diff --git a/.Rhistory b/.Rhistory index 1b74564..e4d3662 100644 --- a/.Rhistory +++ b/.Rhistory @@ -1,512 +1,26 @@ -test = list('est'=df) -View(test) -install.packages('GOplot') -install.packages("GOplot") -df <- read.csv('kegg_serum.csv') -View(df) -# # Building the circ object -circ <- circle_dat(EC$david, EC$genelist) -## Not run: -# # Load the included dataset -data(EC) -library('GOplot') -## Not run: -# # Load the included dataset -data(EC) -circ <- circle_dat(EC$david, EC$genelist) -View(circ) -df <- read.csv('kegg_serum.csv') -GOCircle(df) -summary(df) -GOCircle(df) -install_github('wencke/wencke.github.io') -library(devt) -library(devtools) -install_github('wencke/wencke.github.io') -library(devtools) -install_github('wencke/wencke.github.io') -devtools::install_github("wencke/wencke.github.io") -install.packages('htmltools') -install.packages("htmltools") -library(htmltools) -devtools::install_github("wencke/wencke.github.io") -install.packages('GOplot') -df <- read.csv('kegg_serum.csv') -GOCircle(df) -GOCircle(df) -library(GOplot) -df <- read.csv('kegg_serum.csv') -GOCircle(df) -detach("package:base", unload = TRUE) -detach("package:GOplot", unload = TRUE) -library(GOplot) -devtools::install_github("wencke/wencke.github.io") -df <- read.csv('kegg_serum.csv') -GOCircle(df) -df <- read.csv('kegg_serum.csv') -View(df) -GOCircle(df) -install.packages('corrplot') -library(corrplot) -setwd("C:/Data/Analysis/DAGMAR_IIH/IIH_Serum") -data = read.csv("corr_serum.csv") -M = cor(data) -mode(data) -data = as.matrix(data) -mode(data) -df = read.delim(file = 'corr_serum.tsv') -M = cor(df) -df = read.delim(file = 'corr_serum.tsv', header=T) -M = cor(df) -mode(df) -df = read.delim(file = 'corr_serum.tsv', header=TRUE, row.names = 1) -M = cor(df) -corrplot(M, method = 'color') -# Objective -- Enrichment of formerly sialylated N-linked (SAN-) glycopeptides using MagReSyn® Zr-IMAC HP -setwd("C:/Data/Projects/PTMs/MISAME3/Feb_2024_MISAME/NMP") -# download package from bioconductor -if (!requireNamespace('BiocManager', quietly = TRUE)) -install.packages('BiocManager') -BiocManager::install('EnhancedVolcano') -# load the package -library(EnhancedVolcano) -df = read.delim(file = 'volcano.tsv', header=TRUE, row.names = 1) -# download package from bioconductor -if (!requireNamespace('BiocManager', quietly = TRUE)) -install.packages('BiocManager') -BiocManager::install('EnhancedVolcano') -# load the package -library(EnhancedVolcano) -df = read.delim(file = 'volcano.tsv', header=TRUE, row.names = 1) -# download package from bioconductor -if (!requireNamespace('BiocManager', quietly = TRUE)) -install.packages('BiocManager') -BiocManager::install('EnhancedVolcano') -# load the package -library(EnhancedVolcano) -df = read.delim(file = 'volcano.tsv', header=TRUE, row.names = 1) -View(df) -setwd("C:/Data/Projects/PTMs/MISAME3/Feb_2024_MISAME/NMP") -df = read.delim(file = 'volcano.tsv', header=TRUE, row.names = 1) -View(df) -df = read.delim(file = 'volcano.tsv', header=TRUE, row.names = 1) -View(df) -EnhancedVolcano(df, -lab = rownames(df), -x = 'log2FoldChange', -y = 'pvalue', -selectLab = c('PGRP2_HUMAN','ECM_HUMAN','PSG7_HUMAN', -'LG3BP_HUMAN','BTD_HUMAN','FBLN!_HUMAN'), -xlab = bquote(~Log[2]~ 'fold change'), -pCutoff = 0.1, -FCcutoff = 0.1, -pointSize = 4.0, -labSize = 6.0, -labCol = 'black', -labFace = 'bold', -boxedLabels = TRUE, -colAlpha = 1, -legendPosition = 'right', -legendLabSize = 8, -legendIconSize = 4.0, -drawConnectors = TRUE, -widthConnectors = 1.0, -colConnectors = 'black',xlim=c(-2, 2), ylim = c(0,8)) -EnhancedVolcano(df, -lab = rownames(df), -x = 'log2FoldChange', -y = 'pvalue', -selectLab = c('PGRP2_HUMAN','ECM_HUMAN','PSG7_HUMAN', -'LG3BP_HUMAN','BTD_HUMAN','FBLN!_HUMAN'), -xlab = bquote(~Log[2]~ 'fold change'), -pCutoff = 0.05, -FCcutoff = 1, -pointSize = 4.0, -labSize = 6.0, -labCol = 'black', -labFace = 'bold', -boxedLabels = TRUE, -colAlpha = 1, -legendPosition = 'right', -legendLabSize = 8, -legendIconSize = 4.0, -drawConnectors = TRUE, -widthConnectors = 1.0, -colConnectors = 'black',xlim=c(-2, 2), ylim = c(0,8)) -EnhancedVolcano(df, -lab = rownames(df), -x = 'log2FoldChange', -y = 'pvalue', -selectLab = c('PGRP2_HUMAN','ECM_HUMAN','PSG7_HUMAN', -'LG3BP_HUMAN','BTD_HUMAN','FBLN!_HUMAN'), -xlab = bquote(~Log[2]~ 'fold change'), -pCutoff = 0.05, -FCcutoff = 1, -pointSize = 4.0, -labSize = 6.0, -labCol = 'black', -labFace = 'bold', -boxedLabels = TRUE, -colAlpha = 1, -legendPosition = 'right', -legendLabSize = 8, -legendIconSize = 4.0, -drawConnectors = TRUE, -widthConnectors = 1.0, -colConnectors = 'black',xlim=c(-2, 2), ylim = c(0,3)) -View(data) -setwd("C:/Users/BhosaleS/Downloads") -df = read.delim(file = 'corr.tsv', header=TRUE, row.names = 1) -df = read.delim(file = 'corr.tsv', header=TRUE, row.names = 1) -View(df) -df = read.delim(file = 'corr.tsv', header=TRUE) -View(df) -View(df) -df = read.delim(file = 'corr.tsv', header=TRUE, row.names = 1) -View(df) -sp <- ggscatter(df, x = "CACNA2D3", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE # Add confidence interval -) -install.packages('ggscatter') -library(ggscatter) -install.packages("ggScatRidges") -library(ggScatRidges) -library(ggscatter) -install.packages('ggscatter') -install.packages("ggpubr") -install.packages("ggpubr") -library(ggscatter) -library(ggscatter) -library(ggpubr) -library(ggscatter) -library(ggscatter) -install.packages("ggscatter") -library(ggscatter) -sp <- ggscatter(df, x = "CACNA2D3", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE # Add confidence interval -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp <- ggscatter(df, x = "CACNA2D3", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(10, 13), # setting up the x and y-axis dimension -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp <- ggscatter(df, x = "CACNA2D3", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(10.5, 13), # setting up the x and y-axis dimension -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) -sp + stat_cor(aes(color = group), label.x = 3) -sp <- ggscatter(df, x = "CACNA2D3", y = "ICP_cm_H2O", -color = "group", palette = "jco", -add = "reg.line", conf.int = TRUE) -sp + stat_cor(aes(color = group), label.x = 3) -sp <- ggscatter(df, x = "CACNA2D3", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(10.5, 13), # setting up the x and y-axis dimension -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) -sp <- ggscatter(df, x = "CACNA2D3", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(10.5, 13), # setting up the x and y-axis dimension -group = rep(c('IIH', 'Con'), each = 15), -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_line(aes(col = group)) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_dotplot(aes(col = group)) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -library(ggscatter) -esquisser() -library(esquisse) -library(esquisse) -esquisser() -df = read.delim(file = 'corr.tsv', header=TRUE, row.names = 1) -ggplot(df) + -aes(x = CACNA2D3, y = ICP_cm_H2O, colour = group) + -geom_point(shape = "circle", size = 1.5) + -scale_color_hue(direction = 1) + -theme_minimal() -ggplot(df) + -aes(x = CACNA2D3, y = ICP_cm_H2O, colour = group) + -geom_point(shape = "circle", size = 3) + -scale_color_hue(direction = 1) + -theme_minimal() -ggplot(df) + -aes(x = CACNA2D3, y = ICP_cm_H2O, colour = group, add = "reg.line") + -geom_point(shape = "circle", size = 3) + -scale_color_hue(direction = 1) + -theme_minimal() -df = read.delim(file = 'corr_basp1.tsv', header=TRUE, row.names = 1) -setwd("C:/Users/BhosaleS/Downloads") -df = read.delim(file = 'corr_basp1.tsv', header=TRUE, row.names = 1) -df = read.delim(file = 'corr_basp1.tsv', header=TRUE, row.names = 1) -View(df) -df = read.delim(file = 'corr_basp1.tsv', header=TRUE, row.names = 1) -View(df) -sp <- ggscatter(df, x = "BASP1", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(14, 17), # setting up the x and y-axis dimension -group = rep(c('IIH', 'Con'), each = 15), -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -sp <- ggscatter(df, x = "BASP1", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(14, 17), # setting up the x and y-axis dimension -group = rep(c('IIH', 'Con')(c('49','32'))), -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -sp <- ggscatter(df, x = "BASP1", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(14, 17), # setting up the x and y-axis dimension -group = rep(c('IIH',49),('Con',32)) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -sp <- ggscatter(df, x = "BASP1", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(14, 17), # setting up the x and y-axis dimension -group = rep(c('IIH', 'Con'), each = 49), -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -df = read.delim(file = 'corr_basp1.tsv', header=TRUE, row.names = 1) -View(df) -group = c(rep("IIH",49), rep("Con",32)) -sp <- ggscatter(df, x = "BASP1", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(14, 17), # setting up the x and y-axis dimension -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -df = read.delim(file = 'corr_gmcl2.tsv', header=TRUE, row.names = 1) -View(df) -group = c(rep("IIH",49), rep("Con",32)) -sp <- ggscatter(df, x = "BASP1", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(14, 17), # setting up the x and y-axis dimension -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp <- ggscatter(df, x = "GMCL2", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(14, 17), # setting up the x and y-axis dimension -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -df = read.delim(file = 'corr_gmcl2.tsv', header=TRUE, row.names = 1) -View(df) -group = c(rep("IIH",49), rep("Con",32)) -sp <- ggscatter(df, x = "GMCL2", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(14, 17), # setting up the x and y-axis dimension -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -df = read.delim(file = 'corr_basp1.tsv', header=TRUE, row.names = 1) -View(df) -group = c(rep("IIH",49), rep("Con",32)) -sp <- ggscatter(df, x = "GMCL2", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(14, 17), # setting up the x and y-axis dimension -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp <- ggscatter(df, x = "BASP1", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(14, 17), # setting up the x and y-axis dimension -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp <- ggscatter(df, x = "BASP1", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -df = read.delim(file = 'corr_basp1.tsv', header=TRUE, row.names = 1) -View(df) -group = c(rep("IIH",49), rep("Con",32)) -sp <- ggscatter(df, x = "BASP1", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -xlim=c(14, 17), # setting up the x and y-axis dimension -group = group, -) -sp <- ggscatter(df, x = "BASP1", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -# setting up the x and y-axis dimension -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -# Add correlation coefficient -sp + stat_cor(method = "spearman", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -df = read.delim(file = 'corr_basp1.tsv', header=TRUE, row.names = 1) -View(df) -group = c(rep("IIH",49), rep("Con",32)) -sp <- ggscatter(df, x = "BASP1", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -sp + xlim=c(14, 17) # setting up the x and y-axis dimension -sp <- ggscatter(df, x = "BASP1", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -df = read.delim(file = 'corr_CACNA2D3.tsv', header=TRUE, row.names = 1) -View(df) -group = c(rep("IIH",49), rep("Con",32)) -sp <- ggscatter(df, x = "CACNA2D3", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -df = read.delim(file = 'corr_islr.tsv', header=TRUE, row.names = 1) -View(df) -group = c(rep("IIH",49), rep("Con",32)) -sp <- ggscatter(df, x = "ISLR", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -df = read.delim(file = 'corr_prg4.tsv', header=TRUE, row.names = 1) -View(df) -group = c(rep("IIH",49), rep("Con",32)) -sp <- ggscatter(df, x = "PRG4", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -df = read.delim(file = 'corr_prg4.tsv', header=TRUE, row.names = 1) -View(df) -group = c(rep("IIH",49), rep("Con",32)) -sp <- ggscatter(df, x = "PRG4", y = "BMI", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -df = read.delim(file = 'corr_mdh.tsv', header=TRUE, row.names = 1) -View(df) -group = c(rep("IIH",49), rep("Con",32)) -sp <- ggscatter(df, x = "MDH", y = "BMI", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -sp <- ggscatter(df, x = "MDH1", y = "BMI", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp <- ggscatter(df, x = "MDH1", y = "ICP_cm_H2O", -add = "reg.line", # Add regressin line -add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line -conf.int = TRUE, # Add confidence interval -group = group, -) -# Add correlation coefficient -sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) -sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) + geom_point(aes(col = group)) -if(!requireNamespace("devtools", quietly = TRUE)){ -install.packages("devtools") -} -devtools::install_github("marseille-proteomique/DIAgui") -library(DIAgui) -library(DIAgui) -runDIAgui() -runDIAgui() +install.packages('rmarkdown') +library(rmarkdown) +library(rmarkdown) +render("path/to/file.Rmd") +file.exists("~/.Rprofile") +library(rmarkdown) +library(rmarkdown) +library(rmarkdown) +install.packages('downlit') +install.packages('xml2') +library(downlit) +library(xml2) +library(rmarkdown) +library(rmarkdown) +install.packages('rmarkdown') +install.packages("rmarkdown") +library(rmarkdown) +install.packages('rmarkdown') +install.packages('downlit') +install.packages('xml2') +library(rmarkdown) +library(downlit) +library(xml2) +setwd("C:/Users/BhosaleS/Documents/GitHub/santoshdbhosale.github.io") +utils::packageVersion("rmarkdown") +utils::packageVersion("rmarkdown") diff --git a/.Rproj.user/224B9E6C/jobs/70B54F46-output.json b/.Rproj.user/224B9E6C/jobs/BB71370C-output.json similarity index 66% rename from .Rproj.user/224B9E6C/jobs/70B54F46-output.json rename to .Rproj.user/224B9E6C/jobs/BB71370C-output.json index 9a5840c..74c7812 100644 --- a/.Rproj.user/224B9E6C/jobs/70B54F46-output.json +++ b/.Rproj.user/224B9E6C/jobs/BB71370C-output.json @@ -1,4 +1,4 @@ -[2,"\n"] +[1,"==> quarto preview --render all --no-watch-inputs --no-browse\n\n"] [1,"Rendering:\n"] [1,"\r[ 1/10] 404.qmd\n"] [1,"\r[ 2/10] about\\index.qmd\n"] @@ -9,11 +9,14 @@ [1,"\r[ 7/10] blog\\Serum-Proteomics-Atherosclerosis\\index.qmd\n"] [1,"\r[ 8/10] blog\\Serum-Proteomics-Pre-diabetic\\index.qmd\n"] [1,"\r[ 9/10] cv\\index.qmd\n"] -[1,"Error in loadNamespace(name) : there is no package called 'rmarkdown'\r\nCalls: :: ... loadNamespace -> withRestarts -> withOneRestart -> doWithOneRestart\r\nExecution halted\r\n"] -[1,"WARNING: Unable to perform code-link (code-link requires R packages rmarkdown, downlit, and xml2)\n\r[10/10] index.qmd\n"] +[1,"failed to create process.\r\nWARNING: Unable to perform code-link (code-link requires R packages rmarkdown, downlit, and xml2)\n"] +[1,"\r[10/10] index.qmd\n"] [1,"\n"] [1,"Watching files for changes\nBrowse at http://localhost:22222/\n"] [1,"GET: /\n"] [1," /img/logo.png (404: Not Found)\n"] +[1,"GET: /cv/\n /img/logo.png (404: Not Found)\n"] +[1,"GET: /blog/\n"] +[1," /img/logo.png (404: Not Found)\n"] [1,"GET: /\n"] [1," /img/logo.png (404: Not Found)\n"] diff --git a/.Rproj.user/224B9E6C/pcs/source-pane.pper b/.Rproj.user/224B9E6C/pcs/source-pane.pper index 965e284..be19143 100644 --- a/.Rproj.user/224B9E6C/pcs/source-pane.pper +++ b/.Rproj.user/224B9E6C/pcs/source-pane.pper @@ -1,4 +1,4 @@ { - "activeTab": 1, + "activeTab": 0, "activeTabSourceWindow0": 0 } \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/pcs/windowlayoutstate.pper b/.Rproj.user/224B9E6C/pcs/windowlayoutstate.pper index 327158e..4d843a5 100644 --- a/.Rproj.user/224B9E6C/pcs/windowlayoutstate.pper +++ b/.Rproj.user/224B9E6C/pcs/windowlayoutstate.pper @@ -1,14 +1,14 @@ { "left": { - "splitterpos": 231, + "splitterpos": 344, "topwindowstate": "NORMAL", - "panelheight": 592, - "windowheight": 630 + "panelheight": 951, + "windowheight": 989 }, "right": { - "splitterpos": 329, - "topwindowstate": "MINIMIZE", - "panelheight": 592, - "windowheight": 630 + "splitterpos": 543, + "topwindowstate": "NORMAL", + "panelheight": 951, + "windowheight": 989 } } \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/pcs/workbench-pane.pper b/.Rproj.user/224B9E6C/pcs/workbench-pane.pper index d612aa7..ab5e950 100644 --- a/.Rproj.user/224B9E6C/pcs/workbench-pane.pper +++ b/.Rproj.user/224B9E6C/pcs/workbench-pane.pper @@ -1,5 +1,5 @@ { "TabSet1": 3, - "TabSet2": 4, + "TabSet2": 0, "TabZoom": {} } \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/rmd-outputs b/.Rproj.user/224B9E6C/rmd-outputs index 2fd2252..82490c6 100644 --- a/.Rproj.user/224B9E6C/rmd-outputs +++ b/.Rproj.user/224B9E6C/rmd-outputs @@ -1,7 +1,7 @@ C:/Data/Documents/Personal/CV-first-repo-master/CV_ccmb.pdf C:/Data/Documents/Personal/CV-first-repo-master/CV_ccmb.pdf - +C:/Data/Documents/Personal/CV-first-repo-master/CV_edit1.pdf diff --git a/.Rproj.user/224B9E6C/sources/prop/11366684 b/.Rproj.user/224B9E6C/sources/prop/11366684 index bb27690..8a86b43 100644 --- a/.Rproj.user/224B9E6C/sources/prop/11366684 +++ b/.Rproj.user/224B9E6C/sources/prop/11366684 @@ -1,4 +1,6 @@ { "source_window_id": "", - "Source": "Source" + "Source": "Source", + "cursorPosition": "5,9", + "scrollLine": "0" } \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/prop/36D7AB51 b/.Rproj.user/224B9E6C/sources/prop/36D7AB51 new file mode 100644 index 0000000..883d450 --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/prop/36D7AB51 @@ -0,0 +1,6 @@ +{ + "source_window_id": "", + "Source": "Source", + "cursorPosition": "15,6", + "scrollLine": "3" +} \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/prop/3F79B444 b/.Rproj.user/224B9E6C/sources/prop/3F79B444 index 72f0f40..f1565c1 100644 --- a/.Rproj.user/224B9E6C/sources/prop/3F79B444 +++ b/.Rproj.user/224B9E6C/sources/prop/3F79B444 @@ -1,11 +1,11 @@ { "source_window_id": "", "Source": "Source", - "cursorPosition": "25,0", - "scrollLine": "21", + "cursorPosition": "0,0", + "scrollLine": "15", "rmdVisualMode": "false", "rmdVisualWrapConfigured": "true", "docOutlineVisible": "1", "rmdVisualCollapsedChunks": "", - "rmdVisualModeLocation": "948:925.3333129882812" + "rmdVisualModeLocation": "2:587.3333129882812" } \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/prop/741BBE12 b/.Rproj.user/224B9E6C/sources/prop/741BBE12 new file mode 100644 index 0000000..bb27690 --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/prop/741BBE12 @@ -0,0 +1,4 @@ +{ + "source_window_id": "", + "Source": "Source" +} \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/prop/75320100 b/.Rproj.user/224B9E6C/sources/prop/75320100 new file mode 100644 index 0000000..0f1c1c1 --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/prop/75320100 @@ -0,0 +1,6 @@ +{ + "source_window_id": "", + "Source": "Source", + "cursorPosition": "3,80", + "scrollLine": "0" +} \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/prop/9ED00626 b/.Rproj.user/224B9E6C/sources/prop/9ED00626 new file mode 100644 index 0000000..c4706b1 --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/prop/9ED00626 @@ -0,0 +1,6 @@ +{ + "source_window_id": "", + "Source": "Source", + "cursorPosition": "134,8", + "scrollLine": "0" +} \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/prop/F9FA7054 b/.Rproj.user/224B9E6C/sources/prop/F9FA7054 index 13065b8..c230b58 100644 --- a/.Rproj.user/224B9E6C/sources/prop/F9FA7054 +++ b/.Rproj.user/224B9E6C/sources/prop/F9FA7054 @@ -1,11 +1,11 @@ { "source_window_id": "", "Source": "Source", - "cursorPosition": "18,0", - "scrollLine": "17", + "cursorPosition": "16,311", + "scrollLine": "0", "rmdVisualMode": "false", "rmdVisualWrapConfigured": "true", - "docOutlineVisible": "1", + "docOutlineVisible": "0", "rmdVisualCollapsedChunks": "", "rmdVisualModeLocation": "82:0" } \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/prop/INDEX b/.Rproj.user/224B9E6C/sources/prop/INDEX index a459088..7a0fd12 100644 --- a/.Rproj.user/224B9E6C/sources/prop/INDEX +++ b/.Rproj.user/224B9E6C/sources/prop/INDEX @@ -1,5 +1,6 @@ C%3A%2FData%2FAnalysis%2FPREDI%2FFragPipe%2FMP%2FBatch1-2_results%2F20220818_limmaBatchCorrection.R="B3528A83" C%3A%2FData%2FAnalysis%2FPREDI%2FFragPipe%2FMP%2FBatch1-2_results%2Fbatchqc_report.Rmd="E657EBE5" +C%3A%2FData%2FArticles%2FManuscript%2FAstral%2Fcv.R="9ED00626" C%3A%2FData%2FArticles%2FManuscript%2FEDIA%2F20231117_Heatmap%2FHeatmap.R="6281908E" C%3A%2FData%2FArticles%2FManuscript%2FEDIA%2FNewHeatmap%2FENRC_other.csv="C0E71FA9" C%3A%2FData%2FArticles%2FManuscript%2FEDIA%2FNewHeatmap%2FMinigut_R_script.R="64549641" @@ -11,6 +12,7 @@ C%3A%2FData%2FArticles%2FManuscript%2FNOSecret%2FNewHeatmap%2FMinigut_R_script_a C%3A%2FData%2FDocuments%2FPersonal%2FCV-first-repo-master%2FCV.Rmd="703AEF0D" C%3A%2FData%2FDocuments%2FPersonal%2FCV-first-repo-master%2FCV1.Rmd="2ED4760C" C%3A%2FData%2FDocuments%2FPersonal%2FCV-first-repo-master%2FCV_ccmb.Rmd="959914B0" +C%3A%2FData%2FDocuments%2FPersonal%2FCV-first-repo-master%2FCV_edit1.Rmd="741BBE12" C%3A%2FData%2FDocuments%2FPersonal%2FCV-first-repo-master%2FCV_with_Symb.Rmd="F9AC1C83" C%3A%2FData%2FDocuments%2FPersonal%2FCV-first-repo-master%2Fr%2Fdata_withPatent.r="13B7D63B" C%3A%2FData%2FDocuments%2FPersonal%2FRaj.Rmd="705F1BC2" @@ -52,6 +54,8 @@ C%3A%2FUsers%2FBhosaleS%2FDownloads%2Ftest.qmd="78FF969D" ~%2FGitHub%2Fsantoshdbhosale.github.io%2Fblog%2FMass-Spectrometry-Based-Serum-Proteomics%2Findex.qmd="02BE7E08" ~%2FGitHub%2Fsantoshdbhosale.github.io%2Fblog%2FProteomics-data-analysis-%26-visualization%2Findex.qmd="C449BE20" ~%2FGitHub%2Fsantoshdbhosale.github.io%2Fblog%2FSerum-Proteomics-Atherosclerosis%2Findex.qmd="32BB9D5A" +~%2FGitHub%2Fsantoshdbhosale.github.io%2Fblog%2FSerum-Proteomics-Pre-diabetic%2Ffooter.Rhtml="75320100" +~%2FGitHub%2Fsantoshdbhosale.github.io%2Fblog%2FSerum-Proteomics-Pre-diabetic%2Ffooter.html="36D7AB51" ~%2FGitHub%2Fsantoshdbhosale.github.io%2Fblog%2FSerum-Proteomics-Pre-diabetic%2Findex.qmd="11366684" ~%2FGitHub%2Fsantoshdbhosale.github.io%2Fblog%2Findex.qmd="3F79B444" ~%2FGitHub%2Fsantoshdbhosale.github.io%2Fcv%2Findex.qmd="179D375C" diff --git a/.Rproj.user/224B9E6C/sources/session-676a636/049A85A4 b/.Rproj.user/224B9E6C/sources/session-676a636/049A85A4 