Yaamini’s Notebook: DML Analysis Part 14

Tiling window analysis in methylKit

Now that I’m using mincov = 3, I wanted to try the tiling window analysis in methylKit. I added code for the analysis to the end of this R Markdown file.

A tiling window analysis will allow me to identify differentially methylated regions to complement the differentially methylated loci I previously identified. The analysis takes a given window (default is 1000 bp) and adds the number of C and T from each covered cytosine, and spits out a total number of C and T for each tile. I then takes a step (default is 1000 bp) and peforms the same analysis on the next tile.

I chose to do this analysis with three different window-step size combinations:

1. 100 bp window and step size
2. 1000 bp window and step size (the methylKit default)
3. 1000 bp window and 100 bp step size

Figures 1-3. Full sample CpG methylation clustering for 1) 100 bp window and step size 2) 1000 bp window and step size and 3) 1000 bp window and 100 bp step size.

Figures 4-6. PCA of full sample methylation for 1) 100 bp window and step size 2) 1000 bp window and step size and 3) 1000 bp window and 100 bp step size.

These clustering plots are slightly different from same kind associated with DMLs:

Figures 7-8. Full sample CpG methylation clustering and PCA of full sample methylation using mincov = 3.

When calcuating differential methylation, I didn’t get any error about a glm not fitting. I did get an error when I was calculating differential methylation for individual loci.

Table 1. Window size, step size, total number of regions produced, and the number of DMLs that were at least 50% different between treatment and control samples. The number of regions and siginificantly different DMRs seem to be dictated by the window size, and not the step size.

Window Size (bp)	Step Size (bp)	Total Regions	Number of Significantly Different DMRs
100	100	217538	162
1000	1000	104144	118
1000	100	104144	118

Going forward

I think it makes sense to have smaller-sized DMRs, so I’d rather continue wiht the 100 bp windows and steps. The 100 bp window and step PCA also has the best separation between ambient and treatment samples. I posted this issue to get Steven’s opinion.

// Please enable JavaScript to view the comments powered by Disqus.

from the responsible grad student https://ift.tt/2yNOShi
via IFTTT

Shelly’s Notebook: Thur. Oct 18, 2018

Global DNA methylation quantification of:

1. Triploid and diploid C. gigas mantle tissue control and heat shocked Ronit’s samples
2. C. gigas tripliod tissues from Nisbet Oyster Co. See Sam’s notebook entry for more info on origins
3. Sea lice . See Sam’s notebook entry and Sam’s other notebook entry

I ran the MethylFlash Global DNA Methylation (5-mC) ELISA Easy Kit (Coloritmetric) assay following the manufacturer’s instructions. I noted any modifications I made in the manufacturer’s instructions and below.

• I ran 3 columns because I had 14 samples + a control column.
• I first diluted my DNA samples to be at similar concentrations so I could add 100ng/well. There was one sample, the sea lice female #2 that had a low concentration so I only added 50ng of that one. See googlesheet for more details.
• I made 40mL of WB (4mL 10x WB and 36mL nanopure H2O)
• I used 3mL of BS and added 100uL to each well before adding DNA
• I added 2uL of each standed to the first column, including 2uL of the non-diluted PC, following the plate map tab in the googlesheet
• I added 100ng of sample DNA (except for sea lice female #2 where 50ng DNA was added) to each well following the plate map. The volume and location of DNA sample added to the plate is noted in the googlesheet in the ‘Conc_and_vols’ tab.
• I used the incubator in rm 228 set at 42C because that was actually 37C.
• I made 1.5mL DSC with 1.5mL 1xWB + 1.5uL mcAB + 1.5uL SI + 0.75uL ES, vortexed briefly, did a quick spin.
• I did a total of 6 washes with 1xWB after incubation with DCS because I had extra 1xWB.
• I used ~3mL of DS, with 100uL/well and incubated for 4 min. This is a pic of the plate and color change after 3 minutes. More blue = more methylation.
  
• After 4 minutes, I added 130ul/well of SS on top of the DS. I was only suppose to add 100uL, but oh well. I don’t think it would affect anything, just ensure the rxn is stopped. I let the reaction stop for 2 minutes and then brought it down the MERLAB (Seebs) in MAR to use their plate reader. I used Sam’s program for absorbances @450nm. I read the plate 3 times:
  • raw data file 1
  • raw data file 2
  • raw data file 3
• I copied the plate data into Excel and saw three readings did not differ much (maximally +/- 0.01) so I just went with the first plate reading to generate the standard curve. See ‘DataAndStdCrv’ tab inMethylFlashELISA_StdCrv_and_Data_Analysis.xlsx
• This is the rough % methylation for each sample. I say rough because I did not run any samples in replicate. See ‘MethylationClacs’ tab in MethylFlashELISA_StdCrv_and_Data_Analysis.xlsx for calculations and pretty table with sample descriptions.

Sample % Global DNA Methylation
D1 2.481388664
D2 2.983813902
D9 1.959614718
D10 1.515945492
blank -0.116606537
blank -0.016736149
sea lice 1 0.559509162
sea lice 2 3.377541298
4G 7.024617647
4C 1.229102835
4Ms 2.715071952
4M 4.572973188
T1 2.209976732
T2 2.456263803
T9 0.527919227
T10 0.878520269

Conclusions:
It seems like control diploids and triploids have higher methylation than heat stressed diploids and triploids, with heat stressed triploids showing less methylation than heat stressed diploids. It seems like there is so variation in methylations levels in different triploid tissues. The sea lice individuals show differences in methylation which is interesting.