Konstantin Krismer

This study uses proximity-labeling proteomics to analyze how SHP2 mutations alter its interactome and localization, revealing functional consequences of different mutation types.

Positional scanning peptide arrays profile substrate sequence specificities across 303 human serine/threonine kinases, enabling computational kinase-substrate annotation and signaling pathway inference across the human phosphoproteome.

seqgra is a deep learning pipeline that generates data based on hypothesized regulatory models and identifies neural networks that recover these rules, analyzing performance under varying conditions.

The paper introduces spatzie, an R package and website for detecting significant transcription factor motif co-enrichment in enhancer-promoter interactions using ChIA-PET data.

The paper introduces MIAA, a method to measure DNA accessibility changes caused by synthetic oligonucleotide motifs, and identifies key transcription factor motifs influencing chromatin accessibility in stem cells and endoderm.

Transite identifies RNA-binding proteins modulating gene expression changes by analyzing RNA stability and degradation, validated with hnRNPC in chemotherapeutic resistance.

IDR2D identifies reproducible genomic interactions by filtering spurious ones from chromatin interaction data.

The paper introduces CID, a computational method for chromatin interaction discovery that improves sensitivity and consistency compared to existing methods.