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==Results== === Library and Public dataset === * library = non-directional or directional? * Single-end or Pired-end * Phred score = 33 or 64? * Read Length >= 75 or not? * WGBS or RRBS * Dinh's BAM: /oasis/tscc/scratch/ddiep/Working === Computational Script === * [[43 Samples in prediction model]] * bam to haploinfo * [[bam2hapinfo-V1.0.pl]] * Debug:Update read strain determine function, March 2016: $strand<16 to $strand & 16 or 0x10 * update again: Now, bam2hapinfo.pl works for BAMs created by bismark and bisReadMapper. * haploinfo to MHL * [[hapinfo2mhl-V2016.pl]] === RD10 and 80bp up Region Definition === * Define WGBS frequently coverage genomic regions * Read Dept >= 10 * Length >= 80bp Merge all the Bam files: [[MergeAllBamFilesWithBash.sh]][https://www.biostars.org/p/9864/] /home/kunzhang/softwares/bedtools-2.17.0/bin/bedtools genomecov -bg -split -ibam N37_10_tissue_pool_chr1.merged.bam> N37_10_tissue_pooled.chr1.genomecov.bed awk '$4>9 { print $1"\t"$2"\t"$3}' N37_10_tissue_pooled.chr1.genomecov.bed | /home/kunzhang/softwares/bedtools-2.17.0/bin/bedtools merge -d 10 -i - > N37_10_tissue_pooled.chr1.RD10.genomecov.bed awk '$3-$2>80 {print $1"\t"$2"\t"$3"\t"$3-$2+1}' N37_10_tissue_pooled.chr1.RD10.genomecov.bed > N37_10_tissue_pooled.chr1.RD10_80up.genomecov.bed === GWBS alignment with Bismark === * Alignment with Bismark in glean queue *prepare the sample information manually, therefore, you can alignment single-end and paired-end BS-seq Fastq at same time. [[Sam_Config_File Format]] perl ~/bin/[[bismark2bamPBS.pl]] Sam_Config_File submit *check which alignment is failed and therefore re-submit the job again: perl ~/bin/[[bismarkAlignSucceedCheck.pl]] *check alignment ratio/coverage: perl [[~/bin/bismarkMappingStatistic.pl]] === Bam to Hapinfo (Update: March/29/2016)=== *bam and hapinfo files * N37: /home/shg047/oasis/N37/sortBam ** cd /home/shg047/oasis/N37/hapinfo ** perl ../[[renameN37.pl]] ** rm ~/oasis/monod/hapinfo/N37* ** cp N37* ~/oasis/monod/hapinfo/ * SALK: /home/shg047/oasis/SALK/bam *2 Colon (GSE46644): /home/shg047/oasis/Ziller2013 cd /media/LTS_60T/Dinh/WGBS_LTS33/Hg19/Tumor_WGBS/BAMfiles cd /oasis/tscc/scratch/ddiep/Ziller_BAMfiles GSM1204465 SRX332736 Colon primary tumor GSM1465024 SRX669642 HCT116 cd /home/shg047/oasis/Tumor-WGBS/HCT116-SRX669642/bam perl ~/bin/[[samInfoPrep4Bam2Hapinfo.pl]] .sorted.clipped.bam > ../Saminfo4bam2hapinfo.txt perl ~/bin/[[bam2hapInfo2PBS.pl]] ../Saminfo4bam2hapinfo.txt submit nonbismark cd /home/shg047/oasis/Tumor-WGBS/HCC-SRX332736 perl ~/bin/[[samInfoPrep4Bam2Hapinfo.pl]] .sorted.clipped.bam > ../Saminfo4bam2hapinfo.txt perl ~/bin/[[bam2hapInfo2PBS.pl]] ../Saminfo4bam2hapinfo.txt submit nonbismark * Blood: ** ln -s /home/k4zhang/my_oasis_tscc/MONOD/whole_blood_WGBS/BAMfiles bam ** cd /home/shg047/oasis/AgeBlood ** perl [[~/bin/mergeBamByChrosome.pl]] /home/shg047/oasis/AgeBlood/bam /home/shg047/oasis/AgeBlood/mergeBam submit * HCC: * Plasma: **Phase I: *** where is fastq? ***/oasis/tscc/scratch/ddiep/Working/Rerun_rrbs/BAMfiles ***/oasis/tscc/scratch/ddiep/Working/Rerun_rrbs/MethylFreq **Phase II: *** where is fastq? *** /oasis/tscc/scratch/ddiep/Plasma_RRBS_151208/BAMfiles **Phase III: *** * Estellar: /home/shg047/oasis/Estellar2016 === Hapinfo to Methylation LD Heatmap(Update: March/16/2016)=== * === Hapinfo to MHB (Update: March/16/2016)=== * === Hapinfo to MHL matrix=== * Updated MHL Matrix: /home/shg047/oasis/monod/mhl/monod.mhl.List1.march29.txt * WB cd /home/shg047/oasis/AgeBlood/hapinfo echo "perl ~/bin/[[hapinfo2mhl.pl]] ./ > ../wb.mhl.march22.txt" | qsub -N wbc.hapinfo2mhl -q pdafm [[WB_hapinfo_mf.pbs]] [[WB_hapinfo_mhl.pbs]] * Simulation: Figure 2 (manuscript-code and data). cd /home/shg047/oasis/monod/test/hapinfo2mhl [[perl ~/bin/hapinfo2mhl.pl]] ./ cd /home/shg047/oasis/monod/test/hapinfo2mhb cat ../hapinfo2mhl/*hapInfo.txt > MergeInfo.txt perl [[~/oasis/monod/bin/hapinfo2R2ByBed.pl]] rlt chr10:100027918-100027944 < MergeInfo.txt *[[File:36CF.tm.png|400px]] [[File:6129.tm.png|400px]][[File:757E.tm.png|300px]] * Real Data cd /oasis/tscc/scratch/shg047/AgeBlood/mhl * [[Relationship between MF and MHL in WB(Age) data: Rcode.R]](Attention: here 1-9 haplotype were used. In Dr. Zhang's idea, only 3-7 haplotype were used in version 1 script) * [[smoothscatter.plot.monod.wb.R]] * [[File:Monod.3FD8.tm.png|400px]][[File:6229.tm.png|400px]] miss ratio: 43.63%: sum(is.na(data))/(nrow(data)*ncol(data)) Estellar2016 * Estellar: cd /home/shg047/oasis/Estellar2016/mergeHapinfo Rscript --vanilla ~/bin/[[matrixPlot.R]] -i "Estellar2016.mhl.march19.txt" -o "Esterllar2016" ===Strategy for genome-wide DNA methylation sequencing for cancer and plasma samples=== *Linkage disequilibrium (R2) of methylation status between pair-wise CpG loci within the methylation blocks DNA methylation linkage disequilibrium inferred by the methylation status from the single read of the BS-seq were conducted in the present study. The R2 of pair-wise CpG loci within the methylation block were calculated * Number of MHBs with R2 scp shg047@genome-miner:/home/kunzhang/CpgMIP/MONOD/Data/WGBS_data/mld_block_hapInfo_July2015/All_chromosomes_combined/* /home/shg047/oasis/monod/mhb/hapinfo ===Methylation haplotype blocks were conservative in human tissues=== <gallery widths=400px heights=200px> File:MONOD.08172015.png </gallery> === HMH=== *Indentify cancer assocated hyper-methylated haplotype cd /media/Home_Raid1/shg047/work/monod/hapinfo perl [[methHMH.pl]] 6-T-1.sorted.clipped.bam.hapInfo.txt 6-P-1.sorted.clipped.bam.hapInfo.txt [[excl.list.txt]] ./caHMH/caHMH-1 perl methHMH.pl 6-T-2.sorted.clipped.bam.hapInfo.txt 6-P-2.sorted.clipped.bam.hapInfo.txt excl.list.txt ./caHMH/caHMH-2 perl methHMH.pl 6-T-3.sorted.clipped.bam.hapInfo.txt 6-P-3.sorted.clipped.bam.hapInfo.txt excl.list.txt ./caHMH/caHMH-3 perl methHMH.pl 6-T-4.sorted.clipped.bam.hapInfo.txt 6-P-4.sorted.clipped.bam.hapInfo.txt excl.list.txt ./caHMH/caHMH-4 perl methHMH.pl 6-T-5.sorted.clipped.bam.hapInfo.txt 6-P-5.sorted.clipped.bam.hapInfo.txt excl.list.txt ./caHMH/caHMH-5 cat caHMH* > HMH.txt Rscript caHMH.R #>>>>>>>> caHMH.R #>>>>>>>> data<-read.table("HMH.txt",head=T,sep="\t",as.is=T) input<-data.matrix(data[,8:ncol(data)]) caHMH<-data[which(apply(input,1,function(x) sum(x)==0)),1] unique(as.character(caHMH)) write.table(unique(as.character(caHMH)),file="caHMH.rlt.txt",col.names=F,row.names=F,quote=F) #>>>>>>>> caHMH.sh #>>>>>>>> awk -F":|-" '{print $1,$2,$3,$1":"$2"-"$3}' OFS="\t" caHMH.rlt.txt > caHMH.bed bedtools intersect -wao -a caHMH.bed -b ~/work/db/hg19/hg19_refGene.bed | sort -k1,1n * Take one caHMH and validate with MHL values in all our samples (MHL matrix) # chr1 119543104 119543127 chr1:119543104-119543127 TBX15 setwd("/oasis/tscc/scratch/shg047/monod/hapinfo") load("MHL4.RData") data[match("chr1:119543104-119543127",rownames(data)),] # quite good, highly low methylated in >86% normal tissues and hyper-methylated in cancer related samples including 6-T, and parts of 6-P. \\ * how to test the significance between PBMC and TCGA methylation haplotype blocks [[File:R20150818.txt]] ===Cancer Diagnosis Performance based on Methylation Haplotype Loading of circulating free DNA methylation === ? THINK ABOUT THE TISSUE SPECIFIC AND CANCER SPECIFIC? In this section, RRBS and Capseq dataset were taken as the discovery dataset and the BSPP dataset were used validate the performance of the biomarkers. For RRBS dataset, 68 cancer and 8 normal samples were included. 