Revision as of 01:06, 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 and sequencing summary can be found here: [[1]]
sample ID
|
N2 Index used
|
cell type
|
progenitor line
|
progenitor/donor mouse strain
|
2A4F1 |
N2 index 1 |
iPSC |
B6MEF (we don't have the original sample) |
C57BL/6J
|
2A4F33 |
N2 index 2 |
iPSC |
B6MEF (we don't have the original sample) |
C57BL/6J
|
miPS D1 |
N2 index 3 (not sequenced) |
iPSC |
B6MEF |
C57BL/6J
|
miPS B3 |
N2 index 4 |
iPSC |
B6MEF |
C57BL/6J
|
1E12P20 |
N2 index 5 |
iPSC |
B6MEF |
C57BL/6J
|
nt ES P7 C |
N2 index 6 |
SCNT |
B6MEF P0-3 |
C57BL/6N
|
nt ES P8 B |
N2 index 7 |
SCNT |
B6MEF P0-3 |
C57BL/6N
|
NB 1 |
N2 index 8 (not sequenced) |
SCNT |
B6-P1-1 |
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
|
Sequencing statistics from lane 1 of GA run 121226_HL140:
Sample IDs
|
Experiment
|
Index number
|
Total reads
|
Mapped reads
|
Mapping rate
|
Conversion rate*
|
Ave. coverage
|
2A4F1 |
WGBS |
Indx01 |
453017 |
328106 |
0.724268626 |
0.98 |
10.6
|
2A4F33 |
WGBS |
Indx02 |
409103 |
293128 |
0.716513934 |
0.984 |
9.4
|
miPS B3 |
WGBS |
Indx04 |
406101 |
278669 |
0.686206141 |
0.989 |
7.8
|
1E12P20 |
WGBS |
Indx05 |
375114 |
256596 |
0.684048049 |
0.988 |
8.4
|
nt ES P7 C |
WGBS |
Indx06 |
359991 |
250016 |
0.694506252 |
0.99 |
7.6
|
nt ES P8 B |
WGBS |
Indx07 |
517166 |
362510 |
0.700954819 |
0.991 |
12.3
|
NB 3 |
WGBS |
Indx09 |
397456 |
279016 |
0.70200475 |
0.986 |
7.9
|
SCNT B12 |
WGBS |
Indx10 |
362114 |
250918 |
0.692925432 |
0.982 |
8.5
|
B6 ES |
WGBS |
Indx11 |
352934 |
245005 |
0.694194949 |
0.986 |
7.9
|
129 ES |
WGBS |
Indx12 |
322470 |
200374 |
0.621372531 |
0.989 |
5.7
|
|
Preliminary analysis
- all data are on meangenemachine:/home/zhl002/ZL_1TB
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
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/$c/g" > run.R
perform T-statistics on smoothed data:
Tstat R script
TO run:
for f in *rda
do -> sed "s/RDA/$f/g" runTstat.R > Run.R