Dinh/Dinh 2014/NOTES/2014-9-15: Difference between revisions
Jump to navigation
Jump to search
>Dinh mNo edit summary |
>Dinh |
||
(31 intermediate revisions by the same user not shown) | |||
Line 2: | Line 2: | ||
* Tumor deconvolution paper: http://genomebiology.com/2014/15/8/419 | * Tumor deconvolution paper: http://genomebiology.com/2014/15/8/419 | ||
* Python package: https://pypi.python.org/pypi/MethylPurify/2.0-20140819 | * Python package: https://pypi.python.org/pypi/MethylPurify/2.0-20140819 | ||
* Installed MethylPurify on genome-miner. The scripts dont take much memory & | * Installed MethylPurify on genome-miner. The scripts dont take much memory & finish relatively quickly. | ||
== Test run with WGBS data and BSMAP aligner == | == Test run with WGBS data and BSMAP aligner == | ||
* I tried running MethylPurify on our RRBS MONOD bam files, but could never get the program to complete running/obtain informative bins and mixing ratio. | * I tried running MethylPurify on our RRBS MONOD bam files, but could never get the program to complete running/obtain informative bins and mixing ratio. | ||
Line 54: | Line 54: | ||
MixingRatio:0.175 | MixingRatio:0.175 | ||
* Results from MethylPurify: | * Results from MethylPurify: | ||
[[File:mixRatio.MethylPurify_test1.png | 600px]] | |||
[[File:informativeBins.MethylPurify_test1.png | 600px]] | |||
== MethylPurify run on merged duplicates tumor samples== | |||
* Results from MethylPurify: | |||
[[File:mixRatio.MethylPurify_testMergedTumors.png | 300px]] | |||
[[File:informativeBins.MethylPurify_testMergedTumors.png | 300px]] | |||
* I merged all of the informative bins for colon cancer, lung cancer, and pancreatic cancers together into one BED file. | |||
[[File:colon_cancer_methylpurify_informativeCGIs_merged.txt]] | |||
[[File:lung_cancer_methylpurify_informativeCGIs_merged.txt]] | |||
[[File:pancreatic_cancer_methylpurify_informativeCGIs_merged.txt]] | |||
* I uploaded the BED files to GREAT & used default settings to get the biological processes that are enriched. This may be because the regions are all CGIs, so not certain whether the signal is real or not. | |||
* Results from GREAT: | |||
[[File:colon_cancer_informative_CGIs_biologicalprocess.png| 500px]] | |||
[[File:lung_cancer_informative_CGIs_biologicalprocess.png| 500px]] | |||
[[File:pancreatic_cancer_informative_CGIs_biologicalprocess.png| 500px]] | |||
== Overlap of informative CGIs with blood UMR/LMRs == | |||
* Blood UMRs and LMRs were identified using MethylSeekR | |||
* I used bedtools intersect and required that 50% of the informative bins be overlapped by UMRs/LMRs | |||
* Results: | |||
{| class="wikitable" | |||
| align="center" style="background:#f0f0f0;"|'''''' | |||
| align="center" style="background:#f0f0f0;"|'''colon''' | |||
| align="center" style="background:#f0f0f0;"|'''lung''' | |||
| align="center" style="background:#f0f0f0;"|'''pancreatic''' | |||
|- | |||
| Number of informative CGIs||2852||1248||1798 | |||
|- | |||
| overlap with UMR||2274||1144||1699 | |||
|- | |||
| overlap with LMR||12||14||14 | |||
|- | |||
| %overlap with UMR||79.73%||91.67%||94.49% | |||
|- | |||
| %overlap with LMR||0.42%||1.12%||0.78% | |||
|- | |||
| % no overlap||19.85%||7.21%||4.73% | |||
|} | |||
* Majority of informative CGIs are in UMRs. | |||
* Since only a fraction of informative CGIs are overlapping with LMRs, I looked for region/functional enrichment using GREAT with the CGIs overlapping LMRs as test data and all the informative CGIs as background. No term was enriched. | |||
* Since most of the informative CGIs are overlapping with UMRs, no term was enriched if I used all informative CGIs as background. So I used the whole genome was background. The GREAT results show developmental associated terms enriched in biological processes. | |||
* GREAT results: | |||
