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-install.packages('downlit') -install.packages('xml2') -library(rmarkdown) -library(rmarkdown) -library(xml2) -library(xml2) -library(downlit) -library(rmarkdown) -library(xml2) -library(downlit) -setwd("C:/Data/Documents/Personal/CV-first-repo-master") -install.packages("rmarkdown") -library(rmarkdown) -install.packages(downlit) -install.packages("downlit") -install.packages("xml2") -library(downlit) -library(downlit) -library(xml2) -library(rmarkdown) -Rscript -e "install.packages('rmarkdown') -Rscript -e "install.packages('rmarkdown') -Rscript -e "install.packages('rmarkdown')" -packageVersion('rmarkdown') -library(markdown) +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','DLAT','TST','RPP40','HIGD2A', +'MRPS30','NOM1','SLC1A1','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.5]~ 'fold change'), +pCutoff = 10e-3, +FCcutoff = 0.5, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +vc = EnhancedVolcano(df, +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','DLAT','TST','RPP40','HIGD2A', +'MRPS30','NOM1','SLC1A1','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-3, +FCcutoff = 0.5, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +vc = EnhancedVolcano(df, +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','DLAT','TST','RPP40','HIGD2A', +'MRPS30','NOM1','SLC1A1','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-1, +FCcutoff = 0.5, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +vc = EnhancedVolcano(df, +lab = Genes(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','DLAT','TST','RPP40','HIGD2A', +'MRPS30','NOM1','SLC1A1','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-1, +FCcutoff = 0.5, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +vc = EnhancedVolcano(df, +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','DLAT','TST','RPP40','HIGD2A', +'MRPS30','NOM1','SLC1A1','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-1, +FCcutoff = 0.5, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +vc = EnhancedVolcano(df, +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','DLAT','TST','RPP40','HIGD2A', +'MRPS30','NOM1','SLC1A1','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-1, +FCcutoff = 0.1, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +vc = EnhancedVolcano(df, +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','DLAT','TST','RPP40','HIGD2A', +'MRPS30','NOM1','SLC1A1','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-1, +FCcutoff = 0.2, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +# load the package +library(EnhancedVolcano) +vc = EnhancedVolcano(df, +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-1, +FCcutoff = 0.2, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +View(df) +EnhancedVolcano(df, +lab = rownames(df), +x = 'log2FC', +y = 'pvalues', +title = 'N061011 versus N61311', +pCutoff = 10e-1, +FCcutoff = 0.5, +pointSize = 3.0, +labSize = 6.0) +View(df) +EnhancedVolcano(df, +lab = rownames(df), +x = 'log2FC', +y = 'pvalues', +title = 'N061011 versus N61311', +pCutoff = 10e-1, +FCcutoff = 0.5, +pointSize = 3.0, +labSize = 6.0) +vc = EnhancedVolcano(df, +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-1, +FCcutoff = 0.2, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +df = read.delim(file = 'volcano.tsv') +View(df) +vc = EnhancedVolcano(df, +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-1, +FCcutoff = 0.2, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +install.packages('tidyverse') +library(tidyverse) +df = read.delim(file = 'volcano.tsv') +View(df) +vc = EnhancedVolcano(df, +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-1, +FCcutoff = 0.2, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +# 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') +View(df) +vc = EnhancedVolcano(df, +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-1, +FCcutoff = 0.2, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +vc = EnhancedVolcano(df, +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectlab = c('GEMIN7','STX10','WASHC3', +'PTCD3','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-1, +FCcutoff = 0.2, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +library(tidyselect) +vc = EnhancedVolcano(df, +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-1, +FCcutoff = 0.2, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 'bold', +boxedLabels = TRUE, +colAlpha = 4/5, +legendPosition = 'right', +legendLabSize = 14, +legendIconSize = 4.0, +drawConnectors = TRUE, +widthConnectors = 1.0, +colConnectors = 'black',xlim=c(-2, 2)) +vc +mode(df) +df = as.matrix(df) +mode(df) +vc = EnhancedVolcano(df, +lab = rownames(df), +x = 'Log2FC', +y = 'pvalues', +selectLab = c('GEMIN7','STX10','WASHC3', +'PTCD3','CHCHD5','PHF14', +'DENND6A','TRMT5','CETN2','COG8','GTF2H3','SURF4'), +xlab = bquote(~Log[0.1]~ 'fold change'), +pCutoff = 10e-1, +FCcutoff = 0.2, +pointSize = 4.0, +labSize = 6.0, +labCol = 'black', +labFace = 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"collab_server": "", + "source_window": "", + "last_content_update": 1697490117624, + "read_only": false, + "read_only_alternatives": [] +} \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/0CB95668-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/0CB95668-contents new file mode 100644 index 0000000..ffacfb4 --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-542659ec/0CB95668-contents @@ -0,0 +1,27 @@ +--- +title: "Proteomics data analysis and _visualization_ (no programming skills..its okay..but)" +date: 2023-10-10 +categories: + - data analysis +--- + +Mass 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/). + +Typical 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. + +- Quality control checks. +- Database search and quantitative analysis. +- Statistical analysis +- Functional annotation analysis + +In this blog post, I will highlight the tools available to process the mass spectrometry data by outlining the above headings. + +1. **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. + + Often times to use the functionality of some tools, users needs to convert the proprietary MS files into generic file format such as mzmL + + *DDA analysis* + + - [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. + - [RawBeans](https://bitbucket.org/incpm/prot-qc/downloads/): generates an interactive html report for + - [QuiC ™](https://biognosys.com/software/quic/): Properitary software from Biognosys, supports most of data acquisition mode (SRM, PRM, DIA or DDA) but it requirs addition of iRT peptides in the samples. diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/0D63C7D6-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/0D63C7D6-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/1E5A88AA-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/1E5A88AA-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/27CCD94C-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/27CCD94C-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-1bfafdf1/8E3BA3B8 b/.Rproj.user/224B9E6C/sources/session-542659ec/2AF20336 similarity index 52% rename from .Rproj.user/224B9E6C/sources/session-1bfafdf1/8E3BA3B8 