new file mode 100644 index 0000000..01cab18 --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-676a636/049A85A4 @@ -0,0 +1,31 @@ +{ + "id": "049A85A4", + "path": "~/GitHub/santoshdbhosale.github.io/index.qmd", + "project_path": "index.qmd", + "type": "quarto_markdown", + "hash": "0", + "contents": "", + "dirty": false, + "created": 1712343345139.0, + "source_on_save": false, + "relative_order": 2, + "properties": { + "source_window_id": "", + "Source": "Source", + "cursorPosition": "16,311", + "scrollLine": "0", + "rmdVisualMode": "false", + "rmdVisualWrapConfigured": "true", + "docOutlineVisible": "0", + "rmdVisualCollapsedChunks": "", + "rmdVisualModeLocation": "82:0" + }, + "folds": "", + "lastKnownWriteTime": 1712343898, + "encoding": "UTF-8", + "collab_server": "", + "source_window": "", + "last_content_update": 1712343898749, + "read_only": false, + "read_only_alternatives": [] +} \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/session-676a636/049A85A4-contents b/.Rproj.user/224B9E6C/sources/session-676a636/049A85A4-contents new file mode 100644 index 0000000..29d1106 --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-676a636/049A85A4-contents @@ -0,0 +1,22 @@ +--- +about: + template: jolla + id: about-block + image: "img/SDB1.jpg" +--- + +::: {#about-block} +::: + +# Understanding the Complexity of Proteome! + +Hi, I'm Santosh D. Bhosale, pharmacist by training and currently working as an associate biomedical scientist at **[Cedars-Sinai Precision biomarker laboratories](https://www.cs-pbl.com/)**, Los Angeles, CA, USA. + +Using the versatility of mass spectrometry-based proteomics, I am exploring the properties of biologically important proteins. + +Precisely, I work on mass spectrometry-based proteomics technologies to address the questions related to biomedical research. These includes not only the qualitative and quantitative measurement of proteins, but also their post-translational modifications and interactions with other proteins. I use variety of computational methods to understand the complexity of proteins. + +{} + + + diff --git a/.Rproj.user/224B9E6C/sources/session-b2b5d935/lock_file b/.Rproj.user/224B9E6C/sources/session-676a636/lock_file similarity index 100% rename from .Rproj.user/224B9E6C/sources/session-b2b5d935/lock_file rename to .Rproj.user/224B9E6C/sources/session-676a636/lock_file diff --git a/.Rproj.user/224B9E6C/sources/session-b2b5d935/249EDE74-contents b/.Rproj.user/224B9E6C/sources/session-b2b5d935/249EDE74-contents deleted file mode 100644 index cf5ed42..0000000 --- a/.Rproj.user/224B9E6C/sources/session-b2b5d935/249EDE74-contents +++ /dev/null @@ -1,8 +0,0 @@ ---- -title: "Untitled" -editor: visual ---- - -quarto install extension quarto-ext/fontawesome - -quarto install extension schochastics/academicons diff --git a/.Rproj.user/224B9E6C/sources/session-b2b5d935/B823F555-contents b/.Rproj.user/224B9E6C/sources/session-b2b5d935/B823F555-contents deleted file mode 100644 index 3136c44..0000000 --- a/.Rproj.user/224B9E6C/sources/session-b2b5d935/B823F555-contents +++ /dev/null @@ -1,4 +0,0 @@ -install.packages('rmarkdown') -library(rmarkdown) -packageVersion('rmarkdown') - diff --git a/.Rproj.user/224B9E6C/sources/session-b2b5d935/E4BC5B75 b/.Rproj.user/224B9E6C/sources/session-b2b5d935/E4BC5B75 deleted file mode 100644 index 9a30367..0000000 --- a/.Rproj.user/224B9E6C/sources/session-b2b5d935/E4BC5B75 +++ /dev/null @@ -1,27 +0,0 @@ -{ - "id": "E4BC5B75", - "path": null, - "project_path": null, - "type": "r_source", - "hash": "0", - "contents": "", - "dirty": true, - "created": 1709324989004.0, - "source_on_save": false, - "relative_order": 1, - "properties": { - "tempName": "Untitled1", - "source_window_id": "", - "Source": "Source", - "cursorPosition": "2,66", - "scrollLine": "0" - }, - "folds": "", - "lastKnownWriteTime": 2322280117112693601, - "encoding": "", - "collab_server": "", - "source_window": "", - "last_content_update": 1709325009666, - "read_only": false, - "read_only_alternatives": [] -} \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/session-b2b5d935/E4BC5B75-contents b/.Rproj.user/224B9E6C/sources/session-b2b5d935/E4BC5B75-contents deleted file mode 100644 index 69c3c29..0000000 --- a/.Rproj.user/224B9E6C/sources/session-b2b5d935/E4BC5B75-contents +++ /dev/null @@ -1,3 +0,0 @@ -- icon: files -text: Google-scholar -href: https://scholar.google.com/citations?user=sIxWbx0AAAAJ&hl=en \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/session-b2b5d935/FF8D8B80 b/.Rproj.user/224B9E6C/sources/session-b2b5d935/FF8D8B80 deleted file mode 100644 index 3ae9c32..0000000 --- a/.Rproj.user/224B9E6C/sources/session-b2b5d935/FF8D8B80 +++ /dev/null @@ -1,26 +0,0 @@ -{ - "id": "FF8D8B80", - "path": "~/GitHub/santoshdbhosale.github.io/_quarto.yml", - "project_path": "_quarto.yml", - "type": "yaml", - "hash": "353348354", - "contents": "", - "dirty": false, - "created": 1709324996242.0, - "source_on_save": false, - "relative_order": 2, - "properties": { - "source_window_id": "", - "Source": "Source", - "cursorPosition": "30,64", - "scrollLine": "27" - }, - "folds": "", - "lastKnownWriteTime": 1709335671, - "encoding": "UTF-8", - "collab_server": "", - "source_window": "", - "last_content_update": 1709335671574, - "read_only": false, - "read_only_alternatives": [] -} \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/session-b2b5d935/FF8D8B80-contents b/.Rproj.user/224B9E6C/sources/session-b2b5d935/FF8D8B80-contents deleted file mode 100644 index ad0d19b..0000000 --- a/.Rproj.user/224B9E6C/sources/session-b2b5d935/FF8D8B80-contents +++ /dev/null @@ -1,68 +0,0 @@ -# This file controls the settings for your Quarto template from www.marvinschmitt.com - -# website settings -website: - title: "Santosh D. Bhosale" # Your name - description: "I am pharmacist by training and do mass spectrometry based proteomics" # A brief slogan (optional) - image: img/sdb.png - - - - # start of the navigation bar at the top of the website - navbar: - pinned: true - logo: /img/logo.png - - # your sub-pages - left: - - text: "About" - href: about/index.qmd - - text: "CV" - href: cv/index.qmd - - text: "Blog" - href: blog/index.qmd - - # your social media handles - right: - - icon: linkedin - href: https://www.linkedin.com/in/santoshdbhosale/ - - icon: github - href: https://github.com/santoshdbhosale - - text: "{{< iconify academicons google-scholar size=1.5em >}}" - href: https://scholar.google.com/citations?user=sIxWbx0AAAAJ&hl=en - - icon: envelope - aria-label: email - href: "mailto:santoshdbhosale@gmail.com" - - - -# Don't touch unless you know what you are doing :) ------------ - search: - location: navbar - type: textbox - -project: - type: website - output-dir: docs - - preview: - port: 22222 - 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currently employed as an associate biomedical scientist at Precision biomarker laboratories, Cedars-Sinai Medical Center, Los Angeles, CA, USA.\n\nDuring my postdoctoral fellowship, I worked in Prof. Martin R. Larsen's lab at the University of Southern Denmark on developing a pipeline for identifying post-translationally modified biomarkers in clinical samples.\n\nI pursued my PhD from University of Turku under the joint advisorship of Prof. Riitta Lahesmaa and Dr. Robert Moulder. During the course of studies, I worked on identifying and validating the serum protein biomarkers for type 1 diabetes and carotid atherosclerosis.\n\nBefore enrollment into the PhD study, I gained my first level of research experience with my master thesis in the area of proteomics and mass spectrometry at National Chemical Laboratory (NCL) under the joint supervision of Drs. Mahesh J. Kulkarni and B.Santhakumari, after which I continued as a teacher in college of pharmacy and then as a research assistant again at NCL.\n\n","srcMarkdownNoYaml":"\n\n::: {#about-block}\n:::\n\nI am currently employed as an associate biomedical scientist at Precision biomarker laboratories, Cedars-Sinai Medical Center, Los Angeles, CA, USA.\n\nDuring my postdoctoral fellowship, I worked in Prof. Martin R. Larsen's lab at the University of Southern Denmark on developing a pipeline for identifying post-translationally modified biomarkers in clinical samples.\n\nI pursued my PhD from University of Turku under the joint advisorship of Prof. Riitta Lahesmaa and Dr. Robert Moulder. During the course of studies, I worked on identifying and validating the serum protein biomarkers for type 1 diabetes and carotid atherosclerosis.\n\nBefore enrollment into the PhD study, I gained my first level of research experience with my master thesis in the area of proteomics and mass spectrometry at National Chemical Laboratory (NCL) under the joint supervision of Drs. Mahesh J. Kulkarni and B.Santhakumari, after which I continued as a teacher in college of pharmacy and then as a research assistant again at NCL.\n\n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"engine":"markdown"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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top","search-no-results-text":"No results","search-matching-documents-text":"matching documents","search-copy-link-title":"Copy link to search","search-hide-matches-text":"Hide additional matches","search-more-match-text":"more match in this document","search-more-matches-text":"more matches in this document","search-clear-button-title":"Clear","search-text-placeholder":"","search-detached-cancel-button-title":"Cancel","search-submit-button-title":"Submit","search-label":"Search","toggle-section":"Toggle section","toggle-sidebar":"Toggle sidebar navigation","toggle-dark-mode":"Toggle dark mode","toggle-reader-mode":"Toggle reader mode","toggle-navigation":"Toggle 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a/.quarto/idx/blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd.json b/.quarto/idx/blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd.json index 4f6ffc6..0a8d171 100644 --- a/.quarto/idx/blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd.json +++ b/.quarto/idx/blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd.json @@ -1 +1 @@ -{"title":"AP-MS to Study FOSL Related Proteins Interactome","markdown":{"yaml":{"title":"AP-MS to Study FOSL Related Proteins Interactome","date":"2021-09-16","categories":["publications","T cells"],"image":"fig/FOSL-interactors.png"},"containsRefs":false,"markdown":"\n\nProteins represent the key interacting biomolecules in the complex network within the cell and their interactions are crucial in orchestrating all aspects of life at the molecular level. \nMost biochemical functions are not carried out by a specific protein in isolation but by the multiple protein in associations refereed as a protein-protein interactions (PPIs).\n\nAffinity purification-based mass spectrometry (AP-MS) is a technique of choice in discovering PPIs. These experiments are usually carried out by coupling a bait protein to the protein A or immunoglobulin G (IgG) surfaces or an affinity matrix followed by purification of tagged protein from a cell lysate. \nAdditionally, suitable negative control replicates are mandatorily included to define the non-specific background. The composition of PPIs are then delineated by mass spectrometry analysis. \nThese types of studies are useful in understanding the complicated interplay of proteins inside the cells for generating new hypothesis or may be helpful in placing a specific interactor in a pathway to explain observed phenotypes.