STITCHIT

STITCHIT is a software for learning gene-specific regulatory elements from paired epigenomics and expression data.

Authors
Marcel H. Schulz, PhD
(he/him)
Professor for Computational Biology
I am a Computational Biologist by education. I develop computational
systems biology models and machine learning algorithms that further
the understanding of gene regulation. We work on groundbreaking computational methods
that enable novel approaches for personalized medicine applications in the
context of different human diseases. We live and breathe open-source development.

Authors
Nina Baumgarten
(she/her)
Postdoc
I am a bioinformatician with a diverse background in analyzing genetic and epigenetic data. I have had the chance to work on a variety of projects since I joined the lab, like decoding the effect of non-coding variations, analysing GWAS data, and studying TF binding. Recently, my focus has been shifted toward spatial transcriptomics, which allows me to map genetic findings directly onto tissue architecture. I am motivated by the possibility that these data-driven insights might lead to a better understanding of complex diseases. In my free time, I love spending time in nature for hiking—it’s where the best ideas come to me!