Revision as of 01:43, 7 March 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
|
Pipeline
- 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
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]
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/$c/g" > run.R
output files:
file_name.chr#
chr#.ns#_h#.rda
perform T-statistics on smoothed data
Tstat R script
TO run:
for f in *rda
do -> sed "s/RDA/$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
plotting the DMRs
T-stat will generate txt file that identified potential DMRs between two different group that you have specified
preliminary analysis