\n\n**[We used AP-MS method to study interactome of FOS related proteins (FOSL1 and FOSL2) in human Th17 cells](https://pubs.acs.org/doi/10.1021/acsomega.1c03681).** The fate of Th17 cells is regulated by various transcription factors such as BATF, IRF4 and STAT3. Furthermore, the members of the activator protein (AP-1) family including ATF, FOS and JUN also modulate the differentiation of Th17 cells. \nOf these AP-1 members, FOS related proteins regulates variety of processes such as cancer progression, embryonic development and immune cells signaling. In order to understand how FOS related proteins mediates signaling mechanism in Th17 cells, we performed their interactome analysis.\n\nThe analysis resulted in the identification of 163 and 67 proteins for FOSL1 and FOSL2 respectively. These interactors have passed certain criteria including **[mass spectrometry interaction statistics (MiST) algorithm scores](https://modbase.compbio.ucsf.edu/mist/)** with the matching IgG controls and they were mapped against in house common contaminant detected in related AP-MS experiments. \n\nFurthermore, we validated the interesting binding partners of FOSL1 and FOSL2 by western blotting and parallel reaction monitoring mass spectrometry. The shared interactors between FOSL1 and FOSL2 as depicted in below figure were mapped against the **[STRING database](https://string-db.org/)** to construct a network using **[Cytoscape](https://cytoscape.org/).** \n\n{}\n\nThe gene ontology based molecular functional analysis were performed by **[ClueGO](https://apps.cytoscape.org/apps/cluego)** and **[CluePedia](https://apps.cytoscape.org/apps/cluepedia)** apps built in Cytoscape. \n","srcMarkdownNoYaml":"\n\nProteins represent the key interacting biomolecules in the complex network within the cell and their interactions are crucial in orchestrating all aspects of life at the molecular level. \nMost biochemical functions are not carried out by a specific protein in isolation but by the multiple protein in associations refereed as a protein-protein interactions (PPIs).\n\nAffinity purification-based mass spectrometry (AP-MS) is a technique of choice in discovering PPIs. These experiments are usually carried out by coupling a bait protein to the protein A or immunoglobulin G (IgG) surfaces or an affinity matrix followed by purification of tagged protein from a cell lysate. \nAdditionally, suitable negative control replicates are mandatorily included to define the non-specific background. The composition of PPIs are then delineated by mass spectrometry analysis. \nThese types of studies are useful in understanding the complicated interplay of proteins inside the cells for generating new hypothesis or may be helpful in placing a specific interactor in a pathway to explain observed phenotypes.\n\n**[We used AP-MS method to study interactome of FOS related proteins (FOSL1 and FOSL2) in human Th17 cells](https://pubs.acs.org/doi/10.1021/acsomega.1c03681).** The fate of Th17 cells is regulated by various transcription factors such as BATF, IRF4 and STAT3. Furthermore, the members of the activator protein (AP-1) family including ATF, FOS and JUN also modulate the differentiation of Th17 cells. \nOf these AP-1 members, FOS related proteins regulates variety of processes such as cancer progression, embryonic development and immune cells signaling. In order to understand how FOS related proteins mediates signaling mechanism in Th17 cells, we performed their interactome analysis.\n\nThe analysis resulted in the identification of 163 and 67 proteins for FOSL1 and FOSL2 respectively. These interactors have passed certain criteria including **[mass spectrometry interaction statistics (MiST) algorithm scores](https://modbase.compbio.ucsf.edu/mist/)** with the matching IgG controls and they were mapped against in house common contaminant detected in related AP-MS experiments. \n\nFurthermore, we validated the interesting binding partners of FOSL1 and FOSL2 by western blotting and parallel reaction monitoring mass spectrometry. The shared interactors between FOSL1 and FOSL2 as depicted in below figure were mapped against the **[STRING database](https://string-db.org/)** to construct a network using **[Cytoscape](https://cytoscape.org/).** \n\n{}\n\nThe gene ontology based molecular functional analysis were performed by **[ClueGO](https://apps.cytoscape.org/apps/cluego)** and **[CluePedia](https://apps.cytoscape.org/apps/cluepedia)** apps built in Cytoscape. \n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"engine":"markdown"},"render":{"keep-tex":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true,"format-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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Related Proteins Interactome","markdown":{"yaml":{"title":"AP-MS to Study FOSL Related Proteins Interactome","date":"2021-09-16","categories":["publications","T cells"],"image":"fig/FOSL-interactors.png"},"containsRefs":false,"markdown":"\n\nProteins represent the key interacting biomolecules in the complex network within the cell and their interactions are crucial in orchestrating all aspects of life at the molecular level. \nMost biochemical functions are not carried out by a specific protein in isolation but by the multiple protein in associations refereed as a protein-protein interactions (PPIs).\n\nAffinity purification-based mass spectrometry (AP-MS) is a technique of choice in discovering PPIs. These experiments are usually carried out by coupling a bait protein to the protein A or immunoglobulin G (IgG) surfaces or an affinity matrix followed by purification of tagged protein from a cell lysate. \nAdditionally, suitable negative control replicates are mandatorily included to define the non-specific background. The composition of PPIs are then delineated by mass spectrometry analysis. \nThese types of studies are useful in understanding the complicated interplay of proteins inside the cells for generating new hypothesis or may be helpful in placing a specific interactor in a pathway to explain observed phenotypes.\n\n**[We used AP-MS method to study interactome of FOS related proteins (FOSL1 and FOSL2) in human Th17 cells](https://pubs.acs.org/doi/10.1021/acsomega.1c03681).** The fate of Th17 cells is regulated by various transcription factors such as BATF, IRF4 and STAT3. Furthermore, the members of the activator protein (AP-1) family including ATF, FOS and JUN also modulate the differentiation of Th17 cells. \nOf these AP-1 members, FOS related proteins regulates variety of processes such as cancer progression, embryonic development and immune cells signaling. In order to understand how FOS related proteins mediates signaling mechanism in Th17 cells, we performed their interactome analysis.\n\nThe analysis resulted in the identification of 163 and 67 proteins for FOSL1 and FOSL2 respectively. These interactors have passed certain criteria including **[mass spectrometry interaction statistics (MiST) algorithm scores](https://modbase.compbio.ucsf.edu/mist/)** with the matching IgG controls and they were mapped against in house common contaminant detected in related AP-MS experiments. \n\nFurthermore, we validated the interesting binding partners of FOSL1 and FOSL2 by western blotting and parallel reaction monitoring mass spectrometry. The shared interactors between FOSL1 and FOSL2 as depicted in below figure were mapped against the **[STRING database](https://string-db.org/)** to construct a network using **[Cytoscape](https://cytoscape.org/).** \n\n{}\n\nThe gene ontology based molecular functional analysis were performed by **[ClueGO](https://apps.cytoscape.org/apps/cluego)** and **[CluePedia](https://apps.cytoscape.org/apps/cluepedia)** apps built in Cytoscape. \n","srcMarkdownNoYaml":"\n\nProteins represent the key interacting biomolecules in the complex network within the cell and their interactions are crucial in orchestrating all aspects of life at the molecular level. \nMost biochemical functions are not carried out by a specific protein in isolation but by the multiple protein in associations refereed as a protein-protein interactions (PPIs).\n\nAffinity purification-based mass spectrometry (AP-MS) is a technique of choice in discovering PPIs. These experiments are usually carried out by coupling a bait protein to the protein A or immunoglobulin G (IgG) surfaces or an affinity matrix followed by purification of tagged protein from a cell lysate. \nAdditionally, suitable negative control replicates are mandatorily included to define the non-specific background. The composition of PPIs are then delineated by mass spectrometry analysis. \nThese types of studies are useful in understanding the complicated interplay of proteins inside the cells for generating new hypothesis or may be helpful in placing a specific interactor in a pathway to explain observed phenotypes.\n\n**[We used AP-MS method to study interactome of FOS related proteins (FOSL1 and FOSL2) in human Th17 cells](https://pubs.acs.org/doi/10.1021/acsomega.1c03681).** The fate of Th17 cells is regulated by various transcription factors such as BATF, IRF4 and STAT3. Furthermore, the members of the activator protein (AP-1) family including ATF, FOS and JUN also modulate the differentiation of Th17 cells. \nOf these AP-1 members, FOS related proteins regulates variety of processes such as cancer progression, embryonic development and immune cells signaling. In order to understand how FOS related proteins mediates signaling mechanism in Th17 cells, we performed their interactome analysis.\n\nThe analysis resulted in the identification of 163 and 67 proteins for FOSL1 and FOSL2 respectively. These interactors have passed certain criteria including **[mass spectrometry interaction statistics (MiST) algorithm scores](https://modbase.compbio.ucsf.edu/mist/)** with the matching IgG controls and they were mapped against in house common contaminant detected in related AP-MS experiments. \n\nFurthermore, we validated the interesting binding partners of FOSL1 and FOSL2 by western blotting and parallel reaction monitoring mass spectrometry. The shared interactors between FOSL1 and FOSL2 as depicted in below figure were mapped against the **[STRING database](https://string-db.org/)** to construct a network using **[Cytoscape](https://cytoscape.org/).