rename to .Rproj.user/224B9E6C/sources/session-542659ec/2AF20336 index 184ba6b..f686f4b 100644 --- a/.Rproj.user/224B9E6C/sources/session-1bfafdf1/8E3BA3B8 +++ b/.Rproj.user/224B9E6C/sources/session-542659ec/2AF20336 @@ -1,27 +1,27 @@ { - "id": "8E3BA3B8", - "path": null, + "id": "2AF20336", + "path": "C:/Users/BhosaleS/Downloads/VolcanoPlot.R", "project_path": null, "type": "r_source", "hash": "0", "contents": "", - "dirty": true, - "created": 1693005746197.0, + "dirty": false, + "created": 1697223262252.0, "source_on_save": false, - "relative_order": 1, + "relative_order": 3, "properties": { "tempName": "Untitled1", "source_window_id": "", "Source": "Source", - "cursorPosition": "1,0", - "scrollLine": "0" + "cursorPosition": "35,0", + "scrollLine": "15" }, "folds": "", - "lastKnownWriteTime": 7782220368668085589, - "encoding": "", + "lastKnownWriteTime": 1697237846, + "encoding": "UTF-8", "collab_server": "", "source_window": "", - "last_content_update": 1696617095343, + "last_content_update": 1697237846893, "read_only": false, "read_only_alternatives": [] } \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/2AF20336-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/2AF20336-contents new file mode 100644 index 0000000..fa7f1d4 --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-542659ec/2AF20336-contents @@ -0,0 +1,36 @@ +# 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') +View(df) +EnhancedVolcano(df, + lab = rownames(df), + x = 'log2FoldChange', + y = 'pvalue', + selectLab = c('GEMIN7','STX10','WASHC3', + 'PTCD3','CHCHD5','PHF14', + 'DENND6A','TRMT5','CETN2','COG8', + 'GTF2H3','SURF4'), + 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 = 10, + legendIconSize = 4.0, + drawConnectors = TRUE, + widthConnectors = 1.0, + colConnectors = 'black',xlim=c(-2, 2), ylim = c(0,8)) + +sessionInfo() diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/35037C83-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/35037C83-contents new file mode 100644 index 0000000..64ea472 --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-542659ec/35037C83-contents @@ -0,0 +1,8 @@ +if (!require("BiocManager", quietly = TRUE)) + install.packages("BiocManager") + +BiocManager::install("airway") +library(airway) +data('airway') +head(airway) +str(airway) diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/44309F40-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/44309F40-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/4E6B7132-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/4E6B7132-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/624DD1E9 b/.Rproj.user/224B9E6C/sources/session-542659ec/624DD1E9 new file mode 100644 index 0000000..a5d64bd --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-542659ec/624DD1E9 @@ -0,0 +1,26 @@ +{ + "id": "624DD1E9", + "path": "~/GitHub/santoshdbhosale.github.io/blog/index.qmd", + "project_path": "blog/index.qmd", + "type": "quarto_markdown", + "hash": "0", + "contents": "", + "dirty": false, + "created": 1696972035590.0, + "source_on_save": false, + "relative_order": 1, + "properties": { + "source_window_id": "", + "Source": "Source", + "cursorPosition": "11,59", + "scrollLine": "0" + }, + "folds": "", + "lastKnownWriteTime": 1696973807, + "encoding": "UTF-8", + "collab_server": "", + "source_window": "", + "last_content_update": 1696973807128, + "read_only": false, + "read_only_alternatives": [] +} \ No newline at end of file diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/624DD1E9-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/624DD1E9-contents new file mode 100644 index 0000000..1b4f28e --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-542659ec/624DD1E9-contents @@ -0,0 +1,26 @@ +--- +author: "" +title-block-banner: false +page-layout: full +description-meta: "Welcome to my blog, Here, you will find a collection of posts for some of the previously published articles" +listing: + contents: + - "Serum-Proteomics-Pre-diabetic/index.qmd" + - "Serum-Proteomics-Atherosclerosis/index.qmd" + - "Mass-Spectrometry-Based-Serum-Proteomics/index.qmd" + - "AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd" + - "Proteomics-data-analysis-&-visualization/index.qmd" + sort: "date desc" + categories: true +toc-title: Year +toc-location: right +date-format: "MMMM D, YYYY" +image: "" +code-tools: false +comments: false +--- + +Welcome to my blog, here, you will find a collection of posts for some of the previously published articles! + + + diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/6BD76726-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/6BD76726-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/765A622D-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/765A622D-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/8072100C-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/8072100C-contents new file mode 100644 index 0000000..f5cc5fc --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-542659ec/8072100C-contents @@ -0,0 +1,86 @@ +skills <- tribble( + ~area, ~skills, + "Wet lab", " Cell culture work, animal handling and clinical samples, Cell & tissue sample lysis, SDS-PAGE electrophoresis (1D, 2D) and western blotting ", + "High throughput proteomics", "Trypsin digestion, immunodepletion of serum &/or plasma samples, label free quantification, isobaric labeling, off-line high pH fractionation, PTMs enrichment and immunoprecipitation experiments", + "Mass spectrometry", "Operation and troubleshooting of a range of instruments LTQ Orbitrap Velos Pro, Q Exactive series, Orbitrap Exploris 480 Mass Spectrometer, TSQ Vantage (all from Thermo Scientific), timsTOF Pro (BRUKER), MALDI-TOF-MS (Applied Biosystem)", + "Mass spectrometry informatics tools ", " Xcalibur, Proteome Discoverer (Thermo Scientific), Bruker timsControl and Compass HyStar, MaxQuant and Perseus, Progenesis, Skyline, InfernoRDN, FragPipe, Spectronaut (Biognosys) and DIA-NN", + "Chromatography instrumentation ", " Easy nLC series (Thermo Scientific)", + "Automation platform", "SP100 Automation instrument (Hamilton robot), Biomek i-Series Automated workstation", + "Language and softwares ", " R, Python, Machine learning, Jupyter enviornment, Omics data, Cytoscape and Ingenuity Pathway Analysis " +) + +awards <- tribble( + ~area, ~accomplishment, ~year, ~where, ~detail, + "Doctoral dissertation award", "Awarded with EUR 5000 from Orion Pharma", 2018, "Turku - Finland",NA, + "Doctoral dissertation award", "Awarded with EUR 5000 from The Maud Kuistila Memorial Foundation", 2018, "Turku - Finland",NA, + "Travel grant", "Awarded with EUR 500 to attend computational proteomics course at ETH Zurich from Turku centre for system biology", 2015, "Turku - Finland",NA, + "Research grant", "Awarded with EUR 3500 from Hospital District of Southwest Finland & Turku City", 2014, "Turku - Finland", NA, + "Dr. Ashok B. Vaidya prize ", "Secured first position in an oral session (6 minute competition) organized by South Asian Chapter of American College of Clinical Pharmacology", 2009, "Mumbai - India", NA +) + +edu <- tribble( + ~degree, ~startYear, ~endYear, ~inst, ~where, ~detail, + " University