<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Crawford KD</submitter><funding>NIBIB NIH HHS</funding><funding>W. M. Keck Foundation</funding><funding>Gary and Eileen Morgenthaler Fund</funding><funding>Pew Biomedical Scholars Program</funding><funding>UCSF Discovery Fellows Program</funding><funding>National Institute of Biomedical Imaging and Bioengineering</funding><funding>National Science Foundation</funding><pagination>gkae1199</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11754653</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>53(2)</volume><pubmed_abstract>The bacterial retron reverse transcriptase system has served as an intracellular factory for single-stranded DNA in many biotechnological applications. In these technologies, a natural retron non-coding RNA (ncRNA) is modified to encode a template for the production of custom DNA sequences by reverse transcription. The efficiency of reverse transcription is a major limiting step for retron technologies, but we lack systematic knowledge of how to improve or maintain reverse transcription efficiency while changing the retron sequence for custom DNA production. Here, we test thousands of different modifications to the Retron-Eco1 ncRNA and measure DNA production in pooled variant library experiments, identifying regions of the ncRNA that are tolerant and intolerant to modification. We apply t</pubmed_abstract><journal>Nucleic acids research</journal><pubmed_title>High throughput variant libraries and machine learning yield design rules for retron gene editors.</pubmed_title><pmcid>PMC11754653</pmcid><funding_grant_id>R21EB031393</funding_grant_id><funding_grant_id>R21 EB031393</funding_grant_id><funding_grant_id>MCB 2137692</funding_grant_id><pubmed_authors>Shipman SL</pubmed_authors><pubmed_authors>Goodarzi H</pubmed_authors><pubmed_authors>Khan AG</pubmed_authors><pubmed_authors>Crawford KD</pubmed_authors><pubmed_authors>Lopez SC</pubmed_authors></additional><is_claimable>false</is_claimable><name>High throughput variant libraries and machine learning yield design rules for retron gene editors.</name><description>The bacterial retron reverse transcriptase system has served as an intracellular factory for single-stranded DNA in many biotechnological applications. In these technologies, a natural retron non-coding RNA (ncRNA) is modified to encode a template for the production of custom DNA sequences by reverse transcription. The efficiency of reverse transcription is a major limiting step for retron technologies, but we lack systematic knowledge of how to improve or maintain reverse transcription efficiency while changing the retron sequence for custom DNA production. Here, we test thousands of different modifications to the Retron-Eco1 ncRNA and measure DNA production in pooled variant library experiments, identifying regions of the ncRNA that are tolerant and intolerant to modification. We apply t</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Jan</publication><modification>2025-04-04T22:07:21.917Z</modification><creation>2025-04-04T22:07:21.917Z</creation></dates><accession>S-EPMC11754653</accession><cross_references><pubmed>39658047</pubmed><doi>10.1093/nar/gkae1199</doi></cross_references></HashMap>