** \n\n{}\n\nThe gene ontology based molecular functional analysis were performed by **[ClueGO](https://apps.cytoscape.org/apps/cluego)** and **[CluePedia](https://apps.cytoscape.org/apps/cluepedia)** apps built in Cytoscape. \n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"engine":"markdown"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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top","search-no-results-text":"No results","search-matching-documents-text":"matching documents","search-copy-link-title":"Copy link to search","search-hide-matches-text":"Hide additional matches","search-more-match-text":"more match in this document","search-more-matches-text":"more matches in this document","search-clear-button-title":"Clear","search-text-placeholder":"","search-detached-cancel-button-title":"Cancel","search-submit-button-title":"Submit","search-label":"Search","toggle-section":"Toggle section","toggle-sidebar":"Toggle sidebar navigation","toggle-dark-mode":"Toggle dark mode","toggle-reader-mode":"Toggle reader mode","toggle-navigation":"Toggle 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cells"],"image":"fig/FOSL-interactors.png"},"extensions":{"book":{"multiFile":true}}}},"projectFormats":["html"]} \ No newline at end of file diff --git a/.quarto/idx/blog/Mass-Spectrometry-Based-Serum-Proteomics/index.qmd.json b/.quarto/idx/blog/Mass-Spectrometry-Based-Serum-Proteomics/index.qmd.json index 0793f4a..7fd1d9a 100644 --- a/.quarto/idx/blog/Mass-Spectrometry-Based-Serum-Proteomics/index.qmd.json +++ b/.quarto/idx/blog/Mass-Spectrometry-Based-Serum-Proteomics/index.qmd.json @@ -1 +1 @@ -{"title":"Mass Spectrometry Based Serum Proteomics","markdown":{"yaml":{"title":"Mass Spectrometry Based Serum Proteomics","date":"2020-11-18","categories":["publications","book chapter"]},"containsRefs":false,"markdown":"\n\nI really like Methods in Molecular Biology book series. They publish step by step protocols with detailed information on materials and methods to carry out the experiment in a reproducible manner. In particular notes section provides useful tip and troubleshooting guide.\n\nWe published a protocol entitled **[Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation](https://link.springer.com/protocol/10.1007/978-1-4939-7057-5_31).** \nIt presents a workflow detailing sample preparation with and without immunodepletion of high abundant serum/plasma proteins and, LC-MS/MS methodology for discovery and targeted measurements of candidate biomarkers. ","srcMarkdownNoYaml":"\n\nI really like Methods in Molecular Biology book series. They publish step by step protocols with detailed information on materials and methods to carry out the experiment in a reproducible manner. In particular notes section provides useful tip and troubleshooting guide.\n\nWe published a protocol entitled **[Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation](https://link.springer.com/protocol/10.1007/978-1-4939-7057-5_31).** \nIt presents a workflow detailing sample preparation with and without immunodepletion of high abundant serum/plasma proteins and, LC-MS/MS methodology for discovery and targeted measurements of candidate biomarkers. "},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"engine":"markdown"},"render":{"keep-tex":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true,"format-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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Proteomics","markdown":{"yaml":{"title":"Mass Spectrometry Based Serum Proteomics","date":"2020-11-18","categories":["publications","book chapter"]},"containsRefs":false,"markdown":"\n\nI really like Methods in Molecular Biology book series. They publish step by step protocols with detailed information on materials and methods to carry out the experiment in a reproducible manner. In particular notes section provides useful tip and troubleshooting guide.\n\nWe published a protocol entitled **[Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation](https://link.springer.com/protocol/10.1007/978-1-4939-7057-5_31).** \nIt presents a workflow detailing sample preparation with and without immunodepletion of high abundant serum/plasma proteins and, LC-MS/MS methodology for discovery and targeted measurements of candidate biomarkers. ","srcMarkdownNoYaml":"\n\nI really like Methods in Molecular Biology book series. They publish step by step protocols with detailed information on materials and methods to carry out the experiment in a reproducible manner. In particular notes section provides useful tip and troubleshooting guide.\n\nWe published a protocol entitled **[Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation](https://link.springer.com/protocol/10.1007/978-1-4939-7057-5_31).** \nIt presents a workflow detailing sample preparation with and without immunodepletion of high abundant serum/plasma proteins and, LC-MS/MS methodology for discovery and targeted measurements of candidate biomarkers. "},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"engine":"markdown"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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file diff --git a/.quarto/idx/blog/Proteomics-data-analysis-&-visualization/index.qmd.json b/.quarto/idx/blog/Proteomics-data-analysis-&-visualization/index.qmd.json index ccb32ca..5c45d0a 100644 --- a/.quarto/idx/blog/Proteomics-data-analysis-&-visualization/index.qmd.json +++ b/.quarto/idx/blog/Proteomics-data-analysis-&-visualization/index.qmd.json @@ -1 +1 @@ -{"title":"Proteomics data analysis and _visualization_ (no programming skills..its okay..but)","markdown":{"yaml":{"title":"Proteomics data analysis and _visualization_ (no programming skills..its okay..but)","date":"2023-10-10","categories":["data analysis"]},"containsRefs":false,"markdown":"\n\nMass spectrometry based proteomics is the coolest technique to identify and characterize the proteins (including their interaction, alternative splicing, post-transnational modifications and more). Introduction and details about the technology are beyond the scope of this blog post, however, readers are recommended to follow the [comprehensive overview of modern proteomics](https://jessegmeyerlab.github.io/proteomics-tutorial/).\n\nTypical shotgun proteomics experiment on representative number of samples results in generation of several gigabytes of mass spectrometry data files. The analysis of such data undergoes following steps.\n\n- Quality control checks.\n- Database search and quantitative analysis.\n- Statistical analysis\n- Functional annotation analysis\n\nIn this blog post, I will highlight the tools used to process the mass spectrometry data in the\n\n1. **Quality control checks:** Depending on the mode of LC-MS/MS data acquisition (i.e. either DDA or DIA), there exist plethora of tools to measure QC metrics. However, for the DIA analysis, limited pipelines are available.\n\n Often times to use the functionality of some tools, users needs to convert the proprietary MS files into generic file format such as mzmL\n\n *DDA analysis*\n\n - [RawMeat](https://proteomicsresource.washington.edu/protocols06/): developed by Vast Scientific gives a quick overview of TIC (total ion chromatogram), charge state distribution, fill time, spray stability and target fill times. The tool is limited to use with Thermo instrument and it is no longer supported.\n - [RawBeans](https://bitbucket.org/incpm/prot-qc/downloads/): generates an interactive html report for\n - [QuiC ™](https://biognosys.com/software/quic/): Properitary software from Biognosys, supports most of data acquisition mode (SRM, PRM, DIA or DDA) but it requires addition of iRT peptides to the samples.\n","srcMarkdownNoYaml":"\n\nMass spectrometry based proteomics is the coolest technique to identify and characterize the proteins (including their interaction, alternative splicing, post-transnational modifications and more). Introduction and details about the technology are beyond the scope of this blog post, however, readers are recommended to follow the [comprehensive overview of modern proteomics](https://jessegmeyerlab.github.io/proteomics-tutorial/).\n\nTypical shotgun proteomics experiment on representative number of samples results in generation of several gigabytes of mass spectrometry data files. The analysis of such data undergoes following steps.\n\n- Quality control checks.\n- Database search and quantitative analysis.\n- Statistical analysis\n- Functional annotation analysis\n\nIn this blog post, I will highlight the tools used to process the mass spectrometry data in the\n\n1. **Quality control checks:** Depending on the mode of LC-MS/MS data acquisition (i.e. either DDA or DIA), there exist plethora of tools to measure QC metrics. However, for the DIA analysis, limited pipelines are available.\n\n Often times to use the functionality of some tools, users needs to convert the proprietary MS files into generic file format such as mzmL\n\n *DDA analysis*\n\n - [RawMeat](https://proteomicsresource.washington.edu/protocols06/): developed by Vast Scientific gives a quick overview of TIC (total ion chromatogram), charge state distribution, fill time, spray stability and target fill times. The tool is limited to use with Thermo instrument and it is no longer supported.\n - [RawBeans](https://bitbucket.org/incpm/prot-qc/downloads/): generates an interactive html report for\n - [QuiC ™](https://biognosys.com/software/quic/): Properitary software from Biognosys, supports most of data acquisition mode (SRM, PRM, DIA or DDA) but it requires addition of iRT peptides to the samples.\n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"engine":"markdown"},"render":{"keep-tex":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true,"format-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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_visualization_ (no programming skills..its okay..but)","markdown":{"yaml":{"title":"Proteomics data analysis and _visualization_ (no programming skills..its okay..but)","date":"2023-10-10","categories":["data analysis"]},"containsRefs":false,"markdown":"\n\nMass spectrometry based proteomics is the coolest technique to identify and characterize the proteins (including their interaction, alternative splicing, post-transnational modifications and more). Introduction and details about the technology are beyond the scope of this blog post, however, readers are recommended to follow the [comprehensive overview of modern proteomics](https://jessegmeyerlab.github.io/proteomics-tutorial/).\n\nTypical shotgun proteomics experiment on representative number of samples results in generation of several gigabytes of mass spectrometry data files. The analysis of such data undergoes following steps.\n\n- Quality control checks.\n- Database search and quantitative analysis.\n- Statistical analysis\n- Functional annotation analysis\n\nIn this blog post, I will highlight the tools used to process the mass spectrometry data in the\n\n1. **Quality control checks:** Depending on the mode of LC-MS/MS data acquisition (i.e. either DDA or DIA), there exist plethora of tools to measure QC metrics. However, for the DIA analysis, limited pipelines are available.\n\n Often times to use the functionality of some tools, users needs to convert the proprietary MS files into generic file format such as mzmL\n\n *DDA analysis*\n\n - [RawMeat](https://proteomicsresource.washington.edu/protocols06/): developed by Vast Scientific gives a quick overview of TIC (total ion chromatogram), charge state distribution, fill time, spray stability and target fill times. The tool is limited to use with Thermo instrument and it is no longer supported.