Gabriele Schweikert

Tangerine provides a Python framework for analyzing dynamic gene regulation from transcriptomic time series by reconstructing and visualizing time-varying gene regulatory networks.

This paper introduces DecoDen, a method that learns shared chromatin landscapes and de-noises histone modification assays across multiple tissues.

The paper introduces eDICE, a deep learning model for imputing epigenomic tracks, showing improved performance on reference data and successful application to individual-specific epigenomic patterns.

The paper evaluates 23 imputation models for cross-cell type epigenomic data, finding quality assessment challenging due to distributional shifts, data availability, and redundant metrics.

The paper develops a digital Hi-C assay using a transformer-based algorithm to predict long-distance genomic interactions from limited sequence data.

DecoDen removes shared biases in multi-histone ChIP-Seq data by learning a joint background signal and correcting for it using non-negative matrix factorisation and half-sibling regression.

The paper shows MeCP2 binds to mCG and mCAC sequences in mouse brain, with binding density correlating with transcriptional regulation of neuronal genes.