[[File:colon_cancer_informative_CGIs_UMRs_biologicalprocess.png| 500px]] | |||
[[File:lung_cancer_informative_CGIs_UMRs_biologicalprocess.png| 500px]] | |||
[[File:pancreatic_cancer_informative_CGIs_UMRs_biologicalprocess.png| 500px]] | |||
== Methylation status of informative bins in normal lung, colon, and pancreas == | |||
* Define "informative bins" : regions identified using MethylPurify which can indicate the level of heterogeneity in tumor samples AND these regions are also overlapping with blood UMRs (meaning that they are unmethylated in blood tissues). | |||
* Strategy: (1) Download ENCODE RRBS data for normal tissues (2) Generate a methylation matrix of normal tissues at these bins | |||
=== Download ENCODE data === | |||
* GEO experiment id: GSE27584 | |||
* Downloaded the tar of bed file. | |||
* Normal tissues (33 normal samples, 66 libraries) and Cancer tissues (19 cancer samples, 49 libraries) | |||
{| class="wikitable" | |||
| align="center" style="background:#f0f0f0;"|'''GSM''' | |||
| align="center" style="background:#f0f0f0;"|'''Sample ID''' | |||
| align="center" style="background:#f0f0f0;"|'''Cell karyotype''' | |||
|- | |||
| GSM683779||BJ||normal | |||
|- | |||
| GSM683770||H1-hESC||normal | |||
|- | |||
| GSM683927||GM12878||relatively normal | |||
|- | |||
| GSM683803||BC_Uterus_BN0765||Normal | |||
|- | |||
| GSM683911||BC_Uterus_BN0765||Normal | |||
|- | |||
| GSM683781||BC_Testis_N30||Normal | |||
|- | |||
| GSM683851||HRCEpiC||normal | |||
|- | |||
| GSM683789||BC_Skeletal_Muscle_01-11002||Normal | |||
|- | |||
| GSM683941||HSMM||normal | |||
|- | |||
| GSM683876||BC_Leukocyte_UHN00204||Normal | |||
|- | |||
| GSM683865||BC_Skin_01-11002||Normal | |||
|- | |||
| GSM683847||Fibrobl||normal | |||
|- | |||
| GSM683841||GM12878-XiMat||relatively normal | |||
|- | |||
| GSM683879||H1-hESC||normal | |||
|- | |||
| GSM683782||BC_Liver_01-11002||Normal | |||
|- | |||
| GSM683883||BC_Adrenal_Gland_H12803N||Normal | |||
|- | |||
| GSM683811||BC_Skeletal_Muscle_01-11002||Normal | |||
|- | |||
| GSM683834||BC_Breast_02-03015||Normal | |||
|- | |||
| GSM683801||HSMMtube||normal | |||
|- | |||
| GSM683760||PrEC||Normal | |||
|- | |||
| GSM683838||PrEC||Normal | |||
|- | |||
| GSM683763||BJ||normal | |||
|- | |||
| GSM683821||BC_Lung_01-11002||Normal | |||
|- | |||
| GSM683878||BC_Kidney_01-11002||Normal | |||
|- | |||
| GSM683796||BC_Placenta_UHN00189||Normal | |||
|- | |||
| GSM683765||Astrocy||normal | |||
|- | |||
| GSM683786||BC_Adrenal_Gland_H12803N||Normal | |||
|- | |||
| GSM683798||BC_Brain_H11058N||Normal | |||
|- | |||
| GSM683939||HTR8svn||normal | |||
|- | |||
| GSM683901||HRCEpiC||normal | |||
|- | |||
| GSM683882||Astrocy||normal | |||
|- | |||
| GSM683908||BC_Brain_H11058N||Normal | |||
|- | |||
| GSM683892||BC_Skin_01-11002||Normal | |||
|- | |||
| GSM683867||HTR8svn||normal | |||
|- | |||
| GSM683819||BC_Pancreas_H12817N||Normal | |||
|- | |||
| GSM683906||GM12878||relatively normal | |||
|- | |||
| GSM683928||Osteobl||normal | |||
|- | |||
| GSM683947||BC_Stomach_01-11002||Normal | |||
|- | |||
| GSM683820||BC_Pericardium_H12529N||Normal | |||
|- | |||
| GSM683846||BC_Left_Ventricle_N41||Normal | |||
|- | |||
| GSM683899||BC_Pericardium_H12529N||Normal | |||
|- | |||
| GSM683848||BC_Pancreas_H12817N||Normal | |||
|- | |||
| GSM683774||GM12878-XiMat||relatively normal | |||
|- | |||
| GSM683914||BC_Liver_01-11002||Normal | |||
|- | |||
| GSM683759||BC_Leukocyte_UHN00204||Normal | |||
|- | |||
| GSM683870||BC_Kidney_01-11002||Normal | |||
|- | |||
| GSM683877||BC_Stomach_01-11002||Normal | |||
|- | |||
| GSM683916||SAEC||normal | |||
|- | |||
| GSM683766||Fibrobl||normal | |||
|- | |||
| GSM683943||HRE||normal | |||
|- | |||
| GSM683850||BC_Testis_N30||Normal | |||
|- | |||
| GSM683764||Hepatocytes||normal | |||
|- | |||
| GSM683761||BC_Breast_02-03015||Normal | |||
|- | |||
| GSM683895||BC_Left_Ventricle_N41||Normal | |||
