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a catalog of worldwide biological databases

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to the 3'-untranslated regions of target mRNAs, influencing various biological processes at the post-transcriptional level. Identifying miRNA transcription start sites (TSSs) and transcription factors' (TFs) regulatory roles is crucial for elucidating miRNA function and transcriptional regulation. miRStart 2.0 integrates over 4500 high-throughput datasets across five data types, utilizing a multi-modal approach to annotate 28 828 putative TSSs for 1745 human and 1181 mouse miRNAs, supported by sequencing-based signals. Over 6 million tissue-specific TF-miRNA interactions, integrated from ChIP-seq data, are supplemented by DNase hypersensitivity and UCSC conservation data, with network visualizations. Our deep learning-based model outperforms existing tools in miRNA TSS prediction, achieving the most overlaps with both cell-specific and non-cell-specific validated TSSs. The user-friendly web interface and visualization tools make miRStart 2.0 easily accessible to researchers, enabling efficient identification of miRNA upstream regulatory elements in relation to their TSSs. This updated database provides systems-level insights into gene regulation and disease mechanisms, offering a valuable resource for translational research, facilitating the discovery of novel therapeutic targets and precision medicine strategies. miRStart 2.0 is now accessible at https://awi.cuhk.edu.cn/∼miRStart2.

MicroRNAs (miRNAs) are critical small non-coding RNAs that regulate gene expression by hybridizing to the 3'-untranslated regions (3'-UTR) of target mRNAs, subsequently controlling diverse biological processes at post-transcriptional level. How miRNA genes are regulated receives considerable attention because it directly affects miRNA-mediated gene regulatory networks. Although numerous prediction models were developed for identifying miRNA promoters or transcriptional start sites (TSSs), most of them lack experimental validation and are inadequate to elucidate relationships between miRNA genes and transcription factors (TFs). Here, we integrate three experimental datasets, including cap analysis of gene expression (CAGE) tags, TSS Seq libraries and H3K4me3 chromatin signature derived from high-throughput sequencing analysis of gene initiation, to provide direct evidence of miRNA TSSs, thus establishing an experimental-based resource of human miRNA TSSs, named miRStart. Moreover, a machine-learning-based Support Vector Machine (SVM) model is developed to systematically identify representative TSSs for each miRNA gene. Finally, to demonstrate the effectiveness of the proposed resource, an important human intergenic miRNA, hsa-miR-122, is selected to experimentally validate putative TSS owing to its high expression in a normal liver. In conclusion, this work successfully identified 847 human miRNA TSSs (292 of them are clustered to 70 TSSs of miRNA clusters) based on the utilization of high-throughput sequencing data from TSS-relevant experiments, and establish a valuable resource for biologists in advanced research in miRNA-mediated regulatory networks.