of Turku (Turku Bioscience)", 2012, 2018, "PhD", "Turku - Finland","Developed quantitative proteomics methodology for the analysis of human serum samples, including immunoaffinity depletion, protein digestion, isobaric labelling, label free quantification, offline-SCX fractionation, LC-MS/MS and data analysis", + " University of Turku (Turku Bioscience)", 2012, 2018, "PhD", "Turku - Finland","Developed targeted SRM-LC-MS methods to monitor multiple protein targets", + " University of Turku (Turku Bioscience)", 2012, 2018, "PhD", "Turku - Finland","Cellular proteomic analyses of Th17 and iTreg cells from mouse and human", + " University of Turku (Turku Bioscience)", 2012, 2018, "PhD", "Turku - Finland","Teaching experience in proteomics data analysis (presented at a national meeting, 2017)", + " Rajasthan University of Health Sciences (Lachoo Memorial College of Science & Technology)", 2005, 2008, "Master of Pharmacy (Pharmaceutical Chemistry)", "Jodhpur - India",NA, + " University of Pune (Sitabai Thite College of Pharmacy)", 2001, 2005, "Bachelor of Pharmacy", "Shirur - Pune",NA +) + +work <- tribble( + ~title, ~unit, ~startMonth, ~startYear, ~endMonth, ~endYear, ~where, ~detail, + "Associate Biomedical Scientist", "Cedars-Sinai Precision Biomarker Laboratories", "February", 2023, "Present", NA, "Los Angeles - USA","Research and development operations realted to clinical proteomics", + "Associate Biomedical Scientist", "Cedars-Sinai Precision Biomarker Laboratories", "February", 2023, "Present", NA, "Los Angeles - USA","Client facing role", + "Postdoctoral Researcher", "Protein Research Group, Department of Biochemistry and Molecular Biology, University of Southern Denmark", "January", 2020, "December", 2022, "Odense - Denmark", "Development of a post-translational modification (Cysteine, N-linked glycosylated and phospho modified) specific biomarkers discovery platform for the diagnosis of disease", + "Postdoctoral Researcher", "Protein Research Group, Department of Biochemistry and Molecular Biology, University of Southern Denmark", "January", 2020, "December", 2022, "Odense - Denmark", "Analysis of PTMomics data to identify candidate plasma biomarkers to stratify ovarian cancer patients", + "Postdoctoral Researcher", "Protein Research Group, Department of Biochemistry and Molecular Biology, University of Southern Denmark", "January", 2020, "December", 2022, "Odense - Denmark", "Supervise and work with technician and PhD students", + "Postdoctoral Researcher", "Protein Research Group, Department of Biochemistry and Molecular Biology, University of Southern Denmark", "January", 2020, "December", 2022, "Odense - Denmark", "Work presentation internally and to the collaborators and, report writing", + "Postdoctoral Researcher ", " University of Turku - Turku Bioscience ", "November", 2018, "December", 2019, "Turku - Finland","Serum proteomics measurements to compare the effects of nutrition supplementation in infancy and, child and mother proteome correlation", + "Postdoctoral Researcher ", " University of Turku - Turku Bioscience ", "November", 2018, "December", 2019, "Turku - Finland","Analyzed temporal serum proteomes of celiac disease (CD) developing children", + "Postdoctoral Researcher ", " University of Turku - Turku Bioscience ", "November", 2018, "December", 2019, "Turku - Finland"," Conducted the interactomics measurements and data analysis for several trasnscription factors of T cells", + "Postdoctoral Researcher ", " University of Turku - Turku Bioscience ", "November", 2018, "December", 2019, "Turku - Finland"," Designed and presented practical courses on proteomics data analysis", + "Project Assistant", "National Chemical Laboratory", "November", 2009, "December", 2011, "Pune - India", "Proteomics laboratory work including protein extraction, digestion and cleanup, SDS-PAGE, MS analysis of glycated proteins, oligonulceotides and small molecules", + "Lecturer","JSPMs JSCOPR affiliated to University of Pune","July", 2008, "November", 2009, "Pune - India", "Taught theory and practicals for pharmaceutical biochemistry and pharmaceutical analysis to the bachelor of pharmacy students", + "Lecturer","JSPMs JSCOPR affiliated to University of Pune","July", 2008, "November", 2009, "Pune - India"," Supervised undergraduate pharmacy students", + "Lecturer","JSPMs JSCOPR affiliated to University of Pune","July", 2008, "November", 2009, "Pune - India"," Graded course assignments and examinations" +) + +ref <- tribble( + ~Name, ~Title, ~Contact, + 'Riitta Lahesmaa, M.D., Ph. D.', "Professor, Director, Turku Bioscience, P.O. Box 123 Biocity, FIN-20520, Turku, Finland", "rilahes@utu.fi", + 'Robert Moulder, Ph.D.', "Senior Scientist, Turku Bioscience, P.O. Box 123 Biocity, FIN-20520, Turku, Finland", "robmou@utu.fi", + 'Mahesh J. Kulkarni, Ph.D.', "Senior Principal Scientist, Biochemical Sciences Division, CSIR-National Chemical Laboratory, Pune, India", "mj.kulkarni@ncl.res.in", + 'David R. Goodlett, Ph.D.', "Professor of Biochemistry & Microbiology and Director Genome BC Proteome Centre at University of Victoria, Victoria, British Columbia, Canada", "goodlett@uvic.ca", + 'Martin R. Larsen, Ph.D.', "Professor, Department of Biochemistry and Molecular Biology, Campusvej 55, Odense M 5230, Denmark", "mrl@bmb.sdu.dk", + 'Ole N. Jensen, Ph.D.', "Professor, Department of Biochemistry and Molecular Biology, Campusvej 55, Odense M 5230, Denmark", "jenseno@bmb.sdu.dk" +) + +pubs <- tribble( + ~Title, ~Authors, ~Journal, ~Year, + "Andrabi SBA, Batkulwar K, Bhosale SD, Moulder R, Khan MH, Buchacher T, Khan MM, Arnkil I, Rasool O, Marson A, Kalim UU, Lahesmaa R", "HIC1 interacts with FOXP3 multi protein complex: novel pleiotropic mechanisms to regulate human regulatory T cell differentiation and function","Immunol Lett","2023", + "Hirvonen MK, Lietzén N, Moulder R, Bhosale SD, Koskenniemi J, Vähä-Mäkilä M, Nurmio M, Orešič M, Ilonen J, Toppari J, Veijola R, Hyöty H, Lähdesmäki H, Knip M, Cheng L, Lahesmaa R", "Serum APOC1 levels are decreased in young autoantibody positive children who rapidly progress to type 1 diabetes", "Sci Rep", "2023", + "Beltrán-Camacho L, Bhosale SD, Sánchez-Morillo D, Sánchez-Gomar I, Rojas-Torres M, Eslava-Alcón S, Martínez-Torija M, Ruiz de Infante MA, Nieto-Martín MD, Rodríguez-Iglesias MA, Moreno JA, Berrocoso E, Larsen MR, Moreno-Luna R, Carmen Durán-Ruiz M", "Cardiovascular-related proteomic changes in ECFCs exposed to the serum of COVID-19 patients","Int J Biol Sci","2023", + "Shetty A, Tripathi SK, Junttila S, Buchacher T, Biradar R, Bhosale SD, Envall T, Laiho A, Moulder R, Rasool O, Galande S, Elo LL and Lahesmaa R","A systematic comparison of FOSL1, FOSL2 and BATF-mediated transcriptional regulation during early human Th17 differentiation","Nucleic Acids Res", "2022", + "Kang T, Bhosale S, Edwards A, Larsen MR", "Phosphoproteomics: Methods and Challenges", "Reference Module in Life Sciences", "2022", + "Souza Junior DR, Silva ARM, Rosa-Fernandes L, Reis LR, Alexandria G, Bhosale SD, Ghilardi FR, Dalçóquio TF, Bertolin AJ, Nicolau JC, Marinho CRF, Wrenger C, Larsen MR, Siciliano RF, Di Mascio P, Palmisano