{"title":"Principle and application of self-transcribing active regulatory region sequencing in enhancer discovery research.","authors":"Ji-Long Wang, Qing Li, Ting-Zheng Zhan","doi":"10.16288/j.yczz.24-149","DOIUrl":"https://doi.org/10.16288/j.yczz.24-149","url":null,"abstract":"<p><p>Self-transcribing active regulatory region sequencing (STARR-seq) is a high-throughput sequencing method capable of simultaneously discovering and validating all enhancers within the genome. In this method, candidate sequences are inserted into plasmid vectors and electroporated into cells. Acting as both enhancers and target genes, the self-transcription of these sequences will also be enhanced by themselves. By sequencing the transcriptome and comparing the results with the non-inserted control, the locations and activity of enhancers can be determined. In traditional enhancer discovery strategies, the chromatin open regions and transcription active regions were sequenced and predicted as enhancers. However, the activity of these putative enhancers could only be validated one by one without a high-throughput method. STARR-seq solved this limitation, allowing simultaneous enhancers discovery and activity validation in a high-throughput manner. Since the introduction of STARR-seq, it has been widely used to discover enhancers and validate enhancer activity in a number of organisms and cells. In this review, we present the traditional enhancer prediction methods and the basic principles, development history, specific applications of STARR-seq, and its future prospects, aiming to provide a reference for researchers in related fields conducting enhancer studies.</p>","PeriodicalId":35536,"journal":{"name":"Yi chuan = Hereditas / Zhongguo yi chuan xue hui bian ji","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Inference and prioritization of tissue-specific regulons in Arabidopsis and Oryza","authors":"Honggang Dai, Yaxin Fan, Yichao Mei, Ling-Ling Chen, Junxiang Gao","doi":"10.1007/s42994-024-00176-2","DOIUrl":"10.1007/s42994-024-00176-2","url":null,"abstract":"<div><p>A regulon refers to a group of genes regulated by a transcription factor binding to regulatory motifs to achieve specific biological functions. To infer tissue-specific gene regulons in <i>Arabidopsis</i>, we developed a novel pipeline named InferReg. InferReg utilizes a gene expression matrix that includes 3400 <i>Arabidopsis</i> transcriptomes to make initial predictions about the regulatory relationships between transcription factors (TFs) and target genes (TGs) using co-expression patterns. It further improves these anticipated interactions by integrating TF binding site enrichment analysis to eliminate false positives that are only supported by expression data. InferReg further trained a graph convolutional network with 133 transcription factors, supported by ChIP-seq, as positive samples, to learn the regulatory logic between TFs and TGs to improve the accuracy of the regulatory network. To evaluate the functionality of InferReg, we utilized it to discover tissue-specific regulons in 5 <i>Arabidopsis</i> tissues: flower, leaf, root, seed, and seedling. We ranked the activities of regulons for each tissue based on reliability using Borda ranking and compared them with existing databases. The results demonstrated that InferReg not only identified known tissue-specific regulons but also discovered new ones. By applying InferReg to rice expression data, we were able to identify rice tissue-specific regulons, showing that our approach can be applied more broadly. We used InferReg to successfully identify important regulons in various tissues of <i>Arabidopsis</i> and <i>Oryza</i>, which has improved our understanding of tissue-specific regulations and the roles of regulons in tissue differentiation and development.</p></div>","PeriodicalId":53135,"journal":{"name":"aBIOTECH","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}