ModCon: A unified framework for quantifying epitranscriptomic conservation and prioritizing functional RNA modification sites

ModCon is a unified framework for quantifying epitranscriptomic conservation at single-base resolution. Unlike conventional conservation methods that measure nucleotide sequence conservation, ModCon evaluates whether an RNA modification event itself is evolutionarily retained across species. Built from Oxford Nanopore direct RNA sequencing (ONT) datasets across human, mouse, and pig, ModCon integrates four complementary evidence components to generate a unified conservation score for every modified residue. These complementary signals include orthologous modification concordance, within-species recurrence, local sequence-context conservation, and a fine-tuned DNABERT-2 language-model-derived conservation score. The resulting ModCon score (0–1) provides a quantitative measure of evolutionary conservation at RNA modification sites, enabling researchers to prioritize conserved and potentially functional RNA modifications for downstream biological analyses. Dataset Description for ModCon This dataset contains the reference genomes, liftover chains, species-specific baseline experimental data, pretrained models, and training datasets required for ModCon. For comprehensive usage instructions, please visit our GitHub repository: https://github.com/Gang1998c/Modcon/tree/main File Structure and Details: 1. Fasta and Chains:- hg38.fa: Human reference genome (UCSC version, GRCh38)- mm39.fa: Mouse reference genome (UCSC version, GRCm39)- susScr11.fa: Pig reference genome (UCSC version, SGSC Sscrofa11.1) 2. Species Background Data:Includes 1-based coordinate modification sites derived from the Oxford Nanopore Technologies (ONT) pipeline, which are essential for model prediction.- ontdata: All human modified sites.- mouse_raw: All mouse modified sites.- pig_raw: All pig modified sites. 3. Pretrained Sub-base Models:Pretrained base-specific models of ModCon to predict modification conservation levels.- ModCon-A: Pretrained model for the A base.- ModCon-C: Pretrained model for the C base.- ModCon-G: Pretrained model for the G base.- ModCon-U: Pretrained model for the U base. 4. Training Data:Data extracted from negative and positive data pools used for model training.- Model_A_data: Training data for ModCon-A.- Model_C_data: Training data for ModCon-C.- Model_G_data: Training data for ModCon-G.- Model_U_data: Training data for ModCon-U. 5. Code and Pipelines:- master_pipeline.py: Core Python script for forecasting and inference.- server_R: Background R scripts/files integrated into the master pipeline to generate essential Genomic Features.