G, Ronsein GE", "HDL proteome remodeling associates with COVID-19 severity","J Clin Lipidol", "2021", + "Shetty A, Bhosale SD, Tripathi SK, Buchacher T, Biradar R, Rasool O, Moulder R, Galande S, Lahesmaa R", "Interactome Networks of FOSL1 and FOSL2 in Human Th17 Cells", "ACS Omega", "2021", + "Khan MM, Ullah U, Khan MH, Kong L, Moulder R, Välikangas T, Bhosale SD, Komsi E, Rasool O, Chen Z, Elo LL, Westermarck J, Lahesmaa R", "CIP2A Constrains Th17 Differentiation by Modulating STAT3 Signaling", "iScience", "2020", + "Khan MM, Välikangas T, Khan MH, Moulder R, Ullah U, Bhosale SD, Komsi E, Butt U, Qiao X, Westermarck J, Elo LL & Lahesmaa R", "Protein interactome of the Cancerous Inhibitor of protein phosphatase 2A (CIP2A) in Th17 cells","Current Research in Immunology", "2020", + "Tripathi SK, Välikangas T, Shetty A, Khan MM, Moulder R, Bhosale SD, Komsi E, Salo V, De Albuquerque RS, Rasool O, Galande S, Elo LL, Lahesmaa R", "Quantitative Proteomics Reveals the Dynamic Protein Landscape during Initiation of Human Th17 Cell Polarization", "iScience", "2019", + "Bhosale SD, Moulder R, Venäläinen MS, Koskinen JS, Pitkänen N, Juonala M, Kähönen M, Lehtimäki T, Viikari J, Elo LL, Goodlett DR, Lahesmaa R, Raitakari OT", "Serum Proteomic Profiling to Identify Biomarkers of Premature Carotid Atherosclerosis", "Sci Rep", "2018", + "Mohammad I, Nousiainen K, Bhosale SD, Starskaia I, Moulder R, Rokka A, Cheng F, Mohanasundaram P, Eriksson JE, Goodlett DR, Lähdesmäki H, Chen Z", "Quantitative proteomic characterization and comparison of T helper 17 and induced regulatory T cells", "PLos Biol", "2018", + "Moulder R, Bhosale SD, Goodlett DR, Lahesmaa R", "Analysis of the plasma proteome using iTRAQ and TMT-based Isobaric labeling", "Mass Spectrom Rev", "2018", + "Bhosale SD, Moulder R, Kouvonen P, Lahesmaa R, Goodlett DR", "Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation", "Methods Mol Biol", "2017", + "Moulder R, Bhosale SD, Lahesmaa R, Goodlett DR", "The progress and potential of proteomic biomarkers for type 1 diabetes in children", "Expert Rev Proteomics", "2017", + "Moulder R, Bhosale SD, Erkkilä T, Laajala E, Salmi J, Nguyen EV, Kallionpää H, Mykkänen J, Vähä-Mäkilä M, Hyöty H, Veijola R, Ilonen J, Simell T, Toppari J, Knip M, Goodlett DR, Lähdesmäki H, Simell O, Lahesmaa R", "Serum proteomes distinguish children developing type 1 diabetes in a cohort with HLA-conferred susceptibility", "Diabetes", "2015", + "Kesavan SK, Bhat S, Golegaonkar SB, Jagadeeshaprasad MG, Deshmukh AB, Patil HS, Bhosale SD, Shaikh ML, Thulasiram HV, Boppana R, Kulkarni MJ", "Proteome wide reduction in AGE modification in streptozotocin induced diabetic mice by hydralazine mediated transglycation", "Sci Rep", "2013", + "Bhonsle HS, Korwar AM, Kesavan SK, Bhosale SD, Bansode SB, Kulkarni MJ", "Zoom-ln A targeted database search for identification of glycation modifications analyzed by untargeted tandem mass spectrometry", "Eur J Mass Spectrom (Chichester)", "2012", + "Suresh KK, Bhosale SD, Thulasiram HV, Kulkarni MJ", "Comparative and chemical proteomic approaches reveal gatifloxacin deregulates enzymes involved in glucose metabolism", "J Toxicol Sci", "2011" +) + +patents <- tribble( + ~Title, ~Authors, ~where, ~detail, + "Moulder R, Bhosale SD, Goodlett D, Lähdesmäki H, Simell S, Lahesmaa R", "Means and methods for determining risk of type-1 diabetes by serum protein biomarkers", "Europe & USA", NA +) + diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/82E524E6-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/82E524E6-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/90689B73-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/90689B73-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/94D33331-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/94D33331-contents new file mode 100644 index 0000000..69e5e1f --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-542659ec/94D33331-contents @@ -0,0 +1,125 @@ +--- +name: Santosh +surname: Bhosale +position: "Associate Biomedical Scientist" +address: "Cedars-Sinai Precision Biomarker Laboratories, Los Angeles, CA, USA" +profilepic: "./img/SDB2.png" +email: "santosh.bhosale@cshs.org" +www: santoshdbhosale.github.io +linkedin: santoshdbhosale +github: santoshdbhosale +date: "`r format(Sys.time(), '%B %Y')`" +headcolor: 990000 +aboutme: "I do mass spectrometry based proteomics research, including discovery and validation of protein biomarkers in clinical samples. Collaborations with clinicians, mass spectrometry experts and bioinformaticians." +docname: CV +output: vitae::awesomecv +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = FALSE, + warning = FALSE, + message = FALSE) +library(vitae) +library(tibble) +library(magrittr) +library(here) +library(readr) + +source(file = here("r","data_withPatent.r")) + +``` + +# Research Proficiency + +```{r SKILLS} + +skills %>% + detailed_entries( + with = area, + what = skills + ) + +``` + +# Employment + +```{r EMPLOYMENT} + +work %>% + detailed_entries( + with = title, + what = unit, + why = detail, + when = glue::glue("{startMonth} {startYear} --> {endMonth} {endYear}",.na = ""), + where = where + ) + +``` + + +# Education + +```{r EDUCATION} + +edu %>% + detailed_entries( + with = inst, + what = degree, + why = detail, + when = glue::glue("{startYear} --> {endYear}",.na = ""), + where = where + ) + +``` + +# Awards + +```{r AWARDS} + +awards %>% + detailed_entries( + with = area, + what = accomplishment, + why = detail, + when = year, + where = where + ) + +``` + +# Publications + +```{r PUBLICATIONS} +pubs %>% + detailed_entries( + where = Journal, + with = Authors, + what = Title, + when = Year + ) +``` + +# Patent Applications +```{r PATENT} +patents %>% + detailed_entries( + where = where, + with = Authors, + what = Title, + why = detail + ) +``` + +\newpage + +# References + +```{r REFERENCES} +ref %>% + detailed_entries( + where = Contact, + with = Name, + what = Title + ) +``` + diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/951EA518-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/951EA518-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/96224A6E-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/96224A6E-contents new file mode 100644 index 0000000..12e4826 --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-542659ec/96224A6E-contents @@ -0,0 +1,38 @@ +# 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') +View(df) + +vc = EnhancedVolcano(df, + lab = rownames(df), + x = 'Log2FC', + y = 'pvalues', + selectLab = c('GEMIN7','STX10','WASHC3', + 'PTCD3','CHCHD5','PHF14', + 'DENND6A','TRMT5','CETN2','COG8', + 'GTF2H3','SURF4'), + xlab = bquote(~Log[0.1]~ 'fold change'), + pCutoff = 10e-1, + FCcutoff = 0.2, + pointSize = 4.0, + labSize = 6.0, + labCol = 'black', + labFace = 'bold', + boxedLabels = TRUE, + colAlpha = 4/5, + legendPosition = 'right', + legendLabSize = 14, + legendIconSize = 4.0, + drawConnectors = TRUE, + widthConnectors = 1.0, + colConnectors = 'black',xlim=c(-2, 2)) + +vc + diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/AA5D340F-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/AA5D340F-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/B1638977-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/B1638977-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/B30BC990-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/B30BC990-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/BB86565A-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/BB86565A-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/BC8529C5-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/BC8529C5-contents new file mode 100644 index 0000000..e552707 --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-542659ec/BC8529C5-contents @@ -0,0 +1,10 @@ +library(EnhancedVolcano) + +install.packages('airway') +library(airway) + +library(magrittr) + +data("airway") + +airway$dex %<>% relevel("untrt") diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/BDB3BDD0-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/BDB3BDD0-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/C1DF6354-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/C1DF6354-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/E99FE9EE-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/E99FE9EE-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/ECCC3F5D-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/ECCC3F5D-contents new file mode 100644 index 0000000..5780c9a --- /dev/null +++ b/.Rproj.user/224B9E6C/sources/session-542659ec/ECCC3F5D-contents @@ -0,0 +1,5 @@ +airway_deg = read.csv('airway_deg_results.csv') +EnhancedVolcano(airway_deg,lab=rownames(airway_deg),x="log2FoldChange",y="padj", + pCutoff=1e-15, FCcutoff=2, + selectLab=c("VCAM1", "WNT2", "KLF15", "ZBTB16")) + diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/F5378304-contents b/.Rproj.user/224B9E6C/sources/session-542659ec/F5378304-contents new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/224B9E6C/sources/session-542659ec/lock_file b/.Rproj.user/224B9E6C/sources/session-542659ec/lock_file new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/shared/notebooks/1E7152FE-CV/1/224B9E6C1bfafdf1/chunks.json b/.Rproj.user/shared/notebooks/1E7152FE-CV/1/224B9E6C1bfafdf1/chunks.json new file mode 100644 index 0000000..57ecb07 --- /dev/null +++ b/.Rproj.user/shared/notebooks/1E7152FE-CV/1/224B9E6C1bfafdf1/chunks.json @@ -0,0 +1 @@ +{"chunk_definitions":[],"doc_write_time":1695852840} \ No newline at end of file diff --git a/.Rproj.user/shared/notebooks/37B3EFD0-index/1/224B9E6C542659ec/chunks.json b/.Rproj.user/shared/notebooks/37B3EFD0-index/1/224B9E6C542659ec/chunks.json new 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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","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"},"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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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":"1.3.433","fontsize":"1.1em","theme":["pulse","../html/styles.scss"],"anchor-sections":true,"fig-cap-location":"margin","footnotes-hover":true,"about":{"template":"jolla","id":"about-block"}},"extensions":{"book":{"multiFile":true}}}},"projectFormats":["html"]} \ No newline at end of file +{"markdown":{"yaml":{"about":{"template":"jolla","id":"about-block"}},"containsRefs":false,"markdown":"\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","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"},"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 of contents","toc-title-website":"On this page","related-formats-title":"Other Formats","related-notebooks-title":"Notebooks","source-notebooks-prefix":"Source","section-title-abstract":"Abstract","section-title-appendices":"Appendices","section-title-footnotes":"Footnotes","section-title-references":"References","section-title-reuse":"Reuse","section-title-copyright":"Copyright","section-title-citation":"Citation","appendix-attribution-cite-as":"For attribution, please cite this work as:","appendix-attribution-bibtex":"BibTeX 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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 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b/.quarto/idx/blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd.json index 3863aff..4f6ffc6 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![](fig/FOSL-interactors.png){}\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![](fig/FOSL-interactors.png){}\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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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 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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-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":"1.3.433","fontsize":"1.1em","theme":["pulse","../../html/styles.scss"],"anchor-sections":true,"fig-cap-location":"margin","footnotes-hover":true,"title":"AP-MS to Study FOSL Related Proteins Interactome","date":"2021-09-16","categories":["publications","T cells"],"image":"fig/FOSL-interactors.png"},"extensions":{"book":{"multiFile":true}}}},"projectFormats":["html"]} \ No newline at end of file +{"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![](fig/FOSL-interactors.png){}\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![](fig/FOSL-interactors.png){}\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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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":"AP-MS to Study FOSL Related Proteins Interactome","date":"2021-09-16","categories":["publications","T 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 fd3464c..0793f4a 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 of contents","toc-title-website":"On this page","related-formats-title":"Other Formats","related-notebooks-title":"Notebooks","source-notebooks-prefix":"Source","section-title-abstract":"Abstract","section-title-appendices":"Appendices","section-title-footnotes":"Footnotes","section-title-references":"References","section-title-reuse":"Reuse","section-title-copyright":"Copyright","section-title-citation":"Citation","appendix-attribution-cite-as":"For attribution, please cite this work as:","appendix-attribution-bibtex":"BibTeX 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","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","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 top","search-no-results-text":"No results","search-matching-documents-text":"matching 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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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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 of contents","toc-title-website":"On this page","related-formats-title":"Other Formats","related-notebooks-title":"Notebooks","source-notebooks-prefix":"Source","section-title-abstract":"Abstract","section-title-appendices":"Appendices","section-title-footnotes":"Footnotes","section-title-references":"References","section-title-reuse":"Reuse","section-title-copyright":"Copyright","section-title-citation":"Citation","appendix-attribution-cite-as":"For attribution, please cite this work as:","appendix-attribution-bibtex":"BibTeX 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","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","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 top","search-no-results-text":"No results","search-matching-documents-text":"matching 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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 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":"Mass Spectrometry Based Serum Proteomics","date":"2020-11-18","categories":["publications","book chapter"]},"extensions":{"book":{"multiFile":true}}}},"projectFormats":["html"]} \ No newline at end of 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 new file mode 100644 index 0000000..6198f60 --- /dev/null +++ b/.quarto/idx/blog/Proteomics-data-analysis-&-visualization/index.qmd.json @@ -0,0 +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 available to process the mass spectrometry data by outlining the above headings.