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https://github.com/kull-centre/_2025_voutsinos_degron_cytosol

A complete map of human cytosolic degrons and characterization of their exposure and relevance for disease

https://github.com/kull-centre/_2025_voutsinos_degron_cytosol

Science Score: 57.0%

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    CITATION.cff file
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    DOI references
    Found 7 DOI reference(s) in README
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    Links to: ncbi.nlm.nih.gov, zenodo.org
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    Organization kull-centre has institutional domain (www1.bio.ku.dk)
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    Low similarity (5.9%) to scientific vocabulary
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Repository

A complete map of human cytosolic degrons and characterization of their exposure and relevance for disease

Basic Info
  • Host: GitHub
  • Owner: KULL-Centre
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 98.4 MB
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  • Watchers: 1
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  • Open Issues: 0
  • Releases: 0
Created about 1 year ago · Last pushed 6 months ago
Metadata Files
Readme License

README.md

2025Voutsinosdegroncytosol

Scripts and output from "A complete map of human cytosolic degrons and characterization of their exposure and relevance for disease" by Vasileios Voutsinos, Kristoffer E. Johansson, Fia B. Larsen, Martin Grønbæk-Thygesen, Nicolas Jonsson, Emma Holm-Olesen, Giulio Tesei, Amelie Stein, Douglas M. Fowler, Kresten Lindorff-Larsen and Rasmus Hartmann-Petersen.

Preprint

Contents

  • library: Script and input files for makeing the DNA libraries
  • counts: Scripts for processing FASTQ files. Output counts and raw FASTQ files are available on ERDA
  • score: Scripts for calculating scores. FACS data files are available on ERDA
  • models: Scripts for training models
  • pathogenic: Scripts for analysing pathogenic variants from ClinVar
  • proteome: Scripts for building the "cytosolic proteome" and structural analysis
  • plots: Scripts for plotting

Supplementary data, sequencing counts, FASTQ files and FACS data are available on ERDA, DOI: 10.17894/ucph.e1cbb4ae-6966-4c0f-95a1-c81af94cfdaf

FASTQ files are also deposited at NCBI SRA under BioProject ID: PRJNA1277273

Peptide abundance predictor (PAP)

Predictive models described in the paper are available in the script models/PAP.py. The neural network model requires a weight file compressed in models/pap_weights.tgz and depends on Keras2 available in tensorflow version 2.14.

PAP webserver

The file PapLab.ipynb is made to run as a webservice at Google colab available at https://colab.research.google.com/github/KULL-Centre/2025Voutsinosdegroncytosol/blob/main/PapLab.ipynb

This requires a login for Googles services, e.g. a gmail.

DOI

Owner

  • Name: Linderstrøm-Lang Centre for Protein Science, University of Copenhagen
  • Login: KULL-Centre
  • Kind: organization
  • Location: Copenhagen, Denmark

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