stasaki / DEcode

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 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.

• GTEx transcriptome data - GTEx portal
• Transcription factor binding peaks - GTRD
• RNA binding protein binding peaks - POSTAR2
• miRNA binding locations - TargetScan