\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 requirs addition of iRT peptides in 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 available to process the mass spectrometry data by outlining the above headings.\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 requirs addition of iRT peptides in 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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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","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","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 top","search-no-results-text":"No results","search-matching-documents-text":"matching 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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 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":"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 5d78c66..c2f5933 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![](fig/roc-plot.png){}\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![](fig/roc-plot.png){}\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 of contents","toc-title-website":"On this page","related-formats-title":"Other Formats","related-notebooks-title":"Notebooks","source-notebooks-prefix":"Source","section-title-abstract":"Abstract","section-title-appendices":"Appendices","section-title-footnotes":"Footnotes","section-title-references":"References","section-title-reuse":"Reuse","section-title-copyright":"Copyright","section-title-citation":"Citation","appendix-attribution-cite-as":"For attribution, please cite this work as:","appendix-attribution-bibtex":"BibTeX 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","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","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 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-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":"1.3.433","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![](fig/roc-plot.png){}\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![](fig/roc-plot.png){}\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 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 0b426f9..1308a93 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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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":"1.3.433","fontsize":"1.1em","theme":["pulse","../../html/styles.scss"],"anchor-sections":true,"fig-cap-location":"margin","footnotes-hover":true,"title":"Serum Proteomics Pre diabetic","date":"2020-10-27","categories":["publications","serum"]},"extensions":{"book":{"multiFile":true}}}},"projectFormats":["html"]} \ No newline at end of file +{"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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articles","listing":{"contents":["Serum-Proteomics-Pre-diabetic/index.qmd","Serum-Proteomics-Atherosclerosis/index.qmd","Mass-Spectrometry-Based-Serum-Proteomics/index.qmd","AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd","Proteomics-data-analysis-&-visualization/index.qmd"],"sort":"date desc","categories":true},"toc-title":"Year","toc-location":"right","date-format":"MMMM D, YYYY","image":"","comments":false},"extensions":{"book":{"multiFile":true}}}},"projectFormats":["html"]} \ No newline at end of file diff --git a/.quarto/idx/cv/index.qmd.json b/.quarto/idx/cv/index.qmd.json index e47c332..8003573 100644 --- a/.quarto/idx/cv/index.qmd.json +++ b/.quarto/idx/cv/index.qmd.json @@ -1 +1 @@ -{"title":"Curriculum 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\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\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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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 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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\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\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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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 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0a0efa5..659ff1a 100644 --- a/.quarto/idx/index.qmd.json +++ b/.quarto/idx/index.qmd.json @@ -1 +1 @@ -{"title":"Understanding the Complexity of Proteome!","markdown":{"yaml":{"about":{"template":"jolla","id":"about-block","image":"img/SDB1.jpg"}},"headingText":"Understanding the Complexity of Proteome!","containsRefs":false,"markdown":"\n\n::: {#about-block}\n:::\n\n\nHi, I'm Santosh D. Bhosale 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![](img/Proteomics.png){}\n\n\n\n","srcMarkdownNoYaml":"\n\n::: {#about-block}\n:::\n\n# Understanding the Complexity of Proteome!\n\nHi, I'm Santosh D. Bhosale 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![](img/Proteomics.png){}\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":"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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Bhosale 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![](img/Proteomics.png){}\n\n\n\n","srcMarkdownNoYaml":"\n\n::: {#about-block}\n:::\n\n# Understanding the Complexity of Proteome!