18 research outputs found
Additional file 2: of Differential methylation at MHC in CD4+ T cells is associated with multiple sclerosis independently of HLA-DRB1
Gene set analysis of top CpG hits. Output of results from ORA analysis using Webgestalt. (XLSX 46.8 kb
Additional file 3: of Differential methylation at MHC in CD4+ T cells is associated with multiple sclerosis independently of HLA-DRB1
Individual methylation (Beta value) distribution for top hit. Plots showing the distribution of beta values for case and control group for the top CpG in the RNF39 gene illustrating the DMP is not due to SNP genotype. An example of genotype influenced methylation spread is also shown for comparison. (PDF 113 kb
Additional file 2: of Erythrocyte microRNA sequencing reveals differential expression in relapsing-remitting multiple sclerosis
Table S2. Erythrocyte purity determined by flow cytometry (nâ=â10). Data is shown as mean percent positive events out of 20,000 events. (XLSX 12Â kb
Additional file 3: of Erythrocyte microRNA sequencing reveals differential expression in relapsing-remitting multiple sclerosis
Figure S1. Total erythrocyte RNA extracted from 10 ml whole blood by disease-modifying therapy. Mean total erythrocyte RNA yields from 10 ml whole blood for 2 patients off treatment, 2 on dimethyl fumarate, 13 on fingolimod, 12 on natalizumab and 27 healthy controls. Error bars represent standard deviation (SD). ** p < 0.01; *** p < 0.001. (PNG 41 kb
Additional file 1: of Erythrocyte microRNA sequencing reveals differential expression in relapsing-remitting multiple sclerosis
Table S1. Next-generation sequencing results of erythrocyte microRNAs for 9 healthy controls and 9 relapsing-remitting Multiple Sclerosis patients. (XLSX 27Â kb
Additional file 4 of Erythrocyte microRNA sequencing reveals differential expression in relapsing-remitting multiple sclerosis
Figure S2. Tukey boxplot of relative expression (2-deltaCt) of differentially expressed erythrocyte microRNAs in the sequencing cohort by disease-modifying therapy. Relative expression (2-deltaCt) (y-axis on a logarithmic scale) of differentially expressed erythrocyte miRNAs (x-axis) by disease-modifying therapy (fingolimod: n = 3; natalizumab: n = 2; dimethyl fumarate: n = 2; off treatment: n = 2) and including healthy controls (n = 5). The dots represent outliers defined as deviating ≥1.5 fold from the upper/lower quartile. (PNG 29 kb
Additional file 1 of VariantSpark: population scale clustering of genotype information
PGP population labels and implementation details. Conversion map self-reported descriptive population labels to super-population labels. Details of the R implementation. (PDF 22.2 kb
基于海泡石的细胞透性化
Additional file 2: Table S2. Statistics from analysis of the expression of EIG121 (KIAA1324) from Oncomine⢠Platform comparisons of breast cancer versus normal breast samples ( http://www.oncomine.com ). The values shown refer to over-expression of EIG121 in breast cancer cases compared to normal breast. The number (n) of breast cancer cases and normal breast samples are shown for each dataset
Additional file 1: Figure S1. of Next-generation sequencing reveals broad down-regulation of microRNAs in secondary progressive multiple sclerosis CD4+ T cells
Volcano plot of differentially expressed miRNAs identified with NGS. The FDR-corrected significance threshold is demarked with a green line at p < 1.2 × 10−4. Three miRNAs were identified at the threshold. Mean read counts were low in all three miRNAs: miR-451a (SPMS mean = 76.3, HC mean = 18.9), miR-1246 (SPMS mean = 94.9, HC mean = 51.9), and miR-144-5p (SPMS mean = 15.1, HC mean = 5.5). Differential expression could not be replicated with RT-qPCR. (PNG 57 kb
Additional file 1: of Prevalence of PALB2 mutations in Australian familial breast cancer cases and controls
Cohort information (Table). (DOCX 48 kb