https://github.com/cmkobel/holodoublevu
🪢👀 Generalization and parametrization of the supacow. Phase 1: Proteomics.
Science Score: 49.0%
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Repository
🪢👀 Generalization and parametrization of the supacow. Phase 1: Proteomics.
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Metadata Files
README.md
holodoublevu 🪢👀
"HOlistic LOw-level Denoising & OUtputting for BLEnded multi-View Unification" - (Sorry that backronym was made with chatgpt. But Holodoublevu is mine!)
I'm a pipeline kinda guy.
This is a snakemake workflow that contains (nearly) all steps to perform the proteomics network analysis and interpretation.
Hopefully this workflow is very flexible. But not so much that it false apart when being held up against the sun.
IO
Inputs
- Sample metadata. (key
animal) - Proteome amino acid sequences. (key
protein) - Proteome intensities. (key
protein) - Proteome-to-genome mapping. (key
protein)
Outputs
- Filtered and transformed intensities. (key
protein) - Imputed intensities. (key
protein) - Function/intensity main overview table. (key
protein) - Sample pathway enrichment. (key
samplexpathway) - Sample species overview. (key
samplexpathway) - Ordination plots (PCA, NMDS, UMAP).
- Network analysis (WGCNA). (key
module eigengene) - WGCNA module pathway enrichment. (key
module eigengenexpathway) - Other?
Roadmap
Busy!
Run
Have conda/mamba. And set the principal inputs in config/config.yaml. Then do:
bash
mamba env create -f environment.yaml
conda activate holodoublevu
snakemake
Owner
- Name: Carl Mathias Kobel
- Login: cmkobel
- Kind: user
- Company: Norges Miljø- & Biovitenskapelige Universitet
- Website: https://orcid.org/0000-0002-4461-1159
- Repositories: 49
- Profile: https://github.com/cmkobel
PhD-fellow in the MEMO group at NMBU.
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Last Year
- Release event: 1
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