Florian Schmidt

TF-Prioritizer identifies condition-specific transcription factors using multi-modal genomic data and generates an interactive web report.

DUBStepR improves single-cell clustering by selecting features using gene-gene correlations and a Density Index, outperforming existing methods and detecting rare cell populations.

This paper introduces a new method to identify gene-specific regulatory elements by integrating epigenomics data and demonstrates its application in prioritizing doxorubicin-resistance genes.

scConsensus combines supervised and unsupervised clustering to improve cell type identification in single-cell RNA sequencing data by integrating results and refining clusters with differentially expressed genes.

The paper introduces a computational pipeline to interpret CRISPR mutations in non-coding regions using epigenomic data, revealing novel regulatory elements involved in chemoresistance.

The paper introduces RCA2, a supervised clustering method for scRNA-seq data that is robust to batch effects and supports large-scale analysis.

The paper introduces a TF motif analysis toolbox that integrates DNA methylation and intra-motif dependencies, finding that CpG methylation generally reduces TF binding likelihood.

The paper improves gene expression prediction by integrating chromatin conformation data and identifies key transcription factors involved in enhancer-promoter loops.

The paper compares DNase I-Seq, ATAC-Seq, and NOMe-Seq to identify assay-specific features and their impact on chromatin accessibility and gene expression prediction.

The paper identifies AHR and GLIS1 as key regulators of mesenchymal multipotency using temporal epigenomic and transcriptomic data from mouse bone marrow stromal cells.

This paper introduces TEPIC, a method that combines open-chromatin data with TF binding affinities to predict gene expression, achieving results comparable to TF ChIP-seq.