[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125711-en":3,"doc-seo-125711-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125711,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Genome mining for anti-CRISPR operons using machine learning","Motivation: Anti-CRISPR (Acr) proteins encoded by (pro-)viruses block the CRISPR-Cas immune system in prokaryotic hosts, and they can enable more controllable genome editing. Known acr genes frequently co-occur with other acr genes and phage structural genes within the same operon, but existing Acr prediction tools largely ignore this genomic context feature. AOminer addresses this gap by leveraging the conserved neighborhood of known acr genes and homologs. Results: A two-state HMM learns operon context to separate Acr operons from non-Acr regions, enabling automated mining with improved accuracy.","University of Nebraska-Lincoln  \nDigitalCommons@University of Nebraska-Lincoln  \n\n| Nutrition and Health Sciences--Faculty Publications | Nutrition and Health Sciences, Department of |\n| --- | --- |\n| 5-9-2023\u003Cbr>Genome mining for anti-CRISPR operons using machine learning Bowen Yang\u003Cbr>Minal Khatri\u003Cbr>Jinfang Zheng Jitender S. Deogun Yanbin Yin\u003Cbr>Follow this and additional works at: [https://digitalcommons.unl.edu/nutritionfacpub](https://digitalcommons.unl.edu/nutritionfacpub)\u003Cbr> Part of the Human and Clinical Nutrition Commons, Molecular, Genetic, and Biochemical Nutrition Commons, and the Other Nutrition Commons |  |\n\nThis Article is brought to you for free and open access by the Nutrition and Health Sciences, Department of at DigitalCommons@University of Nebraska-Lincoln. It has been accepted for inclusion in Nutrition and Health Sciences--Faculty Publications by an authorized administrator of DigitalCommons@University of NebraskaLincoln.  \nSequence analysis  \nGenome mining for anti-CRISPR operons using machine learning  \nBowen Yang 1,‡, Minal Khatri2,‡, Jinfang Zheng 1, Jitender Deogun2, Yanbin Yin  1, * 1 Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska—Lincoln, Lincoln, NE 68508, United States  \n2School of Computing, University of Nebraska, Lincoln, NE 68588, United States  \n*Corresponding author. Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska—Lincoln, 1901 N 21 ST, Lincoln, NE 68588-6205, United [States. E-mail: yyin@unl.edu](States. E-mail: yyin@unl.edu) (Y.Y. )  \n‡These authors are co-first authors Associate Editor: Pier Luigi Martelli  \nAbstract  \nMotivation: Encoded by (pro-)viruses, anti-CRISPR (Acr) proteins inhibit the CRISPR-Cas immune system of their prokaryotic hosts. As a result, Acr proteins can be employed to develop more controllable CRISPR-Cas genome editing tools. Recent studies revealed that known acr genes often coexist with other acr genes and with phage structural genes within the same operon. For example, we found that 47 of 98 known acr genes (or their homologs) co-exist in the same operons. None of the current Acr prediction tools have considered this important genomic context feature. We have developed a new software tool AOminer to facilitate the improved discovery of new Acrs by fully exploiting the genomic context of known acr genes and their homologs.  \nResults: AOminer is the first machine learning based tool focused on the discovery of Acr operons (AOs) . A two-state HMM (hidden Markov model) was trained to learn the conserved genomic context of operons that contain known acr genes or their homologs, and the learnt features could distinguish AOs and non-AOs. AOminer allows automated mining for potential AOs from query genomes or operons. AOminer outperformed all existing Acr prediction tools with an accuracy ¼ 0.85. AOminer will facilitate the discovery of novel anti-CRISPR operons.  \nAvailability and implementation: The webserver is available at: [http://aca. unl.edu/AOminer/AOminer_APP/](http://aca. unl.edu/AOminer/AOminer_APP/. The)[. The](http://aca. unl.edu/AOminer/AOminer_APP/. The) python program is at: [https://](https://)[ ](https://)[github.com/boweny920/AOminer](github.com/boweny920/AOminer).  \n1 Introduction  \nAnti-CRISPR (Acr) proteins have attracted a great attention for its application in genome editing (Bondy-Denomy et al. 2013; Nakamura et al. 2019) . A total of 98 Acr proteins have been experimentally characterized. Notably, most Acrs are orphan genes (Yin and Fischer 2008), as no significant sequence similarity was found between the 98 Acrs. In addition, the 98 known Acrs were shown to inhibit only 11/33 CRISPR-Cas subtypes suggesting that the experimentally characterized Acrs only represent a tiny tip of an iceberg of the possible anti-CRISPR diversity in nature.  \nSix bioinformatics tools are available for automated Acr discovery: AcRanker (Eitzinger et al. 2020), Acr","cbCaiasyCP849a1G","https://ap.wps.com/l/cbCaiasyCP849a1G","pdf",1321451,1,4,"English","en",105,"# Abstract\n## Motivation\n## Results\n## Availability and implementation\n# Introduction\n# Algorithm","[{\"question\":\"What genomic context does AOminer use for anti-CRISPR operon discovery?\",\"answer\":\"AOminer fully exploits the conserved genomic context where known anti-CRISPR genes and their homologs co-localize, including co-existence with other acr genes and neighboring phage or gene-neighborhood features.\"},{\"question\":\"How does AOminer perform prediction?\",\"answer\":\"AOminer trains a two-state hidden Markov model to learn conserved operon context features, allowing it to distinguish anti-CRISPR operons from non-operon regions.\"},{\"question\":\"What inputs are accepted by AOminer?\",\"answer\":\"AOminer accepts FASTA sequences of whole genomes or contigs, as well as individual gene clusters and operons as input.\"}]","Genome mining for anti-CRISPR operons using machine learning | 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genomic context does AOminer use for anti-CRISPR operon discovery?","Question",{"text":74,"@type":75},"AOminer fully exploits the conserved genomic context where known anti-CRISPR genes and their homologs co-localize, including co-existence with other acr genes and neighboring phage or gene-neighborhood features.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does AOminer perform prediction?",{"text":79,"@type":75},"AOminer trains a two-state hidden Markov model to learn conserved operon context features, allowing it to distinguish anti-CRISPR operons from non-operon regions.",{"name":81,"@type":72,"acceptedAnswer":82},"What inputs are accepted by AOminer?",{"text":83,"@type":75},"AOminer accepts FASTA sequences of whole genomes or contigs, as well as individual gene clusters and operons as 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