56046 regions were obtained by methylation haplotype loading algorithm (MHL) and 17263 regions were 100% un-methylated in 8 normal samples. 248 regions among 56046 were methylated in at least 50% cancer samples while 100% un-methylated in normal samples. The average length of the regions were 103bp (IQR=95bp, SD=126) and were located in the promoter region of 182 genes (2000bp up-stream of TSS). With the help of text mining, 21 genes of them were validated to be methylation relevant cancer related genes (Table). In order to evaluate the distinguish ability of DNA methylation from cancer to the normal samples, Random forest model were applied. In the procedure of RF model, the optimal number of variables tried at each split is131 (mtry) and number of trees (mtrees) makes no difference to the prediction accuracy from 250 to 7000. RF prediction model showed 1592 regions could provide positive ability to distinguish cancer samples from normal samples with sensitivity of 97.06%, specificity of 100% and accuracy of 97.37%. Furthermore, we found the top 206 most importance regions could take account of 80.0% contribution to the accurate prediction. And the random forest model based on top 206 regions with mtry of 14 and mtrees of 500 showed better performance with 100% sensitivity, 100% specificity and 100% accuracy. The result showed these 206 regions were high frequent hypermethylated in cancers (Mean=41.4%, SD=14.5%, IQR=22.4%). (Supplementary Table **) For CapSeq dataset, 31 cancer 24 normal samples were included. 86206 regions were obtained by methylation haplotype loading algorithm (MHL), however, only 2205 regions were 100% un-methylated in 24 normal samples. Parameters of Random forest model were tuned and the optimal number of variables tried at each split is 92 (mtry) and number of trees (mtree) makes no difference to the prediction accuracy from 250 to 7000. Random forest algorithm showed 516 regions could provide positive ability to separate cancer samples from normal samples with sensitivity of 87.1%, specificity of 75.0% and accuracy of 81.26%. We found that the top 21 most importance regions could take account of 80.0% contribution for the classification. Therefore, the second round prediction process of random forest model based on top 21 regions with mtry of 7 and mtrees of 750 showed perfect classification with 96.77% sensitivity, 100% specificity and 98.18% accuracy. The average hyper-methylation frequency in cancer samples for above 516 and 21 regions 90.43% (SD=0.08, IQR=0.096) and 96.7% (SD=0.041, IQR=0.056), respectively. (Supplementary Table ** and supplementary Table **) For BSPP dataset, 16 cancer plasma and 16 normal plasma samples were included. 36281 regions were obtained by methylation haplotype loading algorithm (MHL) and 4194 regions were 100% un-methylated in 16 normal samples. All the regions hypermethylated in cancer and 100% un-methylated in normal samples were collected from RRBS, SeqCap and BSPP dataset and 30 regions were found un-methylation in all the normal samples of the 3 dataset. These 30 regions could explain 93.75% cancers incidence and the specificity is 100%. The methylation frequency of these 30 regions ranged from 6.25% to 25% in free-cell circulating DNA while it is hypermethylated in 1.47%-44% RRBS cancer samples and 6.45%-77% SeqCap cancer samples. These 30 regions were located in the promoter or gene body of 24 genes, including FGF19, MEF2C-AS1, MEF2C, HABP2, AGO3, TAF15, ZWILCH, YTHDF3-AS1, ZNF213, C9orf114, C14orf28, ADAM11, CDKN2AIP, TMEM87B, CDKL3, MSH5, MSH5-SAPCD1, UBC, PCM1, PCK2, NR2F6, JADE2, KANK1 and MCM3. At least 12 genes have been demonstrated to be associated with human cancers with the annotation from NCBI (HABP2, FGF19, MEF2C, AGO3, CDKL3, MCM3, TAF15, PCK2, NR2F6, MSH5, ZWILCH and PCM1). For example, the FGF19-FGFR4 signaling axis has been implicated in the pathogenesis of several cancers in mice and potentially in humans (7) while HABP2 has been found to be hypermethylated in hepatocellular carcinomas cancer (8). AGO3 was found significant decreased in human liver cancer (9). KANK1 re-expression induced by 5-Aza-2'-deoxycytidine could suppresses nasopharyngeal carcinoma cell proliferation and promotes apoptosis (10). Whole-exome sequencing identifies mutated PCK2 associated with carcinoma cell proliferation in a hepatocellular carcinoma patient(11) ===Genome-wide DNA methylation profile comparison between solid tissue and circulating free DNA methylation in plasma=== All the assumption of we can used DNA methylation in free-cell circulating DNA methylation is based on the methylation status of solid cancers can be released into plasma without any specific selection, thus, the aberrant DNA methylation fragment or biomarkers identified in solid tissues can be used to be the target for cancer non-invasive diagnosis, screening or prognosis surveillance. We then constructed 15 genome-wide DNA methylation profiles for the paired plasmas and solid tissues from same cancer patient with RRBS assay to check whether DNA methylation fragments derived from cancer cells were released into the blood randomly or selectively. 5 paired lung cancer, colon cancer and pancreatic cancer solid tissues and plasma were enrolled in this section and single-base methylome were established by RRBS as mentioned above. Correlation analysis based on 56046 MHL regions showed there is just weak correlation between genome-wide DNA methylation of solid tissues and plasmas. The average correlation between tissue and plasma was 0.50 (95%CI: 0.44-0.56)(Figure). What’s more, only about 28% (95%CI: 25%-31%) regions were simultaneously methylated both in tissues and plasma. Even considering different threshold to define the methylation status and transferring the continuous MHL to binary DNA methylation status, the maximum co-methylation ratio was still less than 63% (Figure). These evidence showed the dramatically significant heterogeneity between solid and plasma DNA methylation profile and provide the importance that we should validate all the biomarkers identified in solid tissues in cell-free circulating plasma DNA to guarantee the validity of the biomarkers. 25204 regions which was methylated in sample pairs for at least one time were selected to evaluate the selectivity of hyper-methylated DNA fragments from solid tissues to plasma. The results showed the process of the releasing were non-random, with at least 1590 fragment were significantly prefer-selected (P<1.98*10-6, binomial test). These fragments were located in the regions of 1190 genes. Function enrichment analysis showed these genes were significantly associated with cancer relevant biological functions (Table), including embryonic morphogenesis, regulation of transcription, neuron differentiation, regionalization, tissue morphogenesis ,transcription factor activity, sequence-specific DNA binding ,transcription regulator activity. [[File:1155.tm.png|400px]] ===Re-validation of the hyper-methylated fragments of plasma in public GEO dataset. === GSE56044 (124 lung cancer and 12 normal), GSE39279 (444 lung cancer), GSE52401 (244 normal lung), TCGA-lung cancer (), TCGA-colon cancer () and TCGA-pancreatic cancer () dataset were downloaded and summarized. === Quantitatively estimation to the level of cancer DNA fractions in the plasma === By computationally mixing the sequencing reads from cancer tissues and whole blood samples (WBC), we created synthetic admixtures at various levels