|- | |||
| GSM683931||BC_Skeletal_Muscle_H12817N||Normal | |||
|- | |||
| GSM683831||HRE||normal | |||
|- | |||
| GSM683893||BC_Placenta_UHN00189||Normal | |||
|- | |||
| GSM683868||BC_Skeletal_Muscle_H12817N||Normal | |||
|- | |||
| GSM683907||HSMM||normal | |||
|- | |||
| GSM683945||HSMMtube||normal | |||
|- | |||
| GSM683818||HSMM||normal | |||
|- | |||
| GSM683872||Hepatocytes||normal | |||
|- | |||
| GSM683833||SAEC||normal | |||
|- | |||
| GSM683940||BC_Lung_01-11002||Normal | |||
|- | |||
| GSM683799||HSMMtube||normal | |||
|- | |||
| GSM683881||Osteobl||normal | |||
|} | |||
{| class="wikitable" | |||
| align="center" style="background:#f0f0f0;"|'''GSM''' | |||
| align="center" style="background:#f0f0f0;"|'''Sample ID''' | |||
| align="center" style="background:#f0f0f0;"|'''Cell karyotype''' | |||
|- | |||
| GSM683792|| Jurkat||cancer | |||
|- | |||
| GSM980577|| ovcar-3||cancer | |||
|- | |||
| GSM720353|| MCF-7||cancer | |||
|- | |||
| GSM683806|| Jurkat||cancer | |||
|- | |||
| GSM720354|| HEK293||cancer | |||
|- | |||
| GSM683769|| HEK293||cancer | |||
|- | |||
| GSM683857|| NB4||cancer | |||
|- | |||
| GSM980582|| Caco-2||cancer | |||
|- | |||
| GSM683946|| LNCaP||cancer | |||
|- | |||
| GSM683863|| LNCaP||cancer | |||
|- | |||
| GSM980580|| A549||cancer | |||
|- | |||
| GSM683856|| K562||cancer | |||
|- | |||
| GSM980575|| MCF-7||cancer | |||
|- | |||
| GSM683884|| SK-N-MC||cancer | |||
|- | |||
| GSM683788|| HCT-116||cancer | |||
|- | |||
| GSM683828|| HeLa-S3||cancer | |||
|- | |||
| GSM1048458|| PANC-1||cancer | |||
|- | |||
| GSM720352|| MCF-7||cancer | |||
|- | |||
| GSM683918|| SK-N-MC||cancer | |||
|- | |||
| GSM683898|| HCT-116||cancer | |||
|- | |||
| GSM683827|| NB4||cancer | |||
|- | |||
| GSM720351|| MCF-7||cancer | |||
|- | |||
| GSM683795|| HepG2||cancer | |||
|- | |||
| GSM683793|| MCF-7||cancer | |||
|- | |||
| GSM683855|| HepG2||cancer | |||
|- | |||
| GSM683924|| LNCaP||cancer | |||
|- | |||
| GSM683909|| PFSK-1||cancer | |||
|- | |||
| GSM683866|| PANC-1||cancer, male | |||
|- | |||
| GSM683800|| SK-N-SH_RA||cancer | |||
|- | |||
| GSM683920|| PANC-1||cancer, male | |||
|- | |||
| GSM720355|| HEK293||cancer | |||
|- | |||
| GSM683926|| HeLa-S3||cancer | |||
|- | |||
| GSM980576|| HL-60||cancer | |||
|- | |||
| GSM683768|| LNCaP||cancer | |||
|- | |||
| GSM980574|| MCF-7||cancer | |||
|- | |||
| GSM720349|| NT2-D1||cancer | |||
|- | |||
| GSM1048457|| HepG2||cancer | |||
|- | |||
| GSM683787|| MCF-7||cancer | |||
|- | |||
| GSM683825|| HEK293||cancer | |||
|- | |||
| GSM683862|| LNCaP||cancer | |||
|- | |||
| GSM683832|| NT2-D1||cancer | |||
|- | |||
| GSM683776|| LNCaP||cancer | |||
|- | |||
| GSM980581|| UCH-1||cancer | |||
|- | |||
| GSM683929|| NT2-D1||cancer | |||
|- | |||
| GSM683804|| NT2-D1||cancer | |||
|- | |||
| GSM683780|| K562||cancer | |||
|- | |||
| GSM720350|| MCF-7||cancer | |||
|- | |||
| GSM683919|| SK-N-SH_RA||cancer | |||
|- | |||
| GSM683810|| PFSK-1||cancer | |||
|} | |||
===Generate methylation matrices over Informative Bins === | |||
* Majority of informative bins are unmethylated in normal tissues | |||
* Two particular cell lines were more methylated: | |||
**HTR: Trophoblast (HTR-8/SVneo) cell line. A thin layer of ectoderm that forms the wall of many mammalian blastulas and functions in the nutrition and implantation of the embryo, cell karyotype: normal, cell lineage: Blastula | |||
**GM12878-XiMat: lymphoblastoid, cell karyotype: relatively normal, cell lineage: International HapMap Project - CEPH/Utah - European Caucasion; Epstein-Barr Virus;Cloned for maternal X inactivation | |||
* Heatmap (normal tissues, showing replicates) | |||
Colon cancer | |||
[[File:ColonCancerBins_normalTissues_heatmap.png | 600px]] | |||
Lung cancer | |||
[[File:LungCancerBins_normalTissues_heatmap.png | 600px]] | |||
Pancreatic cancer | |||
