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Kun:LabNotes/CpgSeq/2008-5-23
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==Informatics== ===Read mapping=== Should generate three files: #Haplotype file: xxx.methylHap.txt ##One molecule per line; ##target_id, offset1, methylotype1, offset2, methylotype2... #Methylation level file, average methylation level per site: xxx.methylFreq.txt #Methylation LD statistics file, all LD statistics: xxx.methylLD.txt ===Mock read generation=== *50% methylation at every CpG site; *No correlation between adjacent CpG; *Read position is evenly distributed; *Can generate both single reads and pair-end reads; *Incorporate an error model for sequencing; *Should generate one raw read file and one methylHap file. Perl Script: [[Media:mockReadsGenV1.txt]] (Note that the calculation of position on the reverse strand could be wrong.) *I generated mock reads at 50X coverage (3,414,819 reads). 98% were mapped back to the target sequences with SOAP with the following parameters: -v 5 -e 10000 -c 0 -f 8 -r 0 -s 9 *To find out whether the 2% unmappable reads were due to multiple mapping positions or too many C:T mismatches, I generated another set of mock reads from completely methylated sequences (so that there is no C:T mismatch). It turned out that 100% reads were successfully mapped to the targets, which means the mapping strategy is bias towards methylated sequence. *I generated a third set of mock reads from completely unmethylated sequences, and performed the mapping with the same parameters. Only 85% reads were mappable. In contrast, if I replace all CGs with TGs in the target sequences, 100% reads were mappable. *I think the best mapping strategy is to map the reads to the completely methylated targets, and to the completely unmethylated targets, then take the union of the mapping results. ==The mapping strategy for CGI bisulfite sequencing reads== #Split raw reads into N subsets and process them separately; for each subset: ##Map reads to completely methylated target sequences, load the mapping results into memory; ##Map reads to completely unmethylated target sequences, load the mapping results and combine the two sets using the following rules: ###Accept the mapping if a read is mapped to the same target regardless the methylation status; ###Accept the one with fewer mismatches if a read is mapped to two different targets depending on the methylation status. ###Accept the mapping if a read is mapped to a target sequences in one methylation state. ##Report the mapping results. Perl Script: [[Media:mapBisSeqReadsV1.txt]] *For 3.4 million mock reads that contain 50% randomly methylation CpG sites, 98% were mapped with this strategy. I mapped the remaining 2% reads using Blast, and found that these reads have multiple mapping positions, which means they are not mappable anyway. *The work flow of data analysis would be: Raw reads [[Media:toMethylFreq.txt|=>]] methylFreq [[Media:methylFreq2BED.txt|=>]] UCSC Genome graph + ==> [[Media:MapBisReadsTargets.txt|Read mapper]] { => correlate with gene expression Targets => haplotype analysis [[Media:toCgiStat.txt|=>]] summary statistics of CGI
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