Revision as of 22:08, 11 April 2014 by >Zsakura2
WGBS of mouse SCNT and iPSC (collaborate with Yang Xu lab)
- The goal of this project is to examine the methylation pattern of mouse SCNT and iPSC against mESC using whole genome bisulfite sequencing, and to characterize epigenetic signatures that resulted from different stem cell reprogramming methods.
- detailed experimental procedures can be found here: [[1]]
sample ID
|
N2 Index used
|
cell type
|
progenitor line
|
progenitor/donor mouse strain
|
2A4F1 |
N2 index 1 |
iPSC |
N/A (used TB_B6MEF in exome study) |
C57BL/6J
|
2A4F33 |
N2 index 2 |
iPSC |
N/A (used TB_B6MEF in exome study) |
C57BL/6J
|
miPS B3 |
N2 index 4 |
iPSC |
ZN_B6MEF |
C57BL/6J
|
1E12P20 |
N2 index 5 |
iPSC |
TB_B6MEF |
C57BL/6J
|
nt ES P7 C |
N2 index 6 |
SCNT |
B6MEF P0-3 |
C57BL/6J
|
nt ES P8 B |
N2 index 7 |
SCNT |
B6MEF P0-3 |
C57BL/6J
|
NB 3 |
N2 index 9 |
SCNT |
B6-P1-1 |
C57BL/6J
|
SCNT B12 |
N2 index 10 |
SCNT |
B6-P1-2 |
C57BL/6J
|
B6 ES |
N2 index 11 |
ES |
- |
C57BL/6J
|
129 ES |
N2 index 12 |
ES |
- |
-
|
Sequencing statistics
- Data Source
- The samples were sequenced in the following runs:
- Hiseq 130104_SN1001
- GAIIx 121226_HL140 (lane 1 only)
Sample ID
|
N2 Index
|
total reads
|
mapped reads
|
mapping rate
|
total # of CpG sites covered (coverage >=5)
|
mean coverage (sites with coverage >=5)
|
mean methylation level (sites with coverage >=5)
|
2A4F1 |
1 |
375578848 |
283699999 |
76% |
17510624 |
12.5 |
0.76
|
2A4F33 |
2 |
360720634 |
253128940 |
70% |
16863956 |
11.3 |
0.74
|
mipsB3 |
4 |
342349949 |
211416203 |
62% |
15215962 |
9.8 |
0.71
|
1E12P20 |
5 |
328678606 |
227406985 |
69% |
15865114 |
10.6 |
0.73
|
nt ES P7C |
6 |
315952780 |
205976115 |
65% |
15025471 |
9.6 |
0.57
|
nt ES P8B |
7 |
461470678 |
333879642 |
72% |
17852991 |
12.1 |
0.61
|
NB3 |
9 |
297479356 |
205398733 |
69% |
14210572 |
9.3 |
0.79
|
SCNTB12 |
10 |
322193316 |
228779506 |
71% |
16016176 |
11.1 |
0.73
|
B6ES |
11 |
310311719 |
214278139 |
69% |
15464407 |
10.5 |
0.74
|
129ES |
12 |
271922039 |
163956125 |
60% |
7433945 |
7.7 |
0.64
|
Pre-processing scripts
- data and processed files can be found on meangenemachine:/home/zhl002/ZL_1TB
- Dinh also has processed files on genome-miner:/media/LTS_15T/Dinh_LTS/WGBS/SCNT_mm9/Soap
Mapping and calculate MethylFreq
perform mapping using SOAP
filtering and processing of alignments using SAMtools and Picard
then calculate methylation frequency
make BED format files
/home/dinh/scripts/methylFreq2BED.pl
TO run:
/home/dinh/scripts/methylFreq2BED.pl [sample_name] [min_depth] < [methylFreq file] > Sample_name.bed.txt
Quality control: calculate forward and reverse correlation
/home/dinh/scripts/frMethylCorr.pl
TO run:
/home/dinh/scripts/frMethylCorr.pl [min_depth] < [methylFreq file] > Sample_name.corr
generate methylation matrix
/home/dinh/scripts/allBED2Matrix_Jan25.pl
TO run:
/home/dinh/scripts/allBED2Matrix_Jan25.pl [list_bed] [min_sample] [min_depth] [windowSize >= 1]
Initial analysis: using BSmooth and T-stat
perform smoothing on the data using BSmooth
BSmooth R script
bsseq package usage and description: Link
UserGuide
TO run:
script_dir="/media/1TB_storeB/ZL_meangenemachine/Habibi_smoothing"
bed="/media/1TB_storeB/ZL_meangenemachine/BEDfiles"
for c in chr#
do -> for f in list_of_file_names
do -> grep $c [space]$bed/$f.bed.txt > $f.$c
for ns in number_CpG_sites
do -> for h in min_window_size
do -> sed "s/TESTNS/$ns/g" runSmoothing.R | sed "s/TESTH/$h/g" | sed "s/CHROM/$c/g" > run.R
output files:
file_name.chr#
chr#.ns#_h#.rda
<bold>perform T-statistics on smoothed data</bold>
Tstat R script
TO run:
for f in *rda
do -> sed "s/RDA/$f/g" runTstat.R > Run.R
output files:
chr#.ns#_h#.rda_comparison_coveredsites
chr#.ns#_h#.rda_comparison_DRMs.txt
chr#.ns#_h#.rda_comparison_plotDMRs.pdf
highlight:
group1 and group2 contain the sample names of the two groups being compared
estimate.var describes which sample are being used to estimate variability, and choices are:
gr# (e.g normal sample when compared again cancer data)
same(assume same variability in each group)
paired(do a paired T-test)
In my analysis, I used gr2 to estimate variability, which are the ES lines
<bold>plotting the DMRs</bold>
T-stat will generate txt file that identified potential DMRs between two group of samples
Dinh modified the methyl matrix perl script which allows it to use chr_interval as input and generate matrix only within the interval specified
on meangenemachine: /home/zhl002/ZL_1TB/allBED2Matrix_Jan222014.pl
TO run: perl allBED2Matrix_Jan222014.pl [list_bed] [min_sample] [min_depth] [windowSize >= 1] chr#:#-#
output: MethylMatrix_bed_list.min#.md#.win#
Then use plotting DMR R script to generate the plot
use vim to change the first line of the script to use the output matrix that we just generated from last step
preliminary analysis
used previously published WGBS on ES cell cultured under 2i and serum conditions Link
do BSmooth on all the data: ns=20 and h=2000 (20 CpG sites in the window that range from minimum =2000 and maximum =10,000)
then run Tstat to do the comparison:
SCNT-serum (indx 9 and 10) against ES-serum (indx11 and Habibi_ES_serum) (ES serum is group2 and also used to estimate variability)
iPSC-serum (indx 1, 2, 4, 5) against ES-serum (indx11 and Habibi_ES_serum) (ES serum is group2 and also used to estimate variability)
SCNT-2i (indx 6 and 7) against ES-serum (Habibi_ES_2i_adapted and Habibi_ES_2i) (ES 2i is group2 and also used to estimate variability)