[[File:PancreaticCancerBins_normalTissues_heatmap.png | 600px]] | |||
* Heatmaps ( all replicates were merged into one file ) | |||
Colon cancer: normal tissues, cancer cell lines | |||
[[File:ColonCancerBins_normalTissues_heatmap_mergedReps.png | 600px]][[File:ColonCancerBins_cancerCellLines_heatmap_mergedReps.png | 600px]] | |||
Lung cancer: normal tissues, cancer cell lines | |||
[[File:LungCancerBins_normalTissues_heatmap_mergedReps.png | 600px]][[File:LungCancerBins_cancerCellLines_heatmap_mergedReps.png | 600px]] | |||
Pancreatic cancer: normal tissues, cancer cell lines | |||
[[File:PancreaticCancerBins_normalTissues_heatmap_mergedReps.png | 600px]][[File:PancreaticCancerBins_cancerCellLines_heatmap_mergedReps.png | 600px]] | |||
==="Normal" reference bins=== | |||
* Dr. Zhang suggested to use only "BC_" samples that we are sure are 100% normal tissues. There are 17 of these. After plotting the heatmaps, placenta and testis should be removed. | |||
* Median methylation levels across normal tissues (removed placenta, testis) | |||
** [[File:Colon.bins.median-methylation-normal.txt]] [[File:Colon.heatmap.qqplot.pdf]] | |||
** [[File:Lung.bins.median-methylation-normal.txt]] [[File:Lung.heatmap.qqplot.pdf]] | |||
** [[File:Pancreatic.bins.median-methylation-normal.txt]] [[File:Pancreatic.heatmap.qqplot.pdf]] | |||
[[File:median-methylation-at-informative-bins-in-normal.png | 300px]] | |||
* To get the "normal" reference: | |||
{| class="wikitable" | |||
| align="center" style="background:#f0f0f0;"|'''''' | |||
| align="center" style="background:#f0f0f0;"|'''Colon''' | |||
| align="center" style="background:#f0f0f0;"|'''Lung''' | |||
| align="center" style="background:#f0f0f0;"|'''Pancreatic''' | |||
|- | |||
| Number of starting bins||2274||1144||1699 | |||
|- | |||
| Number of bins with >=15 normal tissues coverage||2138||1068||1622 | |||
|- | |||
| Number of bins with median methylation in normal <0.2||2070 (96.82%)||1017(95.22%)||1597(98.46%) | |||
|- | |||
| Number of bins with median methylation in normal <0.1||1944(90.93%)||932(87.27%)||1504(92.73%) | |||
|- | |||
| Number of bins with median methylation in normal <0.05||1677(78.43%)||716(67.04%)||1238(76.33%) | |||
|- | |||
| %bins(out of all orig bins, if 0.2 cutoff)||91.03%||88.90%||94.00% | |||
|} | |||
== Divide haplotypes into clusters in solid tumor samples == | |||
* Go back to solid tumor data and try to identify haplotype clusters. | |||
* Try to estimate tumor purify based on the fraction of cancer haplotypes (these should be partially or fully methylated). | |||
== Discussion == | |||
* Discussion with Dr. Zhang (9/12/14): | |||
** Intersect informative bins with blood UMRs, what are those regions enriched for in GREAT? (DONE) | |||
** How to tell whether the minor or major component is the tumor DNA ? It appears that these tumor references have varying amount of minor component. | |||
*** Ave. methyl colon cancer tumors: major = 0.7348, minor = 0.1602 | |||
*** Ave. methyl lung cancer tumors: major = 0.6393, minor = 0.2574 | |||
*** Ave. methyl pancreatic cancer tumors: major = 0.7623, minor = 0.1181 | |||
*** Although majority of informative CGIs are overlapping with blood UMRs, the minor components are not always unmethylated, thus there may be other normal cells in the mixture. | |||
** Use MethylPurify to generate cancer reference from primary tumors. So we can create a database of true cancer references (after substracting out normal cells). We can use the pure cancer reference for methylation haplotyping to detect cancer reads in blood. |
Latest revision as of 19:13, 7 October 2014
MethylPurify[edit]
- Tumor deconvolution paper: http://genomebiology.com/2014/15/8/419
- Python package: https://pypi.python.org/pypi/MethylPurify/2.0-20140819
- Installed MethylPurify on genome-miner. The scripts dont take much memory & finish relatively quickly.
