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== to do list == *Prepare for better version: *(1) Perform additional quality filtering of our data, remove some low-quality samples, and repeat the analysis. *(2) Determine how low (percentage of tumor DNA in blood) can we go in tissue-of-origin mapping, and ctDNA detection, by computational mixing of tumor data with WB data at various ratios, and perform the analysis similar to Figure 4. *(3) Look out for additional WGBS data in the public database. *(4) Look further into the potential differences of the spreading pattern between adding and removing methyl groups. *(5) Try reference-free-deconvolution to detect tumor-DNA from plasma *(6) Cancer-specific methylation haplotype (here, we can include all the genomic region, not only in MHB) and then estimate the cancer fragment contribution. *(7) Any biological function for the interesting tissue-specific MHBs in the prediction model. *(8) The overlap between our prediction features and other studies? *(9) With our prediction model to predict some other public dataset and test the model stability. *(10) Dr. Zhang mentioned that we actually have more data, can we get more interesting result from these sequencing data: Yes. I just realized that we also have another set of data that wasnβt used in our NG paper. The data were generated with a different strategy, and my gut feeling is that it might have a higher unique read depth per sample than our RRBS data. Can you process them similar to what you did in Supp. Figures 11/12? /media/Home_Raid1/kunzhang/MONOD/150209_SN216/SeqCap/mld_blocks_July2015 http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2015-3-4 http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2015-7-6#Update_on_10.2F16.2F2015 Thanks!
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