DeepFun

DeepFun is a deep learning based model for functional evaluation of genetic variants with single-base resolution. Specifically, we collected comprehensive chromatin profiles from ENCODE and Roadmap projects to construct the feature space, including 1548 DNase I accessibility, 1536 histone mark, and 4795 transcription factor binding profiles, and these profiles covered 225 tissues or cell lines. With such comprehensive epi-genomics annotations and pre-trained model, this web-server provides an online service to quickly assess the impact of genetic variants in tissue- or cell type-specific manner..

Screen analysis

Compute SNP activity (accessibility or binding probability) difference (SAD) or relative log fold changes of odds (log-odds) difference between two alleles for a list of query variants across all chromatin features.

In silico saturated mutagenesis analysis

Perform an in silico saturated mutagenesis analysis of the regions surrounding 200 bp of one query variant under target feature.



Developers:

Ruifeng Hu, Guangsheng Pei, Peilin Jia, Zhongming Zhao @ CPH, UTHealth-Houston SBMI.

DeepFun is free and open to all users and there is no login requirement.