Test run with WGBS data and BSMAP aligner[edit]
- I tried running MethylPurify on our RRBS MONOD bam files, but could never get the program to complete running/obtain informative bins and mixing ratio.
- Two major issues with MethylPurify: requires enough reads to get minimum depth of coverage over informative bins and may need to realign data with BSMAP.
- Downloaded WGBS sequencing data from the paper: SRR1232303 (BioSample: SAMN02725471; lung adenocarcinoma tissue, male), containing ~20 million PE reads x 90 bp each.
- Downsampled to get ~25% of only reads 1 to get 5,077,371 x 90 bp single end reads to perform testing.
- Performed mapping with BSMAP as that aligner is listed by MethylPurify to get the necessary bam input file.
- BSMAP mapping command:
/bsmap-2.74/bsmap -u -s 12 -v 0.04 -p 4 -a downsampled.SRR1232303_1.fastq -d hg19.fa -o downsampled.SRR1232303_1.fastq.sam
- BSMAP took 36,221 seconds (10.06 hours) to complete mapping for ~5 million SE x 90 bp reads with 4 CPU threads.
- Performed deconvolution with MethylPurify:
python2.7 /home/ddiep/Downloads/MethylPurify-2.0-20140819/methylpurify/bin/MethylPurify -f downsampled.SRR1232303_1.fastq.bam -g hg19.fa -i /home/ddiep/Downloads/MethylPurify-2.0/methylpurify/db/CGI_hg19_slop1000.bed -c 10 -s 50 -b 300
- MethylPurify outputs:
... The predicted mixing ratio is: 0.265 The number of informative bins is: 29.0 The number of informative bins is less than 400, too less for the prediction of mixing ratio, continue INFO @ Mon, 15 Sep 2014 09:33:27: Running get_methyl_profile: MixingRatio:0.265
- CGI_hg19_slop1000.bed was only found in the MethylPurify-2.0.tar.gz package, but not the latest package. I couldn't find the genome.sh script that was necessary to generate this, so I need to figure out how to generate this file in the future because I am not 100% confident this is the appropriate file to use.
Testing with MONOD RRBS bam files from BisReadMapper[edit]
- Since WGBS and RRBS may cover different CGIs, the CGI selected regions by MethylPurify have this depth of coverage:
samtools depth XXX.CGI.sorted.bam | awk '{sum+=$3} END { print "Sum = ", sum, " Average = ",sum/NR}' >> Sum = 41941637 Average = 2.30644
- Our RRBS data have even better coverage at CGIs, the CGI regions selected by MethylPurify for PC-T-7_2 have this depth of coverage:
samtools depth PC-T-7_2.merged.CGI.sorted.bam | awk '{sum+=$3} END { print "Sum = ", sum, " Average = ",sum/NR}' >> Sum = 393749988 Average = 27.9027
- This could means that MethylPurify failed when I ran on our RRBS bam files because it couldn't understand the alignments from bisReadMapper, but requires the BSMAP alignment format (may have special instructions for MethylPurify). I will figure out the differences in BSMAP and BisReadMapper bam files.
- BSMAP sam file:
SRR1232303.200105 0 chr1 30557958 255 90M * 0 0 ATTCCTTTATTTTGGTGGTTGGGGTGTTTTAGGGTTTTTTTTTTAATTAATTTTTTTTTCGTTGTTTTTTTTTTTTTGTTTTATGTTTTT @CCFFFFFFHHHHJICGHHIJJJJ:DFHIJHJJJHJJJJJJHFDCDDEECEEEDDDDDDDDDDDDDDDDDDDDDDDD38ACD3:(:ADDD NM:i:3 ZS:Z:++ SRR1232303.200112 16 chr1 30559194 255 90M * 0 0 ATCCCTTCCCTTTCACAACCTACTCCGAAAACCGCTAACCTAAAAATACAGCAATCTCTACTATCCTAAAAAAACAAAATCCCAAGGAAT DDDDDDDDBDDEDDDDDDDDCACAFFFHHGIJJIJIHHJIHJJJIIGJJJJJIJGIIJJIJJIHJIHJJJJJJJJJJHHHHHFFFFFCCC NM:i:2 ZS:Z:-+
- BSMAP have extra ZS:Z field that's just the strand information. MethylPurify would fail similarly as for BisReadMapper bam files, after I removed the ZS:Z field from the BSMAP bam file. I may just need to generate the ZS:Z field to get MethylPurify to work on BisReadMapper bam files.
