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A prediction model for differential gene expression (DE) based on genome-wide regulatory interactions
Jupyter Notebook Python R
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Welcome to DEcode!

The goal of this project is to enable you to utilize genomic big data in identifying regulatory mechanisms for differential expression (DE).

DEcode predicts inter-tissue variations and inter-person variations in gene expression levels from TF-promoter interactions, RNABP-mRNA interactions, and miRNA-mRNA interactions.

You can read more about this method in this paper where we conducted a series of evaluation and applications by predicting transcript usage, drivers of aging DE, gene coexpression relationships on a genome-wide scale, and frequent DE in diverse conditions.

Quick start

This tutorial shows you a way to run DEcode on Google Colab that provides you free access to a ready-to-use machine learning environment with a high-end GPU.

  1. Go to Google Colab and sign in to your Google account.
  2. Open Jupyter notebook.
    • Menu -> File -> Open notebook -> GITHUB tab
    • Search
    • Select Run_DEcode.ipynb
  3. Run each block of code.


GTRD - Yevshin, I., Sharipov, R., Kolmykov, S., Kondrakhin, Y. & Kolpakov, F. GTRD: a database on gene transcription regulation—2019 update. Nucleic Acids Res 47, D100-D105 (2019).

POSTAR2 - Zhu, Y. et al. POSTAR2: deciphering the post-transcriptional regulatory logics. Nucleic acids research 47, D203-D211 (2019).

TargetScan - Agarwal, V., Bell, G. W., Nam, J. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. eLife 4 (2015).

DEcode - Tasaki, S., Gaiteri, C., Mostafavi, S. & Wang, Y. Decoding differential gene expression. bioRxiv 2020.01.10.894238; doi:

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