ArticleMethods in molecular biology (Clifton, N.J.)2025
Learning Enhancer-Gene associations from Bulk Transcriptomic and Epigenetic Sequencing Data with STITCHIT.
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Comparing machine learning methods predicting transcriptome from epigenome with applications to association studies.Genome biology · 2026Article
- Effect of the TLR9 signaling pathway on acyclovir infection with herpes simplex virus type 2 in HaCaT cells.Frontiers in microbiology · 2025Article
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2 authors.
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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).
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