- Encoding for ZS:Z field is as follows:
++: forward strand of Watson of reference (BSW) +-: reverse strand of Watson of reference (BSWC) -+: forward strand of Crick of reference (BSC) --: reverse strand of Crick of reference (BSCC)
- Make MethylPurify think that BisReadMapper bam files are from BSMAP: Add in NM:i:0 and ZS:Z:-+ (for Crick) and ZS:Z:++ (for Watson).
samtools view $f | awk '{if($2 == 0) print $0"\tNM:i:0\tZS:Z:++"; if($2 == 16) print $0"\tNM:i:0\tZS:Z:-+"}' | samtools view -bSt /media/2TB_storeA/BisRef/Hg19_plusLamba/hg19_lambda.fa.fai - > mod.$f
- Run MethylPurify:
python2.7 /home/ddiep/Downloads/MethylPurify-2.0-20140819/methylpurify/bin/MethylPurify -f ../mod.PC-T-7_2.merged.bam -g hg19.fa -i /home/ddiep/Downloads/MethylPurify-2.0/methylpurify/db/CGI_hg19_slop1000.bed -c 10 -s 50 -b 300
- MethylPurify outputs:
... The predicted mixing ratio is: 0.175 The number of informative bins is: 304.0 The number of informative bins is less than 400, too less for the prediction of mixing ratio, continue INFO @ Mon, 15 Sep 2014 13:09:12: Running get_methyl_profile: MixingRatio:0.175
- Results from MethylPurify:
File:MixRatio.MethylPurify test1.png File:InformativeBins.MethylPurify test1.png
MethylPurify run on merged duplicates tumor samples[edit]
- Results from MethylPurify:
File:MixRatio.MethylPurify testMergedTumors.png File:InformativeBins.MethylPurify testMergedTumors.png
- I merged all of the informative bins for colon cancer, lung cancer, and pancreatic cancers together into one BED file.
File:Colon cancer methylpurify informativeCGIs merged.txt File:Lung cancer methylpurify informativeCGIs merged.txt File:Pancreatic cancer methylpurify informativeCGIs merged.txt
- I uploaded the BED files to GREAT & used default settings to get the biological processes that are enriched. This may be because the regions are all CGIs, so not certain whether the signal is real or not.
- Results from GREAT:
File:Colon cancer informative CGIs biologicalprocess.png File:Lung cancer informative CGIs biologicalprocess.png File:Pancreatic cancer informative CGIs biologicalprocess.png
Overlap of informative CGIs with blood UMR/LMRs[edit]
- Blood UMRs and LMRs were identified using MethylSeekR
- I used bedtools intersect and required that 50% of the informative bins be overlapped by UMRs/LMRs
- Results:
' | colon | lung | pancreatic |
Number of informative CGIs | 2852 | 1248 | 1798 |
overlap with UMR | 2274 | 1144 | 1699 |
overlap with LMR | 12 | 14 | 14 |
%overlap with UMR | 79.73% | 91.67% | 94.49% |
%overlap with LMR | 0.42% | 1.12% | 0.78% |
% no overlap | 19.85% | 7.21% | 4.73% |
- Majority of informative CGIs are in UMRs.
- Since only a fraction of informative CGIs are overlapping with LMRs, I looked for region/functional enrichment using GREAT with the CGIs overlapping LMRs as test data and all the informative CGIs as background. No term was enriched.
- Since most of the informative CGIs are overlapping with UMRs, no term was enriched if I used all informative CGIs as background. So I used the whole genome was background. The GREAT results show developmental associated terms enriched in biological processes.
- GREAT results:
File:Colon cancer informative CGIs UMRs biologicalprocess.png File:Lung cancer informative CGIs UMRs biologicalprocess.png File:Pancreatic cancer informative CGIs UMRs biologicalprocess.png
Methylation status of informative bins in normal lung, colon, and pancreas[edit]
- Define "informative bins" : regions identified using MethylPurify which can indicate the level of heterogeneity in tumor samples AND these regions are also overlapping with blood UMRs (meaning that they are unmethylated in blood tissues).
- Strategy: (1) Download ENCODE RRBS data for normal tissues (2) Generate a methylation matrix of normal tissues at these bins
Download ENCODE data[edit]
- GEO experiment id: GSE27584
- Downloaded the tar of bed file.
