Learning Enhancer-Gene associations from Bulk Transcriptomic and Epigenetic Sequencing Data with STITCHIT
1 January 2025·
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0 min read
Laura Rumpf
Marcel H. Schulz
Abstract
To reveal gene regulation mechanisms, it is essential to understand the role of regulatory elements, which are possibly distant from gene promoters. Integrative analysis of epigenetic and transcriptomic data can be used to gain insights into gene-expression regulation in specific phenotypes. Here, we discuss STITCHIT, an approach to dissect epigenetic variation in a gene-specific manner across many samples for the identification of regulatory elements without relying on peak calling algorithms. The obtained genomic regions are then further refined using a regularized linear model approach, which can also be used to predict gene expression. We illustrate the use of STITCHIT using H3k27ac ChIP-seq and RNA-seq data from the International Human Epigenome Consortium (IHEC).
Type
Publication
Methods in Molecular Biology (Clifton, N.J.)
Authors
Laura Rumpf
(she/her)
PhD student
I’m a computer scientist by training who found my way into bioinformatics during my master’s. My work focuses on understanding enhancer–gene regulation using machine learning and single-cell transcriptomic and epigenomic data. I’m particularly interested in how computational models can help uncover regulatory mechanisms and translate these insights into disease-related questions in computational medicine. Outside the lab, I’m usually fueled by coffee and a slightly too serious interest in latte art.