Kun:LabNotes/MONOD/2015-3-11: Difference between revisions
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**The next question is what data shall we use to define the bins. To be completely unbiased, we want to use WGBS data in the ideal situation. We have the N37 WGBS data from ten human tissues, plus the Heyn2013 whole blood WGBS data. All these data have been mapped. The Epigenomics Roadmap project just release a large number of WGBS data sets, but Dinh hasn't completed the mapping. These are all from non-cancerous tissues. I think we need to include some cancer data as cancer epigenome is highly screwed up and not represented by any normal tissue. So I would include our own primary tumor data generated by RRBS and SeqCap. | **The next question is what data shall we use to define the bins. To be completely unbiased, we want to use WGBS data in the ideal situation. We have the N37 WGBS data from ten human tissues, plus the Heyn2013 whole blood WGBS data. All these data have been mapped. The Epigenomics Roadmap project just release a large number of WGBS data sets, but Dinh hasn't completed the mapping. These are all from non-cancerous tissues. I think we need to include some cancer data as cancer epigenome is highly screwed up and not represented by any normal tissue. So I would include our own primary tumor data generated by RRBS and SeqCap. | ||
**To obtain methylation haplotype blocks with [[Media:hapInfo2mld_blocks.txt|hapInfo2mld_blocks.pl]], we need haplotypes in the hapInfo format, which would require some sort of target definition in the first place. A reasonable set of targets to start with could be uniquely mappable and sequencible regions in the human genome. In other words, the genome is already partitioned into segments by those repetitive reads. For this purpose, I simply took the N37 WGBS data, reported the read coverage, and identified regions of at least 80bp in size with RD>=10 ([[Media:N37_10_tissues_WGBS_batch_get_hapInfo.txt| Batch processing script]]). | **To obtain methylation haplotype blocks with [[Media:hapInfo2mld_blocks.txt|hapInfo2mld_blocks.pl]], we need haplotypes in the hapInfo format, which would require some sort of target definition in the first place. A reasonable set of targets to start with could be uniquely mappable and sequencible regions in the human genome. In other words, the genome is already partitioned into segments by those repetitive reads. For this purpose, I simply took the N37 WGBS data, reported the read coverage, and identified regions of at least 80bp in size with RD>=10 ([[Media:N37_10_tissues_WGBS_batch_get_hapInfo.txt| Batch processing script]]). | ||
**Partititioning methylation haplotypes and generate summary statistics ([[Media:Batch_WGBS_RRBS_CapSeq_mld_block_partitioning.txt| Batch processing script]]). |
Revision as of 23:08, 19 March 2015
Data analysis: defining bins based on methylation haplotype blocks
- I wanted to be more systematic in analyzing the combined data from RRBS, SeqCap and BSPP experiments.
Define informative bins for haplotype analysis
- A key is to define a set of bins (or windows) across the genome for haplotype extraction.
- My previous approaches were somewhat ad hoc, mostly based on read coverage in each group of data sets. I did try a number of different ways to split bigger windows into smaller ones for RRBS and SeqCap data, but none was systematic.
- The thought here is that we want to define a bin where there is some internal methylation haplotype structure. In other words, every CpG site should be linked to other sites in the same bin. So if we use the conception of methylation LD, the pair-wise LD for all sites in a bin should be above a certain threshold. Formally, we can partition all CpG sites in the entire genome into methylation LD blocks. Each block would be a bin for MONOD. I wrote a script hapInfo2mld_blocks.pl for this purpose.
- The next question is what data shall we use to define the bins. To be completely unbiased, we want to use WGBS data in the ideal situation. We have the N37 WGBS data from ten human tissues, plus the Heyn2013 whole blood WGBS data. All these data have been mapped. The Epigenomics Roadmap project just release a large number of WGBS data sets, but Dinh hasn't completed the mapping. These are all from non-cancerous tissues. I think we need to include some cancer data as cancer epigenome is highly screwed up and not represented by any normal tissue. So I would include our own primary tumor data generated by RRBS and SeqCap.
- To obtain methylation haplotype blocks with hapInfo2mld_blocks.pl, we need haplotypes in the hapInfo format, which would require some sort of target definition in the first place. A reasonable set of targets to start with could be uniquely mappable and sequencible regions in the human genome. In other words, the genome is already partitioned into segments by those repetitive reads. For this purpose, I simply took the N37 WGBS data, reported the read coverage, and identified regions of at least 80bp in size with RD>=10 ( Batch processing script).
- Partititioning methylation haplotypes and generate summary statistics ( Batch processing script).