Workshop: Beginning with Multi-Omics Data Integration

Wednesday June 3rd 2026 | 2.10pm - 3.40pm | Room 51

Discover how to connect diverse omics layers and reveal the systems-level biology hidden within complex datasets.

This intensive workshop provides a practical overview of regulatory genomics analysis across two complementary sessions.

Participants will work hands-on with computational approaches for integrating paired single-cell multi-omics data to link chromatin accessibility to gene expression, and for prioritizing noncoding variants by tracing their regulatory impact through eRNA to target genes.

Emphasis is placed on applying AI-based sequence tools for biological interpretation, building reproducible workflows, and understanding how analytical choices affect downstream conclusions. 

PharmAI roundtable

Moderators

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Speaker profile image for Neva Cherniavsky Durand

Neva Cherniavsky Durand

Senior Scientist, The Broad Institute of MIT and Harvard
Speaker profile image for Natalia Benova

Natalia Benova

PhD Candidate, Leeds Beckett University

    What to expect

    • Gain hands-on experience integrating paired single-cell multi-omics data to connect chromatin accessibility with gene expression and uncover regulatory relationships
    • Learn how to prioritise noncoding variants by tracing their impact from eRNA through to target genes using practical computational approaches
    • Apply AI-based sequence tools and build reproducible workflows while understanding how analytical decisions influence biological interpretation and downstream results.

    Who should attend

    This workshop is aimed at post-docs, researchers, clinicians, and other professionals with an interest in understanding how omics data can be integrated in clinical and non-clinical research. 

    Requirements

    • Introductory background in statistics and data science 

    • Familiarity with R 

    • Personal laptop and a free, basic RStudio account 

    What the workshop will focus on

    Session 1: Hands-On Multi-Omics: Integrating scRNA-seq and scATAC-seq with Deep Learning for Gene Regulatory Discovery 

    • Integrate and analyze paired scRNA-seq and scATAC-seq datasets using modern computational approaches to identify cell types and link chromatin accessibility to gene expression. 
    • Apply deep learning methods for TF footprinting and sequence attribution to predict transcription factor binding sites and decode regulatory grammar from chromatin accessibility.
    • Build reproducible analysis workflows that connect multi-omic measurements to mechanistic gene regulatory insights. 

    Session 2: Functional Prioritization of Noncoding Enhancer Variants Using eRNA Regulatory Output and AI-Based Validation. How do we move from a noncoding variant to a biologically and clinically meaningful interpretation? 

    • Construct reproducible analysis workflows that bridge noncoding variants with post-transcriptional regulation to support mechanistic interpretation of disease-associated variants. 
    • Quantify how variant-associated changes in eRNA propagate to target gene expression using the TranCi module within eRNAkit at cohort and single-patient resolution. 
    • Validate predicted regulatory impact by modelling upstream transcription factor (TF) binding disruption and chromatin effects using AI-based sequence tools (DeepSEA) and regulatory annotation platforms (RegulomeDB). 

    Register your interest

    Please be aware that there are only a limited amount of places available. Attendance is limited to approved Festival attendees only. Please select this workshop when registering for your place.

    All registrations will be reviewed by the organising committee and approved participants will receive confirmation and event details.

    Please contact info@frontlinegenomics.com if you have any questions.

    Register here (opens in a new tab)