\n - [RawBeans](https://bitbucket.org/incpm/prot-qc/downloads/): generates an interactive html report for\n - [QuiC ™](https://biognosys.com/software/quic/): Properitary software from Biognosys, supports most of data acquisition mode (SRM, PRM, DIA or DDA) but it requires addition of iRT peptides to the samples.\n","srcMarkdownNoYaml":"\n\nMass spectrometry based proteomics is the coolest technique to identify and characterize the proteins (including their interaction, alternative splicing, post-transnational modifications and more). Introduction and details about the technology are beyond the scope of this blog post, however, readers are recommended to follow the [comprehensive overview of modern proteomics](https://jessegmeyerlab.github.io/proteomics-tutorial/).\n\nTypical shotgun proteomics experiment on representative number of samples results in generation of several gigabytes of mass spectrometry data files. The analysis of such data undergoes following steps.\n\n- Quality control checks.\n- Database search and quantitative analysis.\n- Statistical analysis\n- Functional annotation analysis\n\nIn this blog post, I will highlight the tools used to process the mass spectrometry data in the\n\n1. **Quality control checks:** Depending on the mode of LC-MS/MS data acquisition (i.e. either DDA or DIA), there exist plethora of tools to measure QC metrics. However, for the DIA analysis, limited pipelines are available.\n\n Often times to use the functionality of some tools, users needs to convert the proprietary MS files into generic file format such as mzmL\n\n *DDA analysis*\n\n - [RawMeat](https://proteomicsresource.washington.edu/protocols06/): developed by Vast Scientific gives a quick overview of TIC (total ion chromatogram), charge state distribution, fill time, spray stability and target fill times. The tool is limited to use with Thermo instrument and it is no longer supported.\n - [RawBeans](https://bitbucket.org/incpm/prot-qc/downloads/): generates an interactive html report for\n - [QuiC ™](https://biognosys.com/software/quic/): Properitary software from Biognosys, supports most of data acquisition mode (SRM, PRM, DIA or DDA) but it requires addition of iRT peptides to the samples.\n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"engine":"markdown"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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citation:","title-block-author-single":"Author","title-block-author-plural":"Authors","title-block-affiliation-single":"Affiliation","title-block-affiliation-plural":"Affiliations","title-block-published":"Published","title-block-modified":"Modified","title-block-keywords":"Keywords","callout-tip-title":"Tip","callout-note-title":"Note","callout-warning-title":"Warning","callout-important-title":"Important","callout-caution-title":"Caution","code-summary":"Code","code-tools-menu-caption":"Code","code-tools-show-all-code":"Show All Code","code-tools-hide-all-code":"Hide All Code","code-tools-view-source":"View Source","code-tools-source-code":"Source Code","tools-share":"Share","tools-download":"Download","code-line":"Line","code-lines":"Lines","copy-button-tooltip":"Copy to Clipboard","copy-button-tooltip-success":"Copied!","repo-action-links-edit":"Edit this page","repo-action-links-source":"View source","repo-action-links-issue":"Report an issue","back-to-top":"Back to 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High","listing-page-field-date":"Date","listing-page-field-title":"Title","listing-page-field-description":"Description","listing-page-field-author":"Author","listing-page-field-filename":"File Name","listing-page-field-filemodified":"Modified","listing-page-field-subtitle":"Subtitle","listing-page-field-readingtime":"Reading Time","listing-page-field-wordcount":"Word Count","listing-page-field-categories":"Categories","listing-page-minutes-compact":"{0} min","listing-page-category-all":"All","listing-page-no-matches":"No matching items","listing-page-words":"{0} words"},"metadata":{"lang":"en","fig-responsive":true,"quarto-version":"1.4.553","fontsize":"1.1em","theme":["pulse","../../html/styles.scss"],"anchor-sections":true,"fig-cap-location":"margin","footnotes-hover":true,"title":"Proteomics data analysis and _visualization_ (no programming skills..its okay..but)","date":"2023-10-10","categories":["data analysis"]},"extensions":{"book":{"multiFile":true}}}},"projectFormats":["html"]} \ No newline at end of file diff --git a/.quarto/idx/blog/Serum-Proteomics-Atherosclerosis/index.qmd.json b/.quarto/idx/blog/Serum-Proteomics-Atherosclerosis/index.qmd.json index c2f5933..63b460b 100644 --- a/.quarto/idx/blog/Serum-Proteomics-Atherosclerosis/index.qmd.json +++ b/.quarto/idx/blog/Serum-Proteomics-Atherosclerosis/index.qmd.json @@ -1 +1 @@ -{"title":"Serum Proteomics Atherosclerosis","markdown":{"yaml":{"title":"Serum Proteomics Atherosclerosis","date":"2020-11-11","categories":["publications","serum"],"image":"fig/roc-plot.png"},"containsRefs":false,"markdown":"\nAtherosclerotic cardiovascular diseases are the major causes of mortality and morbidity in developed world. Thickening of the carotid arterial wall (plaque formation) are indicative of active atherosclerotic process. \nMyocardial infarction and stroke are the clinical events associated with an acute rupture of a critically located atherosclerotic plaque. Currently, ultrasonic assessment of carotid artery intima media thickness is used as a pre-clinical marker of the disease process. \nNevertheless, atherosclerotic process may remain asymptomatic for several decades and whether thickening of intima-media layer of carotid artery and carotid plaque represents two different phenotypes or single traits of disease process is still unclear. \n\nTo gain insights into the pathophysiology of pre-clinical atherosclerosis and identify novel biomarkers, we conducted the **[serum proteomics measurements](https://www.nature.com/articles/s41598-018-27265-9#Sec1) on the unique sample set of participants recruited in [The Cardiovascular Risk in Young Finns Study](https://youngfinnsstudy.utu.fi/studydesign.html).** \nWe performed label free quantitative MS analysis of serum samples obtained from the subjects in whom early signs of plaques were discerned together with matched controls. The serum samples were immunodepleted to remove high abundant proteins prior to LC-MS/MS analysis.\n\nThe profiling results indicated differential abundances in a set of proteins. \nFurthermore, selected reaction monitoring mass spectrometry analysis were performed on undepleted serum samples to verify the observed differences. \nFinally, machine learning analysis identified a panel of three proteins **P23142-4 (Fibulin 1 proteoform C), P02649 (Apolipoprotein E) and P55290 (Cadherin-13)** which segregated the cases from controls with best discrimination (an area under receiver-operating characteristic curve (AUROC) value of 0.79.). \n\n{}\nMore details about the data analysis can be found **[here](https://github.com/santoshdbhosale/Carotid_Atherosclerosis_LFQ).**\n","srcMarkdownNoYaml":"\nAtherosclerotic cardiovascular diseases are the major causes of mortality and morbidity in developed world. Thickening of the carotid arterial wall (plaque formation) are indicative of active atherosclerotic process. \nMyocardial infarction and stroke are the clinical events associated with an acute rupture of a critically located atherosclerotic plaque. Currently, ultrasonic assessment of carotid artery intima media thickness is used as a pre-clinical marker of the disease process. \nNevertheless, atherosclerotic process may remain asymptomatic for several decades and whether thickening of intima-media layer of carotid artery and carotid plaque represents two different phenotypes or single traits of disease process is still unclear. \n\nTo gain insights into the pathophysiology of pre-clinical atherosclerosis and identify novel biomarkers, we conducted the **[serum proteomics measurements](https://www.nature.com/articles/s41598-018-27265-9#Sec1) on the unique sample set of participants recruited in [The Cardiovascular Risk in Young Finns Study](https://youngfinnsstudy.utu.fi/studydesign.html).** \nWe performed label free quantitative MS analysis of serum samples obtained from the subjects in whom early signs of plaques were discerned together with matched controls. The serum samples were immunodepleted to remove high abundant proteins prior to LC-MS/MS analysis.\n\nThe profiling results indicated differential abundances in a set of proteins. \nFurthermore, selected reaction monitoring mass spectrometry analysis were performed on undepleted serum samples to verify the observed differences. \nFinally, machine learning analysis identified a panel of three proteins **P23142-4 (Fibulin 1 proteoform C), P02649 (Apolipoprotein E) and P55290 (Cadherin-13)** which segregated the cases from controls with best discrimination (an area under receiver-operating characteristic curve (AUROC) value of 0.79.). \n\n{}\nMore details about the data analysis can be found **[here](https://github.com/santoshdbhosale/Carotid_Atherosclerosis_LFQ).**\n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"engine":"markdown"},"render":{"keep-tex":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true,"format-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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documents","search-copy-link-title":"Copy link to search","search-hide-matches-text":"Hide additional matches","search-more-match-text":"more match in this document","search-more-matches-text":"more matches in this document","search-clear-button-title":"Clear","search-detached-cancel-button-title":"Cancel","search-submit-button-title":"Submit","search-label":"Search","toggle-section":"Toggle section","toggle-sidebar":"Toggle sidebar navigation","toggle-dark-mode":"Toggle dark mode","toggle-reader-mode":"Toggle reader mode","toggle-navigation":"Toggle navigation","crossref-fig-title":"Figure","crossref-tbl-title":"Table","crossref-lst-title":"Listing","crossref-thm-title":"Theorem","crossref-lem-title":"Lemma","crossref-cor-title":"Corollary","crossref-prp-title":"Proposition","crossref-cnj-title":"Conjecture","crossref-def-title":"Definition","crossref-exm-title":"Example","crossref-exr-title":"Exercise","crossref-ch-prefix":"Chapter","crossref-apx-prefix":"Appendix","crossref-sec-prefix":"Section","crossref-eq-prefix":"Equation","crossref-lof-title":"List of Figures","crossref-lot-title":"List of Tables","crossref-lol-title":"List of Listings","environment-proof-title":"Proof","environment-remark-title":"Remark","environment-solution-title":"Solution","listing-page-order-by":"Order By","listing-page-order-by-default":"Default","listing-page-order-by-date-asc":"Oldest","listing-page-order-by-date-desc":"Newest","listing-page-order-by-number-desc":"High to Low","listing-page-order-by-number-asc":"Low to