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.
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.
- Go to Google Colab and sign in to your Google account.
- Open Jupyter notebook.
- Menu -> File -> Open notebook -> GITHUB tab
- 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: https://doi.org/10.1101/2020.01.10.894238