- Normal tissues (33 normal samples, 66 libraries) and Cancer tissues (19 cancer samples, 49 libraries)
GSM | Sample ID | Cell karyotype |
GSM683779 | BJ | normal |
GSM683770 | H1-hESC | normal |
GSM683927 | GM12878 | relatively normal |
GSM683803 | BC_Uterus_BN0765 | Normal |
GSM683911 | BC_Uterus_BN0765 | Normal |
GSM683781 | BC_Testis_N30 | Normal |
GSM683851 | HRCEpiC | normal |
GSM683789 | BC_Skeletal_Muscle_01-11002 | Normal |
GSM683941 | HSMM | normal |
GSM683876 | BC_Leukocyte_UHN00204 | Normal |
GSM683865 | BC_Skin_01-11002 | Normal |
GSM683847 | Fibrobl | normal |
GSM683841 | GM12878-XiMat | relatively normal |
GSM683879 | H1-hESC | normal |
GSM683782 | BC_Liver_01-11002 | Normal |
GSM683883 | BC_Adrenal_Gland_H12803N | Normal |
GSM683811 | BC_Skeletal_Muscle_01-11002 | Normal |
GSM683834 | BC_Breast_02-03015 | Normal |
GSM683801 | HSMMtube | normal |
GSM683760 | PrEC | Normal |
GSM683838 | PrEC | Normal |
GSM683763 | BJ | normal |
GSM683821 | BC_Lung_01-11002 | Normal |
GSM683878 | BC_Kidney_01-11002 | Normal |
GSM683796 | BC_Placenta_UHN00189 | Normal |
GSM683765 | Astrocy | normal |
GSM683786 | BC_Adrenal_Gland_H12803N | Normal |
GSM683798 | BC_Brain_H11058N | Normal |
GSM683939 | HTR8svn | normal |
GSM683901 | HRCEpiC | normal |
GSM683882 | Astrocy | normal |
GSM683908 | BC_Brain_H11058N | Normal |
GSM683892 | BC_Skin_01-11002 | Normal |
GSM683867 | HTR8svn | normal |
GSM683819 | BC_Pancreas_H12817N | Normal |
GSM683906 | GM12878 | relatively normal |
GSM683928 | Osteobl | normal |
GSM683947 | BC_Stomach_01-11002 | Normal |
GSM683820 | BC_Pericardium_H12529N | Normal |
GSM683846 | BC_Left_Ventricle_N41 | Normal |
GSM683899 | BC_Pericardium_H12529N | Normal |
GSM683848 | BC_Pancreas_H12817N | Normal |
GSM683774 | GM12878-XiMat | relatively normal |
GSM683914 | BC_Liver_01-11002 | Normal |
GSM683759 | BC_Leukocyte_UHN00204 | Normal |
GSM683870 | BC_Kidney_01-11002 | Normal |
GSM683877 | BC_Stomach_01-11002 | Normal |
GSM683916 | SAEC | normal |
GSM683766 | Fibrobl | normal |
GSM683943 | HRE | normal |
GSM683850 | BC_Testis_N30 | Normal |
GSM683764 | Hepatocytes | normal |
GSM683761 | BC_Breast_02-03015 | Normal |
GSM683895 | BC_Left_Ventricle_N41 | Normal |
GSM683931 | BC_Skeletal_Muscle_H12817N | Normal |
GSM683831 | HRE | normal |
GSM683893 | BC_Placenta_UHN00189 | Normal |
GSM683868 | BC_Skeletal_Muscle_H12817N | Normal |
GSM683907 | HSMM | normal |
GSM683945 | HSMMtube | normal |
GSM683818 | HSMM | normal |
GSM683872 | Hepatocytes | normal |
GSM683833 | SAEC | normal |
GSM683940 | BC_Lung_01-11002 | Normal |
GSM683799 | HSMMtube | normal |
GSM683881 | Osteobl | normal |
GSM | Sample ID | Cell karyotype |
GSM683792 | Jurkat | cancer |
GSM980577 | ovcar-3 | cancer |
GSM720353 | MCF-7 | cancer |
GSM683806 | Jurkat | cancer |
GSM720354 | HEK293 | cancer |
GSM683769 | HEK293 | cancer |
GSM683857 | NB4 | cancer |
GSM980582 | Caco-2 | cancer |
GSM683946 | LNCaP | cancer |
GSM683863 | LNCaP | cancer |
GSM980580 | A549 | cancer |
GSM683856 | K562 | cancer |
GSM980575 | MCF-7 | cancer |
GSM683884 | SK-N-MC | cancer |
GSM683788 | HCT-116 | cancer |
GSM683828 | HeLa-S3 | cancer |
GSM1048458 | PANC-1 | cancer |
GSM720352 | MCF-7 | cancer |
GSM683918 | SK-N-MC | cancer |
GSM683898 | HCT-116 | cancer |
GSM683827 | NB4 | cancer |
GSM720351 | MCF-7 | cancer |
GSM683795 | HepG2 | cancer |
GSM683793 | MCF-7 | cancer |
GSM683855 | HepG2 | cancer |
GSM683924 | LNCaP | cancer |
GSM683909 | PFSK-1 | cancer |
GSM683866 | PANC-1 | cancer, male |
GSM683800 | SK-N-SH_RA | cancer |
GSM683920 | PANC-1 | cancer, male |
GSM720355 | HEK293 | cancer |
GSM683926 | HeLa-S3 | cancer |
GSM980576 | HL-60 | cancer |
GSM683768 | LNCaP | cancer |
GSM980574 | MCF-7 | cancer |
GSM720349 | NT2-D1 | cancer |
GSM1048457 | HepG2 | cancer |
GSM683787 | MCF-7 | cancer |
