Welcome to ac4C Info database. ac4C Info is a database platform dedicated to collecting N4 acetylytidine (ac4C) modifications in RNA from all species. At present, it is the first version, mainly consisting of four parts:the ac4CDB database, which includes the ac4C modification site information identified by ac4C-seq single base resolved detection technology for ac4C RNA acetylation modification, and the distribution area information of ac4C in the transcriptome analyzed by acetylated RNA immunoprecipitation (acRIP-seq) technology; The ac4CDiseaseDB database contains germline variations extracted from dbSNP, 1000 Genomes, and Ensembl, as well as disease related ac4C SNPs (involving the addition or loss of ac4C sites) identified from somatic variations of 33 different human cancer types collected from the Cancer Genome Atlas (TCGA); And two analysis modules for real-time interactive analysis of ac4C modification site prediction (ac4C Predictor) and evaluation of the impact of genetic variation on ac4C modification (ac4C SNP IFer). We hope that ac4C Info can become a comprehensive and valuable resource platform for sharing, centralizing, annotating, and customizing analysis of ac4C data.
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ac4CDB
DatabaseA curated database of ac4C sites identified by high-throughput sequencing, with 1,847,660 validated entries across nine species. Supports epigenetic and functional genomics research.
ac4CDiseaseDB
DatabaseA meticulously curated database links ac4C modifications with genetic variations, encompassing associations with multiple diseases and their corresponding levels of evidence.
Help
Open AccessInstructions for using every part of this website
ac4C Predictor
Analysis ToolDeep learning model predicts potential ac4C sites with high accuracy. Submit your sequences for analysis and get detailed results.
ac4C SNP Influencer(IFer)
Analysis ToolA sophisticated computational tool designed to predict the impact of genetic variants on ac4C modification sites. Enables comprehensive analysis of user-provided SNPs through an ensemble of nine rigorously trained machine learning models.
Download
Open AccessDownload the complete dataset in compressed format (. rar).