{"title":"Be-dataHIVE: a base editing database.","authors":"Lucas Schneider, Peter Minary","doi":"10.1186/s12859-024-05898-0","DOIUrl":null,"url":null,"abstract":"<p><p>Base editing is an enhanced gene editing approach that enables the precise transformation of single nucleotides and has the potential to cure rare diseases. The design process of base editors is labour-intensive and outcomes are not easily predictable. For any clinical use, base editing has to be accurate and efficient. Thus, any bystander mutations have to be minimized. In recent years, computational models to predict base editing outcomes have been developed. However, the overall robustness and performance of those models is limited. One way to improve the performance is to train models on a diverse, feature-rich, and large dataset, which does not exist for the base editing field. Hence, we develop BE-dataHIVE, a mySQL database that covers over 460,000 gRNA target combinations. The current version of BE-dataHIVE consists of data from five studies and is enriched with melting temperatures and energy terms. Furthermore, multiple different data structures for machine learning were computed and are directly available. The database can be accessed via our website https://be-datahive.com/ or API and is therefore suitable for practitioners and machine learning researchers.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"25 1","pages":"330"},"PeriodicalIF":2.9000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11476525/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s12859-024-05898-0","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Base editing is an enhanced gene editing approach that enables the precise transformation of single nucleotides and has the potential to cure rare diseases. The design process of base editors is labour-intensive and outcomes are not easily predictable. For any clinical use, base editing has to be accurate and efficient. Thus, any bystander mutations have to be minimized. In recent years, computational models to predict base editing outcomes have been developed. However, the overall robustness and performance of those models is limited. One way to improve the performance is to train models on a diverse, feature-rich, and large dataset, which does not exist for the base editing field. Hence, we develop BE-dataHIVE, a mySQL database that covers over 460,000 gRNA target combinations. The current version of BE-dataHIVE consists of data from five studies and is enriched with melting temperatures and energy terms. Furthermore, multiple different data structures for machine learning were computed and are directly available. The database can be accessed via our website https://be-datahive.com/ or API and is therefore suitable for practitioners and machine learning researchers.
期刊介绍:
BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology.
BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.