Alice:Whole Genome Bisulfite Sequencing Lab Notes: Difference between revisions

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*detailed method and pre-liminary analysis of the pipeline can also be found here: [[http://genome-tech.ucsd.edu/LabNotes/index.php/Dinh/Dinh_2014/NOTES/2014-4-1 Dinh's wiki page]]
*detailed method and pre-liminary analysis of the pipeline can also be found here: [[http://genome-tech.ucsd.edu/LabNotes/index.php/Dinh/Dinh_2014/NOTES/2014-4-1 Dinh's wiki page]]
===DMR plots and method comparison===
===DMR plots and method comparison===
===Discussion===
*we focused on the DMRs generated by method 2 and method 3. Although method 3 has the lowest FDR, it might be too stringent because the candidate DMRs seem to be biased towards samples with higher coverage
*by down-sampling the published ESC data, we are expected to observe less of strain-specific DMRs
*we will repeat the comparison between B6ESC and iPSC/SCNT

Revision as of 00:07, 16 April 2014

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]

DMR finding 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

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 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

results

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)

Among the candidate DMRs generated from the above steps, we were able to detect true culture-specific DMRs, where there are differentially methylated regions between 2i and serum conditions. However, because this method relies on biological replicates to do smoothing and calculate T-statistics, so we have decided to use other published method to find DMRs.

DMR finding using MOAB

  • detailed method and pre-liminary analysis of the pipeline can also be found here: [Dinh's wiki page]

DMR plots and method comparison

Discussion

  • we focused on the DMRs generated by method 2 and method 3. Although method 3 has the lowest FDR, it might be too stringent because the candidate DMRs seem to be biased towards samples with higher coverage
  • by down-sampling the published ESC data, we are expected to observe less of strain-specific DMRs
  • we will repeat the comparison between B6ESC and iPSC/SCNT