of tumor fraction. We found that MHL is 2-5 folder higher than the methylation level of individual CpG sites across the full range of tumor fractions. Remarkably, MHL provides additional gain of signal-to-noise ratio (mean divided by standard deviation) compared with AMF as the fraction of tumor DNA decreases below 10%, which is typical for clinical samples. We then took the individual plasma data sets, and predicted the tumor fraction based on the MHL distribution established by computational mixing. Except for a small number (N<5) of outliers, we observed significantly higher average MHL in cancer plasma than in normal plasma. Note that all Group II MHBs were selected without using any information from the plasma samples, and hence they should be generally applicable to other plasma samples. Interestingly, we also found that the estimated tumor DNA fraction were positive correlated with normalized cfDNA yield from the cancer patients (P<0.000023). ===Quantitatively estimation to the level of cancer DNA fractions in the plasma=== Recent studies have demonstrated that epigenetic information imbedded in cfDNA has the potential for predicting tumor’s tissue-of-origin. Consistently, we found that tissue-of-origin derived methylation haplotypes were the most abundant fraction in cancer plasma. Here we asked whether a MHL-based framework and a set of targets derived from whole genome data would allow us to predict tissue-of-origin with quantifiable sensitivity and specificity, which is crucial for future clinical applications. We compiled 43 WGBS and RRBS data sets for 10 human normal tissues that have high cancer incident rate, and identified a set of 2,880 tissue-specific MHBs as the candidates. We then used these tissue-specific MHBs or subsets to predict the tissue-of-origin for the cancer plasma sample. We adopted the approach by counting the total number of tissue-specific MHBs in the plasma samples and comparing with all other tissues, in order to infer the most probable tissue-of-origin. At the cutoff of minimal 10 tissue-specific methylated haplotypes per tissue type, we observed an average 90% accuracy for mapping a data set from the primary tissue to its tissue type (Figure 5, middle). We then applied this method to the full set of plasma data from 59 cancer patients and 75 normal individuals, and achieved an average prediction accuracy of 82.8%, 88.5%, 91.2% for the plasma from colorectal cancer, lung cancer, and control plasma samples respectively with 5-fold cross-validation. For the incorrectly classified samples, we noticed that 4 out of 5 colorectal cancer plasma were from metastatic colorectal cancer patients while the fifth was in fact tubular adenoma. In the case of lung cancer, one misclassified sample came from a patient with benign fibrous tissue. Our invention demonstrate these biomarkers could be useful for tissue-of-origin mapping and other related applications. === Tissue-of-origin mapping by normal, cancer and inflammation reference === * Dr. Zhang said maybe we can integrate cancer,normal and inflammation samples as the reference so that we can mapping the plasma to differnet tissue, different stage, just like the 'GPS' * Take the normal samples as the background and don't forget to do the cross-validation and then estimate each plasma to differenet refernece and then give the pvalue * Very important and interesting idea come out from Dr. Zhang again. Dr. Zhang said we can merge one disease reference and one normal reference and for that diease, we would get more strong signals,compared with single reference (10/16/2016). * differrent stastic Z-score, Baysian score. * kedney cancer as the reference since we have samples.
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