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EpiMOLAS: An Intuitive Web-based Framework for Genome-Wide DNA Methylation Analysis
- 1. EpiMOLAS: An Intuitive Web-based Framework for Genome-wide DNA Methylation Analysis Presented By Sheng-Yao Su Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica Institute of Biomedical Informatics, National Yang-Ming University TAIWAN Sep 10, 2019
- 2. Outline • Introduction • Methods • Implementations and Results • EpiMOLAS consists of DocMethyl and EpiMOLAS_web • Discussion • Conclusion
- 3. Introduction
- 4. Epigenomics • Epi- (upon, above, beyond) genomics (DNA sequence) • Waddington proposed this term in 1940s. • Epigenomics is the study of the complete set of epigenetic modification on the genetic material of a cell (wiki)
- 6. DNA methylation – an epigenetic mark of cellular memory DNA methylation: an epigenetic mark of cellular memory Experimental & Molecular Medicine volume 49, page e322 (2017)
- 8. Sodium Bisulfite treatment Correct conversion : C -> U -> T Correct conversion : mC -> mC -> C incorrect conversion : mC -> U -> T Bisulfite treatment PCR amplification Unmethylated DNA Methylated DNA Original sequence CCGTCGACGT CmCGTmCGAmCGT Bisulfite converted UUGTUGAUGT UmCGTmCGAmCGT PCR product TTGTTGATGT TCGTCGACGT Incomplete conversion
- 9. Detect DNA modification changes • Bisulfite conversion treatment • Reduced Representation Bisulfite Sequencing (RRBS) • Whole Genome Bisulfite Sequencing (WGBS) • Bisulfite-free • Anti-methylcytosine Antibody • Methyl-CpG binding domain (MBD) • Chemical labeling (MeFISH) • Methylation-sensitive restriction enzyme • Electrochemical oxidation • Third Generation (SMRT-seq, Nanopore)
- 10. Methods
- 11. Generic bioinformatic analysis workflow for bisulfite sequencing data Seq Reads Quality Control Alignment Methylation Call Visualization Annotation Diff. Methyl. Region Biomarker Candidates
- 12. Flowchart of EpiMOLAS Biomarker CandidatesSeq Reads Trim Galore! FastQC Bowtie Visualization Annotation Bismark Extract. EpiMOLAS_webDocMethyl Bismark mtable
- 13. Metric for Methylation Profiling - mtable Gene Genome C C C C at least four counts of methylated and unmethylated cytosine at least five qualified observed cytosines 1 16425704 + 0 8 CHH CTC 1 16425710 + 6 6 CHG CAG 1 16425714 + 10 5 CHH CAA 1 16425717 + 6 0 CHG CTG 1 16425719 + 4 0 CG CGC Bismark genome-wide cytosine report Sequence depth Input Output EpiMolas.jar CG CHG CHH Su et al. TEA: the epigenome platform for Arabidopsis methylome study. BMC Genomics 17(Suppl 13): 1027 (2016)
- 14. An Example of mtable Ensembl Gene ID Methylation level of gene body and promoter regions according to three cytosine methylation contexts less than five qualified observed cytosines
- 17. Architecture of DocMethyl and EpiMOLAS_web
- 18. DocMethyl • Docker • Galaxy Infrastructure Operating System Docker Daemon Galaxy platform TrimGalore FastQC Bismark EpiMolas.jar Workflow mtable Methylation Report Raw data Input DocMethyl DocMethyl output QC Report Trimmed Data Reference Genome Gene Annotation
- 19. A Workflow In DocMethyl Trim Sequences Check QC of Trimmed reads Map Reads on Genome Extract Methylated Cytosines Generate Output of Submission to EpiMOLAS_web • Trim Galore • FastQC • Bismark • EpiMolas.jar
- 20. Steps and Output Files of the Workflow
- 21. Full text Search DMGs (select diff methylation Genes) mC Threshold Import Genelist KEGG Global View Gene List Analysis Generate New Gene List for further Analysis in Built-in Approaches Modules Inside EpiMOLAS_web
- 22. Find Genes of Interest
- 23. Gene Profile
- 25. Visualization Modules • Boxplot • Circos plot • Heatmap • Potein network
- 26. Discussion
- 27. Discussion • It is hard to find the significant DMG according to DMG approaches. Long region of gene size in length amortize the effect of DNA methylation. • Approximately 80% of all CpGs are located in repetitive sequences and centromeric repeat regions of chromosomes, and are heavily methylated. • We list the comparison among several platforms and tools for genome-wide DNA methylation analysis.
- 28. Comparison of each platform EpiMOLAS BAT ENCODE -WGBS snakePipe NGI- MethylSeq Mint RnBeads 2.0 MethylPipe MethylSig Methylkit Environment Docker, Galaxy, Web server Docker Shell script Bioconda Snakemake Docker Nexflow Galaxy R package R package R package R package Sequence context CG, CHG, CHH CG CG, CHG, CHH CG, CHG, CHH CG, CHG, CHH CG CG CG, CHG, CHH CG, CHG, CHH CG, CHG, CHH Start with raw reads raw reads raw reads raw reads raw reads raw reads Methyl. Call file Methyl. Call file Methyl. Call file Methyl. Call file Docker Container + + – – + – NA NA NA NA Web interface + (Galaxy) – + – – + (Galaxy) NA NA NA NA Adapter and base quality trimming + – + + + + – NA NA NA QC report + + + + + + + NA NA NA Read mapping + + + + + + – NA NA NA Methylation sites calling + + + + + + – NA NA NA
- 29. EpiMOLAS BAT ENCODE -WGBS snakePipe NGI- MethylSeq Mint RnBeads 2.0 MethylPipe MethylSig Methylkit Discriptive statistics + + – + + + + + + + Find DMRs + (simple) + (metilene) – + (metilene) – + (DSS) + + + + Clustering analysis + (heatmap) + (heatmap) – + (heatmap) – – + (heatmap) – + (heatmap) – GO term enrichment + – – – – + + + – – KEGG pathway enrichment + – – – – – – – – – TFBS enrichment – – – – – – – – + – Genome- wide visualization + (circos plot) + (circos plot) – – – – + – – – Interactive Quantitative analysis + – – – – – + (R Shiny) NA NA NA Data browing and retrieving UI + – – – – – + (R Shiny) NA NA NA
- 30. EpiMOLAS BAT ENCODE -WGBS snakePipe NGI- MethylSeq Mint RnBeads 2.0 MethylPipe MethylSig Methylkit Gene list with tracking logs + – – – – – NA NA NA NA Venn analysis on gene lists + – – – – – NA – – – Interplay with other high throughput data protein Interactome transcript ome – RNA-seq, ChIP-seq, ATAC-seq, Hi-C etc. – 5- hmc – RNA-seq, ChIP-seq, Dnase-seq – –
- 31. Conclusion
- 32. Conclusion • We present an integrated two-phase web-based ‘gene-centric’ framework for WGBS data from raw data processing to downstream analysis. • EpiMOLAS helps users deal with their WGBS data and alleviates the burden on conducting reproducible analysis of public datasets.
- 35. Thank you for your attention ! Photo by KageHuang/Getty Images