Epigenomics and Forest Genomics: Expanding the Toolbox for Predictive Modeling

This week, I'm taking part in the Epigenomics Data Analysis Workshop (Physalia courses, April 14–18, 2025), led by Dr. Jacques Serizay from the Institut Pasteur. Held online to encourage international participation, the course dives deep into the analysis of epigenomic data—including RNA-seq, ATAC-seq, MNase-seq, ChIP-seq, and Hi-C—and its integration using R/Bioconductor tools and command-line workflows.

As part of my Momentum-CSIC postdoctoral project, I'm developing genomic prediction models that aim to incorporate multi-omic data and artificial intelligence to predict complex phenotypes in forest species—traits such as disease resistance, drought tolerance, and productivity. These traits are key for breeding and conservation programs in the face of global change.

This workshop is being an excellent opportunity to build the technical foundation for the integration of epigenomic layers into predictive models. Understanding chromatin accessibility, histone modifications, and 3D genome organization will allow us to move beyond DNA sequence variation and incorporate regulatory information into genomic models, improving their power and biological relevance.

So far, we’ve covered:

• RNA-seq and gene expression analysis

• Chromatin accessibility profiling (ATAC-seq, MNase-seq)

• ChIP-seq for TF binding and histone marks

• Hi-C and 3D genome architecture

• And soon: multi-omics integration strategies

By bridging quantitative genomics with regulatory epigenomics, this training brings me one step closer to my goal: building predictive models that are not only statistically robust but also biologically informed, helping us make better decisions in forest tree breeding and conservation genomics

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Looking forward to applying these tools in the context of forest resilience and contributing to the sustainable management of genetic resources through AI-enhanced, omics-integrated modeling.