High","listing-page-field-date":"Date","listing-page-field-title":"Title","listing-page-field-description":"Description","listing-page-field-author":"Author","listing-page-field-filename":"File Name","listing-page-field-filemodified":"Modified","listing-page-field-subtitle":"Subtitle","listing-page-field-readingtime":"Reading Time","listing-page-field-categories":"Categories","listing-page-minutes-compact":"{0} min","listing-page-category-all":"All","listing-page-no-matches":"No matching items"},"metadata":{"lang":"en","fig-responsive":true,"quarto-version":"1.3.450","fontsize":"1.1em","theme":["pulse","../../html/styles.scss"],"anchor-sections":true,"fig-cap-location":"margin","footnotes-hover":true,"title":"Serum Proteomics Atherosclerosis","date":"2020-11-11","categories":["publications","serum"],"image":"fig/roc-plot.png"},"extensions":{"book":{"multiFile":true}}}},"projectFormats":["html"]} \ No newline at end of file +{"title":"Serum Proteomics Atherosclerosis","markdown":{"yaml":{"title":"Serum Proteomics Atherosclerosis","date":"2020-11-11","categories":["publications","serum"],"image":"fig/roc-plot.png"},"containsRefs":false,"markdown":"\nAtherosclerotic cardiovascular diseases are the major causes of mortality and morbidity in developed world. Thickening of the carotid arterial wall (plaque formation) are indicative of active atherosclerotic process. \nMyocardial infarction and stroke are the clinical events associated with an acute rupture of a critically located atherosclerotic plaque. Currently, ultrasonic assessment of carotid artery intima media thickness is used as a pre-clinical marker of the disease process. \nNevertheless, atherosclerotic process may remain asymptomatic for several decades and whether thickening of intima-media layer of carotid artery and carotid plaque represents two different phenotypes or single traits of disease process is still unclear. \n\nTo gain insights into the pathophysiology of pre-clinical atherosclerosis and identify novel biomarkers, we conducted the **[serum proteomics measurements](https://www.nature.com/articles/s41598-018-27265-9#Sec1) on the unique sample set of participants recruited in [The Cardiovascular Risk in Young Finns Study](https://youngfinnsstudy.utu.fi/studydesign.html).** \nWe performed label free quantitative MS analysis of serum samples obtained from the subjects in whom early signs of plaques were discerned together with matched controls. The serum samples were immunodepleted to remove high abundant proteins prior to LC-MS/MS analysis.\n\nThe profiling results indicated differential abundances in a set of proteins. \nFurthermore, selected reaction monitoring mass spectrometry analysis were performed on undepleted serum samples to verify the observed differences. \nFinally, machine learning analysis identified a panel of three proteins **P23142-4 (Fibulin 1 proteoform C), P02649 (Apolipoprotein E) and P55290 (Cadherin-13)** which segregated the cases from controls with best discrimination (an area under receiver-operating characteristic curve (AUROC) value of 0.79.). \n\n{}\nMore details about the data analysis can be found **[here](https://github.com/santoshdbhosale/Carotid_Atherosclerosis_LFQ).**\n","srcMarkdownNoYaml":"\nAtherosclerotic cardiovascular diseases are the major causes of mortality and morbidity in developed world. Thickening of the carotid arterial wall (plaque formation) are indicative of active atherosclerotic process. \nMyocardial infarction and stroke are the clinical events associated with an acute rupture of a critically located atherosclerotic plaque. Currently, ultrasonic assessment of carotid artery intima media thickness is used as a pre-clinical marker of the disease process. \nNevertheless, atherosclerotic process may remain asymptomatic for several decades and whether thickening of intima-media layer of carotid artery and carotid plaque represents two different phenotypes or single traits of disease process is still unclear. \n\nTo gain insights into the pathophysiology of pre-clinical atherosclerosis and identify novel biomarkers, we conducted the **[serum proteomics measurements](https://www.nature.com/articles/s41598-018-27265-9#Sec1) on the unique sample set of participants recruited in [The Cardiovascular Risk in Young Finns Study](https://youngfinnsstudy.utu.fi/studydesign.html).** \nWe performed label free quantitative MS analysis of serum samples obtained from the subjects in whom early signs of plaques were discerned together with matched controls. The serum samples were immunodepleted to remove high abundant proteins prior to LC-MS/MS analysis.\n\nThe profiling results indicated differential abundances in a set of proteins. \nFurthermore, selected reaction monitoring mass spectrometry analysis were performed on undepleted serum samples to verify the observed differences. \nFinally, machine learning analysis identified a panel of three proteins **P23142-4 (Fibulin 1 proteoform C), P02649 (Apolipoprotein E) and P55290 (Cadherin-13)** which segregated the cases from controls with best discrimination (an area under receiver-operating characteristic curve (AUROC) value of 0.79.). \n\n{}\nMore details about the data analysis can be found **[here](https://github.com/santoshdbhosale/Carotid_Atherosclerosis_LFQ).**\n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"engine":"markdown"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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citation:","title-block-author-single":"Author","title-block-author-plural":"Authors","title-block-affiliation-single":"Affiliation","title-block-affiliation-plural":"Affiliations","title-block-published":"Published","title-block-modified":"Modified","title-block-keywords":"Keywords","callout-tip-title":"Tip","callout-note-title":"Note","callout-warning-title":"Warning","callout-important-title":"Important","callout-caution-title":"Caution","code-summary":"Code","code-tools-menu-caption":"Code","code-tools-show-all-code":"Show All Code","code-tools-hide-all-code":"Hide All Code","code-tools-view-source":"View Source","code-tools-source-code":"Source Code","tools-share":"Share","tools-download":"Download","code-line":"Line","code-lines":"Lines","copy-button-tooltip":"Copy to Clipboard","copy-button-tooltip-success":"Copied!","repo-action-links-edit":"Edit this page","repo-action-links-source":"View source","repo-action-links-issue":"Report an issue","back-to-top":"Back to 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at end of file diff --git a/.quarto/idx/blog/Serum-Proteomics-Pre-diabetic/index.qmd.json b/.quarto/idx/blog/Serum-Proteomics-Pre-diabetic/index.qmd.json index 1308a93..989b5ce 100644 --- a/.quarto/idx/blog/Serum-Proteomics-Pre-diabetic/index.qmd.json +++ b/.quarto/idx/blog/Serum-Proteomics-Pre-diabetic/index.qmd.json @@ -1 +1 @@ -{"title":"Serum Proteomics Pre diabetic","markdown":{"yaml":{"title":"Serum Proteomics Pre diabetic","date":"2020-10-27","categories":["publications","serum"]},"containsRefs":false,"markdown":"\n\nType-1 diabetes (T1D) is an autoimmune disease that is characterized by the destruction of the insulin producing β cells in the Islets of Langerhans of the pancreas. Currently the measurement of autoantibodies (Aabs) like islet-cell autoantibodies, protein tyrosine phosphatase, glutamic acid decarboxylase, insulin and zinc transporter Slc30A8 protein indicates the manifestation of β cells autoimmunity and increased disease risk. However, the destruction of β cells usually starts early in life and symptoms appears when 90% of the cells are destroyed. The time period of appearance of first Aabs to the onset of the clinical disease can vary from 1 month to over 10 years, moreover, not all Aab positive subjects develop T1D. Thus additional indicators of early disease process and progression are needed. To identify disease associated changes, a careful selection of study group is essential such as **The Finnish Type-1 Diabetes Prediction and Prevention project [DIPP](https://dipp.fi/?page_id=5239&lang=en)** has initiated in 1994. The DIPP cohort has collected blood serum samples at 3 to 6 months intervals from children with a genetically conferred T1D risk and tested for T1D associated autoantibodies. **These longitudinal series of samples cover all the stages of disease progression from birth to clinical T1D and matching samples from carefully matched healthy children.**\n\nWe utilized such unique samples from the DIPP cohort to identify early serum protein biomarkers associated with T1D using quantitative mass spectrometry based approach. The study involved LC-MS/MS analysis with both iTRAQ and label-free quantification strategy on the immunodepleted serum.\n\nPrevious serum proteomics biomarker studies of T1D have typically compared disease end points with control groups, i.e. the differences between patients with T1D and healthy controls. In contrast to the published reports, to our knowledge we have shown for the **[first time serum proteomics profile of pre-diabetic children](https://diabetes.diabetesjournals.org/content/64/6/2265)**, mapping the changes from early infancy, seroconversion and diagnosis. The main finding included lower and higher levels of APOC4 and AFAM in cases compared to controls respectively and, the combination of this two proteins classified T1D developing children from controls with 91% success rate with an area under the curve value of 0.85. Notably the levels of APOC4 were found to be lower even before seroconversion.\n","srcMarkdownNoYaml":"\n\nType-1 diabetes (T1D) is an autoimmune disease that is characterized by the destruction of the insulin producing β cells in the Islets of Langerhans of the pancreas. Currently the measurement of autoantibodies (Aabs) like islet-cell autoantibodies, protein tyrosine phosphatase, glutamic acid decarboxylase, insulin and zinc transporter Slc30A8 protein indicates the manifestation of β cells autoimmunity and increased disease risk. However, the destruction of β cells usually starts early in life and symptoms appears when 90% of the cells are destroyed. The time period of appearance of first Aabs to the onset of the clinical disease can vary from 1 month to over 10 years, moreover, not all Aab positive subjects develop T1D. Thus additional indicators of early disease process and progression are needed. To identify disease associated changes, a careful selection of study group is essential such as **The Finnish Type-1 Diabetes Prediction and Prevention project [DIPP](https://dipp.fi/?page_id=5239&lang=en)** has initiated in 1994. The DIPP cohort has collected blood serum samples at 3 to 6 months intervals from children with a genetically conferred T1D risk and tested for T1D associated autoantibodies. **These longitudinal series of samples cover all the stages of disease progression from birth to clinical T1D and matching samples from carefully matched healthy children.**\n\nWe utilized such unique samples from the DIPP cohort to identify early serum protein biomarkers associated with T1D using quantitative mass spectrometry based approach. The study involved LC-MS/MS analysis with both iTRAQ and label-free quantification strategy on the immunodepleted serum.