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一周小结

一周小结. 时间：2013年8月23日. DNA 甲基化检测的方法. 大 体分为两个步骤 : (1)待 检测样品的前期处理 (2)目标序列的定位和甲基 化状态的量化. 亚硫酸氢钠 限制性内切酶 利用特定抗体对甲基化的胞 嘧啶进行免疫沉淀反应. Classification of Individual Lung Cancer Cell Lines Based on DNA Methylation Markers. 时间：2004年 期刊： ournal of Molecular Diagnostics 分区：二区 影响因子：3.576

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一周小结

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  1. 一周小结 时间：2013年8月23日

  2. DNA 甲基化检测的方法 • 大体分为两个步骤: (1)待检测样品的前期处理 (2)目标序列的定位和甲基化状态的量化 • 亚硫酸氢钠 • 限制性内切酶 • 利用特定抗体对甲基化的胞嘧啶进行免疫沉淀反应

  3. Classification of Individual Lung Cancer Cell LinesBased on DNA Methylation Markers • 时间：2004年 • 期刊：ournal of Molecular Diagnostics • 分区：二区 • 影响因子：3.576 • 主要内容：utility of linear discriminant analysis and artificial neural networks as classificatory tools of DNA methylation profiles, in an effort to develop diagnostic models that could distinguish SCLC from NSCLC

  4. step: • The percentage methylated refer-ence (PMR) for each locus was calculated by dividing theGENE:reference ratio of a sample by the GENE:referenceratio of highly methylatedSssI-treated human sperm DNAand multiplying by 100. • utility of linear discriminant analysis and artificial neural networks • The PMR data from 20 loci was subjected to backward step-wise analysis to eliminate the variables

  5. CpG_MPs: identification of CpG methylationpatterns of genomic regions from high-throughputbisulfite sequencing data • 时间：2013年 • 期刊：Nucleic Acids Research • 分区：二区 • 影响因子：8.026 • 主要内容：developed a comprehensive tool, CpG_MPs, for identification and analysis of the methylation patterns of genomic regions from bisulfite sequencing data.

  6. Workflow

  7. Calculation of the methylation level of CpGs • hotspot extension algorithm (i) unmethylated CpGs withmethylation levels <0.3 (ii) partially unmethylated CpGsranging from 0.3 to 0.5 (iii) partially methylated CpGsranging from 0.5 to 0.7 (iv) methylated CpGs whosemethylation levels>0.7

  8. Step 1: Convert the normalized methylation level ofCpGs into the methylation status of CpGs. • Step 2: Scan CpGs from a 5' to3' direction to extractthe genomic regions including at least n successivelyunmethylated (methylated) CpGs as unmethylated(methylated) hotspots. • Step 3: Extend the unmethylated (methylated) hotspotsupstream and downstream to incorporate unmethylated (methylated) or partially unmethylated (methy-lated) CpGs into the hotspots as unmethylatedregions, until methylated (unmethylated) or partiallymethylated (unmethylated) CpGs are met. • Step 4: Combine two neighboring genomic regions withthe same methylatiopattern together if their distanceis <200 bp. • Step 5: Compute the mean value and standard deviationof methylation level of CpGs in each unmethylated/methylated region

  9. Identification of CMRs and DMRs

  10. calculate the sample-methylation patterns ofoverlapping regions (ORs) in thereference genome are recorded defined to deter-mine the methylation patterns of ORs across multiplesamples assess the overlapping ratio of thenumber of samples

  11. Sequence features of genomic regions ofdifferent methylation patterns • length, GC content and CpG ratio

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