Taking the oysters to bed

Having previously taken a look at eastern oysters in OA to identify DMLs, here I attempt to take those data, redescribe and generate beds. TLDR: https://github.com/epigeneticstoocean/2018_L18-adult-methylation/tree/main/igv

Controls

(Trying to ID any sex differences)

nb: https://github.com/epigeneticstoocean/2018_L18-adult-methylation/blob/main/code/03.1-methykit.Rmd

When setting threshold to

# get hyper methylated bases (0 is male, 1 female)
myDiff_c75p.hyper=getMethylDiff(myDiff_c,difference=75,qvalue=0.01,type="hyper")
#
# get hypo methylated bases
myDiff_c75p.hypo=getMethylDiff(myDiff_c,difference=75,qvalue=0.01,type="hypo")
#
# get all differentially methylated bases
myDiff_c75p=getMethylDiff(myDiff_c,difference=75,qvalue=0.01)

Comparison	DMLs	Hyper	Hypo	threshold
Sex	15000	6	14994	75%

Getting the bed was not so straight forward …

sex_dml <- dplyr::select(myDiff_c75p.tab, chr, start, end, meth.diff) %>%
  mutate(start = start -1) %>%
  mutate_if(is.numeric, as.integer) %>%
  mutate(TYPE ="sex_DML") %>%
  select(chr, start, end, TYPE, meth.diff)

write.table(sex_dml, file = "../analyses/sex_dml.bed", sep = "\t", row.names = FALSE, quote = FALSE)

file = https://raw.githubusercontent.com/epigeneticstoocean/2018_L18-adult-methylation/main/analyses/sex_dml.bed

OA comparison

(ignoring sex)

Comparison	DMLs	Hyper	Hypo	threshold
OA	10	5	5	50%

file = https://raw.githubusercontent.com/epigeneticstoocean/2018_L18-adult-methylation/main/analyses/oao_dml.bed

Combined comparison

(Four conditions)

Comparison	DMLs	Hyper	Hypo	threshold
combined (4 conditions)	78205	78205	0	50%

File = https://raw.githubusercontent.com/epigeneticstoocean/2018_L18-adult-methylation/main/analyses/4cond_dml.bed

Written on December 1, 2021