\n\nHi, I'm Santosh D. Bhosale 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![](img/Proteomics.png){}\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":"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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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,"about":{"template":"jolla","id":"about-block","image":"img/SDB1.jpg"}},"extensions":{"book":{"multiFile":true}}}},"projectFormats":["html"]} \ No newline at end of file diff --git a/.quarto/listing/listing-cache.json b/.quarto/listing/listing-cache.json index 81e75e8..e3d6a22 100644 --- a/.quarto/listing/listing-cache.json +++ b/.quarto/listing/listing-cache.json @@ -5,7 +5,7 @@ "Serum-Proteomics-Atherosclerosis/index.qmd", "Mass-Spectrometry-Based-Serum-Proteomics/index.qmd", "AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd", - "ECFCs" + "Proteomics-data-analysis-&-visualization/index.qmd" ] } } \ No newline at end of file diff --git a/.quarto/preview/lock b/.quarto/preview/lock index 1d7984f..f40b1af 100644 --- a/.quarto/preview/lock +++ b/.quarto/preview/lock @@ -1 +1 @@ -1892 \ No newline at end of file +22424 \ No newline at end of file diff --git a/.quarto/xref/06dba913 b/.quarto/xref/06dba913 index 7df77d5..208374b 100644 --- a/.quarto/xref/06dba913 +++ b/.quarto/xref/06dba913 @@ -1 +1 @@ -{"headings":[],"entries":[]} \ No newline at end of file +{"entries":[],"headings":[]} \ No newline at end of file diff --git a/.quarto/xref/0ecb96e6 b/.quarto/xref/0ecb96e6 index 7df77d5..208374b 100644 --- a/.quarto/xref/0ecb96e6 +++ b/.quarto/xref/0ecb96e6 @@ -1 +1 @@ -{"headings":[],"entries":[]} \ No newline at end of file +{"entries":[],"headings":[]} \ No newline at end of file diff --git a/.quarto/xref/52dc296d b/.quarto/xref/52dc296d index 7df77d5..208374b 100644 --- a/.quarto/xref/52dc296d +++ b/.quarto/xref/52dc296d @@ -1 +1 @@ -{"headings":[],"entries":[]} \ No newline at end of file +{"entries":[],"headings":[]} \ No newline at end of file diff --git a/.quarto/xref/62a219a9 b/.quarto/xref/62a219a9 new file mode 100644 index 0000000..208374b --- /dev/null +++ b/.quarto/xref/62a219a9 @@ -0,0 +1 @@ +{"entries":[],"headings":[]} \ No newline at end of file diff --git a/.quarto/xref/INDEX b/.quarto/xref/INDEX index 1c5b12a..d720462 100644 --- a/.quarto/xref/INDEX +++ b/.quarto/xref/INDEX @@ -25,5 +25,8 @@ }, "index.qmd": { "index.html": "4fa06d25" + }, + "blog/Proteomics-data-analysis-&-visualization/index.qmd": { + "index.html": "62a219a9" } } \ No newline at end of file diff --git a/.quarto/xref/b45646c3 b/.quarto/xref/b45646c3 index 7df77d5..208374b 100644 --- a/.quarto/xref/b45646c3 +++ b/.quarto/xref/b45646c3 @@ -1 +1 @@ -{"headings":[],"entries":[]} \ No newline at end of file +{"entries":[],"headings":[]} \ No newline at end of file diff --git a/.quarto/xref/edff53e8 b/.quarto/xref/edff53e8 index 7df77d5..208374b 100644 --- a/.quarto/xref/edff53e8 +++ b/.quarto/xref/edff53e8 @@ -1 +1 @@ -{"headings":[],"entries":[]} \ No newline at end of file +{"entries":[],"headings":[]} \ No newline at end of file diff --git a/blog/Proteomics-data-analysis-&-visualization/index.qmd b/blog/Proteomics-data-analysis-&-visualization/index.qmd new file mode 100644 index 0000000..ffacfb4 --- /dev/null +++ b/blog/Proteomics-data-analysis-&-visualization/index.qmd @@ -0,0 +1,27 @@ +--- +title: "Proteomics data analysis and _visualization_ (no programming skills..its okay..but)" +date: 2023-10-10 +categories: + - data analysis +--- + +Mass 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/). + +Typical 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. + +- Quality control checks. +- Database search and quantitative analysis. +- Statistical analysis +- Functional annotation analysis + +In this blog post, I will highlight the tools available to process the mass spectrometry data by outlining the above headings. + +1. **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. + + Often times to use the functionality of some tools, users needs to convert the proprietary MS files into generic file format such as mzmL + + *DDA analysis* + + - [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. + - [RawBeans](https://bitbucket.org/incpm/prot-qc/downloads/): generates an interactive html report for + - [QuiC ™](https://biognosys.com/software/quic/): Properitary software from Biognosys, supports most of data acquisition mode (SRM, PRM, DIA or DDA) but it requirs addition of iRT peptides in the samples. diff --git a/blog/index.qmd b/blog/index.qmd index 42df01c..2ce305c 100644 --- a/blog/index.qmd +++ b/blog/index.qmd @@ -9,7 +9,7 @@ listing: - "Serum-Proteomics-Atherosclerosis/index.qmd" - "Mass-Spectrometry-Based-Serum-Proteomics/index.qmd" - "AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.qmd" - - "ECFCs" + - "Proteomics-data-analysis-&-visualization/index.qmd" sort: "date desc" categories: true toc-title: Year diff --git a/cv/cv.pdf b/cv/cv.pdf index 2192f2363fac33d58829b3bf9ca3c179102d74d9..b85229377a7368cb11a7d9bed656b46ca4785a85 100644 GIT binary patch delta 25329 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a/docs/about/index.html +++ b/docs/about/index.html @@ -2,7 +2,7 @@ - + diff --git a/docs/blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.html b/docs/blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.html index 9685b70..74741c7 100644 --- a/docs/blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.html +++ b/docs/blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.html @@ -2,7 +2,7 @@ - + diff --git a/docs/blog/Mass-Spectrometry-Based-Serum-Proteomics/index.html b/docs/blog/Mass-Spectrometry-Based-Serum-Proteomics/index.html index afb7b29..225f158 100644 --- a/docs/blog/Mass-Spectrometry-Based-Serum-Proteomics/index.html +++ b/docs/blog/Mass-Spectrometry-Based-Serum-Proteomics/index.html @@ -2,7 +2,7 @@ - + diff --git a/docs/blog/Proteomics-data-analysis-&-visualization/index.html b/docs/blog/Proteomics-data-analysis-&-visualization/index.html new file mode 100644 index 0000000..80da2ed --- /dev/null +++ b/docs/blog/Proteomics-data-analysis-&-visualization/index.html @@ -0,0 +1,421 @@ + + + + + + + + + + +Santosh D. Bhosale - Proteomics data analysis and visualization (no programming skills..its okay..but) + + + + + + + + + + + + + + + + + + + + + + + + + +
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Proteomics data analysis and visualization (no programming skills..its okay..but)

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October 10, 2023

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Mass 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.

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Typical 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.

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  • Quality control checks.
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  • Database search and quantitative analysis.
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  • Statistical analysis
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  • Functional annotation analysis
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In this blog post, I will highlight the tools available to process the mass spectrometry data by outlining the above headings.

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  1. 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.

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    Often times to use the functionality of some tools, users needs to convert the proprietary MS files into generic file format such as mzmL

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    DDA analysis

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    • RawMeat: 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.
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    • RawBeans: generates an interactive html report for
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    • QuiC ™: Properitary software from Biognosys, supports most of data acquisition mode (SRM, PRM, DIA or DDA) but it requirs addition of iRT peptides in the samples.
