The purpose of this repository is to provide the scripts that are used to produce the figures related to the analysis of disease-causing mutations affecting short linear motif content in transmembrane proteins and also the analysis of gain/loss of short linear motif mediated protein interactions based on the peptide array analysis as documented in the paper Meyer et al, Cell, 2018.

See paper: https://www.sciencedirect.com/science/article/pii/S0092867418310353

Vignettes:

motif_gains_transmembrane_proteins

The Rmarkdown scripts in this folder can be run to reproduce figures 7B, 7C, S6B, and S6C from the paper.

Requirements

The vignettes are implemented in R and makes use of various CRAN and Bioconductor packages. It also depends on the disorder prediction tool IUPred.

slimR package

install.packages('devtools') devtools::install_github('BIMSBbioinfo/slimR')

CRAN packages

install.packages(c('BiocManager', 'rmarkdown', 'knitr', 'data.table', 'ggplot2', 'ggrepel', 'pbapply', 'stringi'))

Bioconductor packages

BiocManager::install(c('Biostrings', 'biomaRt', 'rtracklayer', 'GenomicRanges'))

IUPred Disorder Predictor

IUPred source code can be dowloaded from here: http://iupred.enzim.hu/Downloads.php . After unpacking the source code, cd to the src directory. Compile the code with "cc iupred.c -o iupred"

motif_gains_TM_proteins.humsavar.Rmd

This vignette shows how to reproduce Figures 7B and 7C from Meyer et al.

To render this vignette type:

Rscript ./vignettes/motif_gains_transmembrane_proteins/render.vignette.R \ ./vignettes/motif_gains_transmembrane_proteins/motif_gains_TM_proteins.humsavar.Rmd \ ./data

motif_gains_TM_proteins.clinvar.Rmd

This vignette shows how to reproduce Figures S6B and S6C from Meyer et al.

To render this vignette type:

Rscript ./vignettes/motif_gains_transmembrane_proteins/render.vignette.R \ ./vignettes/motif_gains_transmembrane_proteins/motif_gains_TM_proteins.clinvar.Rmd \ ./data

peptideArrayAnalysis

The scripts in this folder reproduce the analysis of peptide array pull-down experiment analysis results, in particular Figure S2B and Data S1.

Required R packages

The required R packages can be installed via:

slimR package from github:

install.packages('devtools')

devtools::install_github('BIMSBbioinfo/slimR')

packages from CRAN:

install.packages(c('cowplot', 'data.table', 'DT', 'ggplot2', 'ggnetwork', 'intergraph', 'ggsignif', 'rmarkdown', 'DT'))

Description

The script preprocess_peptideArray_table.R preprocesses the peptide pull-down results table, which is at ./data/20170522_Neuroarray_results.tsv.

The second script findSLiMDomainPairs.R looks for motif gains/losses in mutant peptides with respect to the wild-type peptides and associates theses changes to PFAM domains in the detected proteins as gained/lost interaction partners of the peptides.

The rmarkdown script peptideArray_manuscript_figures.Rmd reproduces the figure S2B and supplementary data file Data S1 from the paper.

The R script render.vignette.R is used to run the Rmarkdown script.

Step by step: how to run the peptide array analysis

Assuming the current directory as the top-level source directory.

1. Preprocess the peptide pull-down result table

Rscript vignettes/peptideArrayAnalysis/preprocess_peptideArray_table.R ./data

2. Associate slims to PFAM domains

Rscript vignettes/peptideArrayAnalysis/findSLiMDomainPairs.R ./data

3. Render the manuscript figures/tables

Rscript ./vignettes/peptideArrayAnalysis/render.vignette.R \ ./vignettes/peptideArrayAnalysis/peptideArray_manuscript_figures.Rmd \ ./data

The output is a pdf file named network_data.clustering_goterms.pdf and an html file named peptideArray_manuscript_figures.html.