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 (full text is available at https://rdcu.be/b5r3p) 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.

Run DEcode on Google Colab

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 https://github.com/stasaki/DEcode
    • Select Run_DEcode_toy.ipynb
  3. Run each block of code.

Gene level features for all genes are available at data/GTEx53_gene/DEcode_data.tar.gz. However, due to the memory limitation in Google Colab, you cannot train a model with all genes. Please consider setting up Keras with GPU in your environment or use Code Ocean platform.

Run DEcode on Code Ocean

You can run DEcode on Code Ocean platform without setting up a computational environment. Our Code Ocean capsule provides reproducible workflows, all processed data, and pre-trained models for tissue- and person-specific transcriptomes and DEprior, at gene- or transcript level.

If you find DEcode useful in your work, please cite our manuscript.

Tasaki, S., Gaiteri, C., Mostafavi, S. & Wang, Y. Deep learning decodes the principles of differential gene expression. Nature Machine Intelligence (2020) [link to paper] (full text is available at https://rdcu.be/b5r3p)

Source databases for traning data.