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==2nd analysis updates== *Dinh mapped Read 1 and Read 2 separately, and report the mapping data in two separate bam files. In my first analysis, I extracted haplotypes from the two files and treat them separately. In fact each pair of reads came from one DNA molecule and should be combined for one single haplotype. Treating the two reads separately leads to double counting and also some short haplotypes that might be filtered out. *First I merged two bam files from each library, sorted the merged files and re-created the bam indexes: [[Media:140702_RRBS_batch_merge_N_sort.txt|batch_merge_N_sort.sh]] *Then I modified the [[Media: mergedBam2hapInfo_v2.txt|mergedBam2hapInfo.pl]] script in order to combine each pair of reads into one haplotype. Note that the fastq Read IDs are different between MiSeq and HiSeq data. It took me some debugging in order to process both types of data correctly. *Next I re-ran all the batch processing scripts, with two more changes: (i) I required that a cancer haplotype should have at least four methylated CpG sites (increase the stringency); (ii) Target regions that have at least 1 HMH are included (previously I required 2 or more). *With these changes, I managed to detect significant (Z score > 3) cancer load in five patient serum samples. Fraction of HMH in all haplotypes. [[Image:140702_RRBS_HMH_abundance_PE-haps.png]] Enrichment of HMH compared with normal plasma (based on Z-scores) [[Image:140702_RRBS_HMH_enrichment_PE-haps.png]]
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