GSM683825 | HEK293 | cancer |
GSM683862 | LNCaP | cancer |
GSM683832 | NT2-D1 | cancer |
GSM683776 | LNCaP | cancer |
GSM980581 | UCH-1 | cancer |
GSM683929 | NT2-D1 | cancer |
GSM683804 | NT2-D1 | cancer |
GSM683780 | K562 | cancer |
GSM720350 | MCF-7 | cancer |
GSM683919 | SK-N-SH_RA | cancer |
GSM683810 | PFSK-1 | cancer |
Generate methylation matrices over Informative Bins[edit]
- Majority of informative bins are unmethylated in normal tissues
- Two particular cell lines were more methylated:
- HTR: Trophoblast (HTR-8/SVneo) cell line. A thin layer of ectoderm that forms the wall of many mammalian blastulas and functions in the nutrition and implantation of the embryo, cell karyotype: normal, cell lineage: Blastula
- GM12878-XiMat: lymphoblastoid, cell karyotype: relatively normal, cell lineage: International HapMap Project - CEPH/Utah - European Caucasion; Epstein-Barr Virus;Cloned for maternal X inactivation
- Heatmap (normal tissues, showing replicates)
Colon cancer File:ColonCancerBins normalTissues heatmap.png Lung cancer File:LungCancerBins normalTissues heatmap.png Pancreatic cancer File:PancreaticCancerBins normalTissues heatmap.png
- Heatmaps ( all replicates were merged into one file )
Colon cancer: normal tissues, cancer cell lines File:ColonCancerBins normalTissues heatmap mergedReps.pngFile:ColonCancerBins cancerCellLines heatmap mergedReps.png Lung cancer: normal tissues, cancer cell lines File:LungCancerBins normalTissues heatmap mergedReps.pngFile:LungCancerBins cancerCellLines heatmap mergedReps.png Pancreatic cancer: normal tissues, cancer cell lines File:PancreaticCancerBins normalTissues heatmap mergedReps.pngFile:PancreaticCancerBins cancerCellLines heatmap mergedReps.png
"Normal" reference bins[edit]
- Dr. Zhang suggested to use only "BC_" samples that we are sure are 100% normal tissues. There are 17 of these. After plotting the heatmaps, placenta and testis should be removed.
- Median methylation levels across normal tissues (removed placenta, testis)
File:Median-methylation-at-informative-bins-in-normal.png
- To get the "normal" reference:
' | Colon | Lung | Pancreatic |
Number of starting bins | 2274 | 1144 | 1699 |
Number of bins with >=15 normal tissues coverage | 2138 | 1068 | 1622 |
Number of bins with median methylation in normal <0.2 | 2070 (96.82%) | 1017(95.22%) | 1597(98.46%) |
Number of bins with median methylation in normal <0.1 | 1944(90.93%) | 932(87.27%) | 1504(92.73%) |
Number of bins with median methylation in normal <0.05 | 1677(78.43%) | 716(67.04%) | 1238(76.33%) |
%bins(out of all orig bins, if 0.2 cutoff) | 91.03% | 88.90% | 94.00% |
Divide haplotypes into clusters in solid tumor samples[edit]
- Go back to solid tumor data and try to identify haplotype clusters.
- Try to estimate tumor purify based on the fraction of cancer haplotypes (these should be partially or fully methylated).
Discussion[edit]
- Discussion with Dr. Zhang (9/12/14):
- Intersect informative bins with blood UMRs, what are those regions enriched for in GREAT? (DONE)
- How to tell whether the minor or major component is the tumor DNA ? It appears that these tumor references have varying amount of minor component.
- Ave. methyl colon cancer tumors: major = 0.7348, minor = 0.1602
- Ave. methyl lung cancer tumors: major = 0.6393, minor = 0.2574
- Ave. methyl pancreatic cancer tumors: major = 0.7623, minor = 0.1181
- Although majority of informative CGIs are overlapping with blood UMRs, the minor components are not always unmethylated, thus there may be other normal cells in the mixture.
- Use MethylPurify to generate cancer reference from primary tumors. So we can create a database of true cancer references (after substracting out normal cells). We can use the pure cancer reference for methylation haplotyping to detect cancer reads in blood.