WGBS analysis of Atlantic silversides
###Chapter 1 (to do: images from fastqc & attach the multiQC report) Processing of fastq files to obtain cytosine reports for input in R -Adapter removal -Hardtrimming -Adjust reference genomes -(bsgenome stuff) -Mapping -Deduplication -Output
###Chapter 2 (to do: ...) What does the data tell us about the methylation level
- Read in the data and metadata
- Problem with insufficient data (binomial)
- CpG coverage
- Number of CpGs Covered at different thresholds
- Output >=2 in most samples
###Chapter 3 ( to do: move local analysis to part 8)
Filtering potential SNPs from methylation data
-Problem with SNPs in WGBS data
-Indications (mean methylation levels)
-Data available
-Analysis
-SNP-free output
###Chapter 4 #Removal of batch effects from methylation data #-Problem with batch effects in WGBS data #-Indications (PCA and mean methylation levels) #-DMRs between batches #-After ComBat #-Indications (PCA and mean methylation levels) #-DMRs between batches => none
###Chapter 5 Exploratory data analysis (PCA and mean methylation levels) -PC1 vs length -lm()
###Chapter 6 Differential methylation analysis (BSseq analysis) -Smoothing of methylation levels -fstat for DMRs between groups incl. fwer #-Remove batch effects from smoothed values - rewrite 2 fstat and tstat #-Rewrite plot functions to include batch adjusted values #-fstat for DMRs between groups incl. fwer -overlaps between DMRs groups and DMRs batches -tstat R1 vs groups -log10 vs pos for each group
-Fst and mean methylation
###Chapter 7 Annotation of mean methylation Annotation of DMRs Table of candidate genes and DMRs Size + methylation correlation
###Chapter 8 genetic structure within groups -D2 chr24 inversion analysis -NN -> NS -> SS t-test -plot DMR including the snp differences.
###Chapter 9 Nanopore chr24 inversion analysis
Methylation calling Mapping to ref genome Whatshap and haplotagging Import to BSseq CpG likelihood filtering - T-test