| Autor: |
Shalima Nair, Phuc-Loi Luu, Wenjia Qu, Maddugoda, Madhavi, Huschtscha, Lily, Reddel, Roger, Chenevix-Trench, Georgia, Toso, Martina, Kench, James, Horvath, Lisa, Hayes, Vanessa, Stricker, Phillip, Hughes, Timothy, White, Deborah, Rasko, John, Wong, Justin, Clark, Susan |
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| Popis: |
Additional file 2. Figure 1: Box plot showing the difference in coverage across CpG islands, CpG shores and other regions of the genome for each of the five library preparation methods compared. Figure 2 a, b Two representative examples of regions showing SNP in both the WGS and WGBS data of the same clinical sample. Figure 3 a Bar plot showing the percentage of SNPs from WGBS concordant in WGS-GS at ~ 26× coverage and the percentage of SNPs from spike-in WGS concordant in WGS-GS at 30× coverage. b A representative Venn diagram for one prostate cancer sample, 2ab showing the number of SNPs concordant at 26× coverage for WGBS and 30× coverage for spike-in WGS when compared with WGS-GS data. Figure 4 a, Plot showing the distribution of normalised frequency of number of SNPs called across regions of the genome with varying levels of GC content for WGBS and WGS-GS. b Plot showing the distribution of normalised frequency of number of SNPs called across regions of the genome with varying levels of methylation ratio of CpG sites nearest to a SNP within 50 bp. Figure 5 a, b Box plot showing the coverage distribution across exons, intergenic regions, introns, promoter regions and repeat regions of the genome for a cell line sequenced on one lane of HiSeq X Ten (a) and HiSeq 2500 (b). Figure 6 a, b Two examples showing the difference in distribution of reads for a FFPET library obtained from the TruMethyl WG method and Accel-NGS Methyl-Seq method across a CpG island. Figure 7 a, b Summary of workflow for achieving optimal coverage on the HiSeq X Ten for intact genomic DNA and FFPET DNA. |
| Databáze: |
OpenAIRE |
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