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VESPA (Virtual Enrichment-based Signaling Protein-activity Analysis) signaling network inference module

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vespa.net - Signaling reconstruction module for the VESPA R-package

1. Installation

This module requires the Snakemake workflow manager and either Singularity (Linux) or local versions of the vespa R-package and vespa.aracne (Linux/macOS/WSL) to be installed. It has only been tested on a CentOS 7 cluster, but it might work on other systems too.

Download repository

Clone the repository to your working directory.

git clone [email protected]:califano-lab/vespa.net.git

Singularity

If you are using Singularity on a HPC environment, Singularity images for vespa and vespa.aracne will be downloaded and generated automatically.

Local installation

Please install the vespa R-package and vespa.aracne separately. Make sure that the vespa.aracne JAR file is exported to the Java classpath:

export CLASSPATH=$CLASSPATH:REPLACE_WITH_PATH_TO_CODE/vespa.aracne/dist/aracne.jar

java aracne.Aracne # this should display the help text of vespa.aracne if everything works.

2. Using vespa.net

Proteomic data

Add one or several RDS phosphopeptide abundance files (e.g. CPTAC_S045_COAD_phospho.rds) with different sample tags (e.g. CPTAC_S045_COAD) to the root directory. Add corresponding whole proteome files (CPTAC_S045_COAD_proteo.rds) with paired tags. Note, that they only differ by file ending (_phospho.rds / _proteo.rds). If no whole proteome files are available, make a duplicate copy of the _phospho.rds file and rename it to _proteo.rds, the algorithm will then skip this step.

Proteomic reference data for optimization

The vespa.net module optimizes signalons using the metaVIPER algorithm for a target phosphoproteome. A single RDS phosphopeptide abundance file (i.e. reference.rds) needs to be added to the root directory. This file can be identical to one of the learning RDS phosphopeptide abundance files (e.g. CPTAC_S045_COAD_phospho.rds).

FASTA library

Place the FASTA library (without decoys) used for the MS analysis in file library.fasta in the root directory.

3. Run analysis

Submit the Snakemake job. On a local computer with local installation, this could for example be:

snakemake --snakefile Snakefile -j 64 --restart-times 2

In a HPC environment with SLURM, the following command might be adapted:

sbatch --qos=1day --time=1-00:00:00 --mem-per-cpu=8192 snakemake --snakefile Snakefile --use-singularity -j 64 --restart-times 2 --cluster-config envs/res.json --cluster "sbatch --ntasks {cluster.nCPUs} --mem-per-cpu {cluster.memory} --qos=6hours --time=6:00:0"

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VESPA (Virtual Enrichment-based Signaling Protein-activity Analysis) signaling network inference module

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