Biotechnology
                gene delivery, genetics, genetic engineering, mobile elements

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