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

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Line 142: Line 142:
   [[File:runTstat.txt | Tstat R script]]
   [[File:runTstat.txt | Tstat R script]]
   TO run:
   TO run:
   for f in *rda  
   for f in *rda
   do -> sed "s/RDA/$f/g" runTstat.R > Run.R
   do -> sed "s/RDA/$f/g" runTstat.R > Run.R

Revision as of 01:06, 7 March 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 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