Additional file 1 of EpiGePT: a pretrained transformer-based language model for context-specific human epigenomics
Creators: name: Gao, Zijing
name: Liu, Qiao
name: Zeng, Wanwen
name: Jiang, Rui
name: Wong, Wing Hung
Publisher: name: figshare
Year: 2024
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 1: Text S1: Data splitting strategy for model training. Text S2: Data processing for ChromHMM annotation data. Text S3: Details of the comparison matrix in functional chromatin status prediction experiments. Text S4: Implementation of Enformer model and Enformer+. Text S5: Description and comparison of EpiGePT with different settings. Text S6: Guidance on how to use EpiGePT in two scenarios without tissue-specific TFs data. Text S7: System design and implementation of the web server. Text S8: Case application of the EpiGePT-online. Text S9: Conditions and data preparation for retraining the EpiGePT model on other species. Text S10: Running time of the EpiGePT and baseline methods. Text S11: Computational resources used to train the model. Fig S1: Model architecture of EpiGePT for multiple epigenomic signals prediction. Fig. S2: Three data partitioning strategies for model training and testing. Fig. S3: EpiGePT's performance in predicting DNase-seq and other epigenetic signals. Fig. S4: Comparison of cross-cell-type chromatin accessibility (DNase-seq profiles) prediction performance between EpiGePT and baseline methods. Fig. S5: Performance of EpiGePT and baseline methods on chromatin states classification and causal variants classification. Fig. S6: The average predictive performance of the remaining methods when each model serves as the ground truth. Fig. S7: The performance comparison of retrained Enformer with different numbers of encoder layers for predicting chromatin accessibility. Fig. S8: Performance of EpiGePT in cross-cell-type prediction. Fig. S9: Zero-shot learning of EpiGePT on chromatin accessibility (DNase-seq) prediction on mouse data. Fig. S10: Model fine-tuning EpiGePT on chromatin accessibility (DNase-seq) prediction on mouse data. Fig. S11: The cross-chromosome predictive performance of pretrained EpiGePT and EpiGePT-seq after fine-tuning on mouse data. Fig. S12: The performance (auROC) of attention score of EpiGePT in distinguishing regulatory element-gene pairs at different distance ranges. Fig. S13: Incorporating 3D genomic information from HiChip data enhances the predictive performance of EpiGePT on E-P regulatory interaction on K562 cell line. Fig. S14: The fine-tuning performance of the EpiGePT model on predicting potential enhancer-promoter regulatory networks. Fig. S15: The ROC and PR curves of the EpiGePT model on predicting potential enhancer-promoter regulatory networks. Fig. S16: Comparison of the performance between fine-tuned EpiGePT-3D, EpiGePT and baseline methods in predicting HiChIP loops (ROC curves). Fig. S17: Comparison of the performance between fine-tuned EpiGePT-3D, EpiGePT and baseline methods in predicting HiChIP loops (PR curves). Fig. S18: The performance comparison of original Enformer with EpiGePT and EpiGePT-3D on 78 genomic tracks. Fig. S19: The GIS of ChIP-seq overlapped bins versus non-overlapped bins of POU5F1 centered at the TSS of ESRRB. Fig. S20: Gene ontology enrichment analysis based on the top 5% TFs with high expression in ESCs. Fig. S21: Enrichment result (Cellular component and Molecular function) of the nearest genes of the COVID-19 associated SNPs with the low LOS. Fig. S22: Ablation analysis of the EpiGePT model. Fig. S23: The simulation evaluated the changes in PCC for cross-cell type predictions under different numbers of missing TF profiles with three replacement strategies. Fig. S24: In three sets of similar cell types, the changes in predictive performance of eight types of epigenomic signals were simulated under different replacement strategies (similar, mean, median) when TF profiles were missing. Fig. S25: In the scenario of two TF profiles from the same cell type but different samples, the changes in DNase-seq predictive performance are simulated when missing TF profiles are replaced with the TF profile from a different sample. Fig. S26: The pie charts illustrate the distribution of RNA-seq data from the ENCODE project. Fig. S27: The pie charts illustrate the distribution of human and mouse single-cell RNA-seq data across various tissues from the CELLxGene database. Fig. S28: The pie charts illustrate the distribution of human and mouse single-cell RNA-seq data across various cell types from the CELLxGene database. Fig. S29: The Pearson correlation coefficient (PCC) for predicting chromatin accessibility (DNase-seq) under different numbers of training cell lines/tissues. Fig. S30: The Spearman correlation coefficient (SCC) for predicting chromatin accessibility (DNase-seq) under different numbers of training cell lines/tissues. Fig. S31: Case application of the EpiGePT-online. Fig. S32: The overlap between human and plant gene annotations from the Ensembl Plant project in terms of the 711 selected TFs. Fig. S33: The computational resource usage, including peak GPU memory and time for training an epoch on an NVIDIA GeForce RTX 4090.