\n\nPrevious serum proteomics biomarker studies of T1D have typically compared disease end points with control groups, i.e. the differences between patients with T1D and healthy controls. In contrast to the published reports, to our knowledge we have shown for the **[first time serum proteomics profile of pre-diabetic children](https://diabetes.diabetesjournals.org/content/64/6/2265)**, mapping the changes from early infancy, seroconversion and diagnosis. The main finding included lower and higher levels of APOC4 and AFAM in cases compared to controls respectively and, the combination of this two proteins classified T1D developing children from controls with 91% success rate with an area under the curve value of 0.85. Notably the levels of APOC4 were found to be lower even before seroconversion.\n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"engine":"markdown"},"render":{"keep-tex":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true,"format-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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diabetic","date":"2020-10-27","categories":["publications","serum"]},"containsRefs":false,"markdown":"\n\nType-1 diabetes (T1D) is an autoimmune disease that is characterized by the destruction of the insulin producing β cells in the Islets of Langerhans of the pancreas. Currently the measurement of autoantibodies (Aabs) like islet-cell autoantibodies, protein tyrosine phosphatase, glutamic acid decarboxylase, insulin and zinc transporter Slc30A8 protein indicates the manifestation of β cells autoimmunity and increased disease risk. However, the destruction of β cells usually starts early in life and symptoms appears when 90% of the cells are destroyed. The time period of appearance of first Aabs to the onset of the clinical disease can vary from 1 month to over 10 years, moreover, not all Aab positive subjects develop T1D. Thus additional indicators of early disease process and progression are needed. To identify disease associated changes, a careful selection of study group is essential such as **The Finnish Type-1 Diabetes Prediction and Prevention project [DIPP](https://dipp.fi/?page_id=5239&lang=en)** has initiated in 1994. The DIPP cohort has collected blood serum samples at 3 to 6 months intervals from children with a genetically conferred T1D risk and tested for T1D associated autoantibodies. **These longitudinal series of samples cover all the stages of disease progression from birth to clinical T1D and matching samples from carefully matched healthy children.**\n\nWe utilized such unique samples from the DIPP cohort to identify early serum protein biomarkers associated with T1D using quantitative mass spectrometry based approach. The study involved LC-MS/MS analysis with both iTRAQ and label-free quantification strategy on the immunodepleted serum.\n\nPrevious serum proteomics biomarker studies of T1D have typically compared disease end points with control groups, i.e. the differences between patients with T1D and healthy controls. In contrast to the published reports, to our knowledge we have shown for the **[first time serum proteomics profile of pre-diabetic children](https://diabetes.diabetesjournals.org/content/64/6/2265)**, mapping the changes from early infancy, seroconversion and diagnosis. The main finding included lower and higher levels of APOC4 and AFAM in cases compared to controls respectively and, the combination of this two proteins classified T1D developing children from controls with 91% success rate with an area under the curve value of 0.85. Notably the levels of APOC4 were found to be lower even before seroconversion.\n","srcMarkdownNoYaml":"\n\nType-1 diabetes (T1D) is an autoimmune disease that is characterized by the destruction of the insulin producing β cells in the Islets of Langerhans of the pancreas. Currently the measurement of autoantibodies (Aabs) like islet-cell autoantibodies, protein tyrosine phosphatase, glutamic acid decarboxylase, insulin and zinc transporter Slc30A8 protein indicates the manifestation of β cells autoimmunity and increased disease risk. However, the destruction of β cells usually starts early in life and symptoms appears when 90% of the cells are destroyed. The time period of appearance of first Aabs to the onset of the clinical disease can vary from 1 month to over 10 years, moreover, not all Aab positive subjects develop T1D. Thus additional indicators of early disease process and progression are needed. To identify disease associated changes, a careful selection of study group is essential such as **The Finnish Type-1 Diabetes Prediction and Prevention project [DIPP](https://dipp.fi/?page_id=5239&lang=en)** has initiated in 1994. The DIPP cohort has collected blood serum samples at 3 to 6 months intervals from children with a genetically conferred T1D risk and tested for T1D associated autoantibodies. **These longitudinal series of samples cover all the stages of disease progression from birth to clinical T1D and matching samples from carefully matched healthy children.**\n\nWe utilized such unique samples from the DIPP cohort to identify early serum protein biomarkers associated with T1D using quantitative mass spectrometry based approach. The study involved LC-MS/MS analysis with both iTRAQ and label-free quantification strategy on the immunodepleted serum.\n\nPrevious serum proteomics biomarker studies of T1D have typically compared disease end points with control groups, i.e. the differences between patients with T1D and healthy controls. In contrast to the published reports, to our knowledge we have shown for the **[first time serum proteomics profile of pre-diabetic children](https://diabetes.diabetesjournals.org/content/64/6/2265)**, mapping the changes from early infancy, seroconversion and diagnosis. The main finding included lower and higher levels of APOC4 and AFAM in cases compared to controls respectively and, the combination of this two proteins classified T1D developing children from controls with 91% success rate with an area under the curve value of 0.85. Notably the levels of APOC4 were found to be lower even before seroconversion.\n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"engine":"markdown"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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extracts information from research publications by using individual's Google Scholar ID.\n\n 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\n \n Download current CV\n \n
\n \n```\n\n\n","srcMarkdownNoYaml":"\n\n```{css echo=FALSE}\n.embed-container {\n position: relative;\n padding-bottom: 129%;\n height: 0;\n overflow: hidden;\n max-width: 100%;\n}\n.embed-container iframe,\n.embed-container object,\n.embed-container embed {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n}\n```\n\n```{=html}\n\n \n Download current CV\n \n
\n \n```\n\n\n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"engine":"knitr"},"render":{"keep-tex":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true,"format-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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navigation","crossref-fig-title":"Figure","crossref-tbl-title":"Table","crossref-lst-title":"Listing","crossref-thm-title":"Theorem","crossref-lem-title":"Lemma","crossref-cor-title":"Corollary","crossref-prp-title":"Proposition","crossref-cnj-title":"Conjecture","crossref-def-title":"Definition","crossref-exm-title":"Example","crossref-exr-title":"Exercise","crossref-ch-prefix":"Chapter","crossref-apx-prefix":"Appendix","crossref-sec-prefix":"Section","crossref-eq-prefix":"Equation","crossref-lof-title":"List of Figures","crossref-lot-title":"List of Tables","crossref-lol-title":"List of Listings","environment-proof-title":"Proof","environment-remark-title":"Remark","environment-solution-title":"Solution","listing-page-order-by":"Order By","listing-page-order-by-default":"Default","listing-page-order-by-date-asc":"Oldest","listing-page-order-by-date-desc":"Newest","listing-page-order-by-number-desc":"High to Low","listing-page-order-by-number-asc":"Low to 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vitae","markdown":{"yaml":{"layout":"page","title":"Curriculum vitae","excerpt":"My current CV","execute":{"freeze":true},"engine":"knitr","resources":["cv.pdf"],"cv":{"pdf":"cv.pdf"}},"containsRefs":false,"markdown":"\n\n```{css echo=FALSE}\n.embed-container {\n position: relative;\n padding-bottom: 129%;\n height: 0;\n overflow: hidden;\n max-width: 100%;\n}\n.embed-container iframe,\n.embed-container object,\n.embed-container embed {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n}\n```\n\n```{=html}\n\n \n Download current CV\n \n
\n \n```\n\n\n","srcMarkdownNoYaml":"\n\n```{css echo=FALSE}\n.embed-container {\n position: relative;\n padding-bottom: 129%;\n height: 0;\n overflow: hidden;\n max-width: 100%;\n}\n.embed-container iframe,\n.embed-container object,\n.embed-container embed {\n position: absolute;\n top: 0;\n left: 0;\n width: 100%;\n height: 100%;\n}\n```\n\n```{=html}\n\n \n Download current CV\n \n
\n \n```\n\n\n"},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":null,"freeze":true,"echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"engine":"knitr"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":"none","code-overflow":"scroll","code-link":true,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","toc":false,"reference-location":"margin","output-file":"index.html"},"language":{"toc-title-document":"Table 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Bhosale, pharmacist by training and currently working as an associate biomedical scientist at **[Cedars-Sinai Precision biomarker laboratories](https://www.cs-pbl.com/)**, Los Angeles, CA, USA.\n\nUsing the versatility of mass spectrometry-based proteomics, I am exploring the properties of biologically important proteins.\n\nPrecisely, I work on mass spectrometry-based proteomics technologies to address the questions related to biomedical research. These includes not only the qualitative and quantitative measurement of proteins, but also their post-translational modifications and interactions with other proteins. I use computational methods to understand the complexity of proteins.\n\n{}\n\n\n\n","srcMarkdownNoYaml":"\n\n::: {#about-block}\n:::\n\n# Understanding the Complexity of Proteome!\n\nHi, I'm Santosh D. 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Bhosale, pharmacist by training and currently working as an associate biomedical scientist at **[Cedars-Sinai Precision biomarker laboratories](https://www.cs-pbl.com/)**, Los Angeles, CA, USA.\n\nUsing the versatility of mass spectrometry-based proteomics, I am exploring the properties of biologically important proteins.\n\nPrecisely, I work on mass spectrometry-based proteomics technologies to address the questions related to biomedical research. These includes not only the qualitative and quantitative measurement of proteins, but also their post-translational modifications and interactions with other proteins. 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