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Categories
All (5)
T cells (1)
book chapter (1)
data analysis (1)
publications (4)
serum (2)
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+Proteomics data analysis and visualization (no programming skills..its okay..but) +

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+Mass spectrometry based proteomics is the coolest technique to identify and characterize the proteins (including their interaction, alternative splicing… +
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a/docs/index.html +++ b/docs/index.html @@ -2,7 +2,7 @@ - + diff --git a/docs/listings.json b/docs/listings.json index ce1b075..896f64c 100644 --- a/docs/listings.json +++ b/docs/listings.json @@ -2,6 +2,7 @@ { "listing": "/blog/index.html", "items": [ + "/blog/Proteomics-data-analysis-&-visualization/index.html", "/blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.html", "/blog/Mass-Spectrometry-Based-Serum-Proteomics/index.html", "/blog/Serum-Proteomics-Atherosclerosis/index.html", diff --git a/docs/search.json b/docs/search.json index 047e51c..25a7862 100644 --- a/docs/search.json +++ b/docs/search.json @@ -14,18 +14,18 @@ "text": "Type-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 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.\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.\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, 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." }, { - "objectID": "blog/Mass-Spectrometry-Based-Serum-Proteomics/index.html", - "href": "blog/Mass-Spectrometry-Based-Serum-Proteomics/index.html", - "title": "Mass Spectrometry Based Serum Proteomics", + "objectID": "blog/Proteomics-data-analysis-&-visualization/index.html", + "href": "blog/Proteomics-data-analysis-&-visualization/index.html", + "title": "Proteomics data analysis and visualization (no programming skills..its okay..but)", "section": "", - "text": "I 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.\nWe published a protocol entitled Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation. It 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." + "text": "Mass 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.\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\nQuality control checks.\nDatabase search and quantitative analysis.\nStatistical analysis\nFunctional annotation analysis\n\nIn this blog post, I will highlight the tools available to process the mass spectrometry data by outlining the above headings.\n\nQuality 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.\nOften times to use the functionality of some tools, users needs to convert the proprietary MS files into generic file format such as mzmL\nDDA analysis\n\nRawMeat: 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.\nRawBeans: generates an interactive html report for\nQuiC ™: Properitary software from Biognosys, supports most of data acquisition mode (SRM, PRM, DIA or DDA) but it requirs addition of iRT peptides in the samples." }, { - "objectID": "blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.html", - "href": "blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.html", - "title": "AP-MS to Study FOSL Related Proteins Interactome", + "objectID": "blog/index.html", + "href": "blog/index.html", + "title": "Santosh D. Bhosale", "section": "", - "text": "Proteins 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. Most 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).\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. Additionally, suitable negative control replicates are mandatorily included to define the non-specific background. The composition of PPIs are then delineated by mass spectrometry analysis. These 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.\nWe used AP-MS method to study interactome of FOS related proteins (FOSL1 and FOSL2) in human Th17 cells. 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. Of 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.\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 with the matching IgG controls and they were mapped against in house common contaminant detected in related AP-MS experiments.\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 to construct a network using Cytoscape.\n\nThe gene ontology based molecular functional analysis were performed by ClueGO and CluePedia apps built in Cytoscape." + "text": "Welcome to my blog, here, you will find a collection of posts for some of the previously published articles!\n\n\n\n\n\n\n\n\n \n\n\n\n\nProteomics data analysis and visualization (no programming skills..its okay..but)\n\n\n\n\n\n\n\ndata analysis\n\n\n\n\n\n\n\n\n\n\n\nOctober 10, 2023\n\n\n\n\n\n\n \n\n\n\n\nAP-MS to Study FOSL Related Proteins Interactome\n\n\n\n\n\n\n\npublications\n\n\nT cells\n\n\n\n\n\n\n\n\n\n\n\nSeptember 16, 2021\n\n\n\n\n\n\n \n\n\n\n\nMass Spectrometry Based Serum Proteomics\n\n\n\n\n\n\n\npublications\n\n\nbook chapter\n\n\n\n\n\n\n\n\n\n\n\nNovember 18, 2020\n\n\n\n\n\n\n \n\n\n\n\nSerum Proteomics Atherosclerosis\n\n\n\n\n\n\n\npublications\n\n\nserum\n\n\n\n\n\n\n\n\n\n\n\nNovember 11, 2020\n\n\n\n\n\n\n \n\n\n\n\nSerum Proteomics Pre diabetic\n\n\n\n\n\n\n\npublications\n\n\nserum\n\n\n\n\n\n\n\n\n\n\n\nOctober 27, 2020\n\n\n\n\n\n\nNo matching items" }, { "objectID": "about/index.html", @@ -35,11 +35,18 @@ "text": "I am currently employed as an associate biomedical scientist at Precision biomarker laboratories, Cedars-Sinai Medical Center, Los Angeles, CA, USA.\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.\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.\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." }, { - "objectID": "blog/index.html", - "href": "blog/index.html", - "title": "Santosh D. Bhosale", + "objectID": "blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.html", + "href": "blog/AP-MS-to-Study-FOSL-Related-Proteins-Interactome/index.html", + "title": "AP-MS to Study FOSL Related Proteins Interactome", + "section": "", + "text": "Proteins 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. Most 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).\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. Additionally, suitable negative control replicates are mandatorily included to define the non-specific background. The composition of PPIs are then delineated by mass spectrometry analysis. These 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.\nWe used AP-MS method to study interactome of FOS related proteins (FOSL1 and FOSL2) in human Th17 cells. 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. Of 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.\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 with the matching IgG controls and they were mapped against in house common contaminant detected in related AP-MS experiments.\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 to construct a network using Cytoscape.\n\nThe gene ontology based molecular functional analysis were performed by ClueGO and CluePedia apps built in Cytoscape." + }, + { + "objectID": "blog/Mass-Spectrometry-Based-Serum-Proteomics/index.html", + "href": "blog/Mass-Spectrometry-Based-Serum-Proteomics/index.html", + "title": "Mass Spectrometry Based Serum Proteomics", "section": "", - "text": "Welcome to my blog, here, you will find a collection of posts for some of the previously published articles!\n\n\n\n\n\n\n\n\n \n\n\n\n\nAP-MS to Study FOSL Related Proteins Interactome\n\n\n\n\n\n\n\npublications\n\n\nT cells\n\n\n\n\n\n\n\n\n\n\n\nSeptember 16, 2021\n\n\n\n\n\n\n \n\n\n\n\nMass Spectrometry Based Serum Proteomics\n\n\n\n\n\n\n\npublications\n\n\nbook chapter\n\n\n\n\n\n\n\n\n\n\n\nNovember 18, 2020\n\n\n\n\n\n\n \n\n\n\n\nSerum Proteomics Atherosclerosis\n\n\n\n\n\n\n\npublications\n\n\nserum\n\n\n\n\n\n\n\n\n\n\n\nNovember 11, 2020\n\n\n\n\n\n\n \n\n\n\n\nSerum Proteomics Pre diabetic\n\n\n\n\n\n\n\npublications\n\n\nserum\n\n\n\n\n\n\n\n\n\n\n\nOctober 27, 2020\n\n\n\n\n\n\nNo matching items" + "text": "I 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.\nWe published a protocol entitled Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation. It 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." }, { "objectID": "blog/Serum-Proteomics-Atherosclerosis/index.html",