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Thread | Thread Starter | Forum | Replies | Last Post |
ChIP-Seq: Enabling Data Analysis on High-Throughput Data in Large Data Depository Usi | Newsbot! | Literature Watch | 1 | 04-18-2018 09:50 PM |
How to use SVA for removing noise from WGBS data | yu_chem | Bioinformatics | 0 | 04-10-2018 09:47 AM |
Low mapping efficiency of WGBS data for DNA methylation | naveed.jhamat | Bioinformatics | 3 | 11-17-2016 07:30 AM |
Software tools for comparing entire methylomes (WGBS) | anth | Bioinformatics | 3 | 12-10-2013 03:25 PM |
[NGS - analysis of gene expression data] Machine Learning + RNAseq data | Chuckytah | Bioinformatics | 7 | 03-05-2012 03:16 AM |
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#1 |
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Location: Japan Join Date: Mar 2015
Posts: 20
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Hello
I'm analyzing WGBS (bulk) data from ENCODE project. I would like to know how do many researchers analyze WGBS data. When I extracted CpG sites having > 10 coverage on 20 samples, CpG sites having >10 coverage on more samples than 70% of all samples are approx. 30 % of all CpG sites. so if one region be focused, the methylation information of the region contains many missing value. I think that imputation can not be applied, because of numerousness of missing value. So should I treat average methylation rate on each focused regions and compare with average methylation rate ? and additional question is that How bacth effect and blood contamination effect are removed? I'm beginner. so I need information (paper, web sites shows analysis process) I hope your help thank you |
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#2 |
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Location: usa Join Date: Apr 2018
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