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olink

Analysis of OLINK proteomic data to identify proteins that may be associated with brain-derived extracellular vesicles.

https://github.com/walt-lab/olink

Science Score: 52.0%

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Repository

Analysis of OLINK proteomic data to identify proteins that may be associated with brain-derived extracellular vesicles.

Basic Info
  • Host: GitHub
  • Owner: Walt-Lab
  • License: gpl-3.0
  • Language: Roff
  • Default Branch: main
  • Homepage:
  • Size: 242 MB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 3
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

OLINK Proteomic Analysis to Identify Potential Extracellular Vesicle-Associated Proteins

Analysis of OLINK proteomic data to identify proteins that may be associated with brain-derived extracellular vesicles.

Key Features

  • A dataset containing information concenrning 5416 unique proteins, collected via the OLINK HT panel using frationated human cerebrospinal fluid.
  • Read OLINK parquet files and identify proteins that may be associated with extracellular vesicles using relative protein abundances in fractionated human cerebrospinal fluid.
  • Overlay lists of proteins that may be associated with extracellular vesicles with single-cell RNA sequencing data and subcellular localization analysis to determine if a particular protein could be a potential cell-type specific immunocapture or validation target.

Modules

config.py

  • Contains several global variables.

rawdatapreprocessing.py

  • Converts the raw parquet file produced by OLINK into a tidy dataframe.
  • Generates graphs to display the median fractionation pattern of a protein of interest.
  • Calculates the EV Association Score of a protein of interest.

Required Packages - matplotlib.axes - matplotlib.pyplot - pandas

Required Documentation - config.py

olink_fractionation.py

  • Uses fractionation patterns reported by Olink to identify proteins that may be associated with extracellular vesicles.

Required Packages - pandas

specificity_functions.py

  • Calculates various statistical measures of specificity, including tau score, tissue specificity index, gini coefficient, Shannon entropy, specificity measure, and zscore.

Required Packages - numpy - pandas - scipy

brainrnaseq_specificity.py

  • Uses data collected and made available by BrainRNA-Seq to determine proteins that are specific to a cell type of interest.

Required Packages - requests - numpy - pandas - io - pathlib

Required Documentation - config - specificity_functions

gtex_specificity.py

  • Uses data made available by GTEx to determine proteins that are specific to the brain.

Required Packages - pandas

Required Documentation - specificity_functions - config

deeptmhmm_localization.py

  • Uses the DeepTMHMM deep learning model to identify the most likely subcellular localization of proteins of interest.

Required Packages - biolib - gzip - os - pathlib - pandas

identify_targets.py

  • Uses specified fractionation, localization, and cell-type specificity criteria to identify protein targets.

Required Packages - pandas - typing - pathlib

Required Documentation - rawdatapreprocessing.py - olinkfractionation.py - brainrnaseqspecificity.py - deeptmhmm_localization.py - config.py

Owner

  • Name: Walt Lab
  • Login: Walt-Lab
  • Kind: organization

@ Wyss/BWH

Citation (CITATION.cff) 

Norman, M., Shami-shah, A., D'Amaddio, S.C., Travis, B.G., Ter-Ovanesyan, D., Dougan, T.J. and Walt, D.R. (2025), 
Toward Identification of Markers for Brain-Derived Extracellular Vesicles in Cerebrospinal Fluid: 
A Large-Scale, Unbiased Analysis Using Proximity Extension Assays. 
J Extracell Vesicles., 14: e70052. https://doi.org/10.1002/jev2.70052

GitHub Events

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  • Avg Commits per committer: 12.0
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Dependencies

pyproject.toml pypi