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Genomics

We work to unravel the complexity of the genome, its regulation and its phenotypic consequences.

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About

An immensely complex molecular network of interactions forms the foundation of human biology and disease.

Genomic approaches provide a particularly illuminating window to biological systems, and when combined with advanced analysis, allow us to learn and model this complexity.

The goal of our research is to interpret and distill this complexity through accurate analysis and modeling of molecular pathways, particularly those in which malfunctions lead to the manifestation of disease. We are inventing integrative methods, often based on deep learning and model discovery, for systems-level pathway modeling through integrative analysis of genome-scale datasets.

Publications

Exploring How Workflow Variations in Denaturation-Based Assays Impact Global Protein–Protein Interaction Predictions

Molecular & Cell Proteomics, February 1, 2026

Tavis J. Reed, et al.

An Evidence-Grounded Research Assistant for Functional Genomics and Drug Target Assessment

bioRxiv, December 31, 2025

Ksenia Sokolova, et al.

Age-related nigral downregulation of the Parkinson’s risk factor FAM49B primes human microglia for inflammaging

npj Aging, December 19, 2025

Jacqueline Martin, et al.

People

Portrait photo of Olga Troyanskaya

Olga Troyanskaya

Senior Research Scientist, Center for Computational Biology

A man with short black hair and glasses, wearing a black button-up shirt, stands indoors with a blurred background of lights and windows.

Xi Chen

Research Scientist, Center for Computational Biology

A smiling young woman with long wavy hair wears a red top and a small letter A pendant necklace.

Aviya Litman

Flatiron Research Fellow, Center for Computational Biology

Portrait photo of Christopher Park

Christopher Park

Research Scientist, Center for Computational Biology

A portrait photo of Natalie Sauerwald.

Natalie Sauerwald

Associate Research Scientist, Center for Computational Biology

Portrait photo of Rachel Sealfon

Rachel Sealfon

Research Scientist, Center for Computational Biology

Portrait photo of Claudia Skok Gibbs

Claudia Skok Gibbs

Flatiron Research Fellow, Center for Computational Biology

Science in Action

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Finding Signals in the Noise with HumanBase

Using machine-learning algorithms developed by scientists on the Genomics research team, HumanBase is an interactive software system that digs through data from 61,400 experiments from 24,930 publications to make predictions about how genes in specific tissues in the body are turned on, what they do and how they interact with each other. The results allow researchers to explore the genetic underpinnings of how diseases emerge and manifest differently from one tissue to the next. Continue

Latest Research

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Physics-Informed AI Method Could Help Make CRISPR Safer

Colorful illustration of a blue RNA strand with highlighted pink and yellow sections, surrounded by clusters of tangled, ribbon-like protein structures against a deep blue background.

New Testing Approach Diagnoses COVID-19 With Near-Perfect Accuracy

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Big Data and Powerful Computing Join Forces To Link DNA Sequence and Function

Software & Datasets

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DeepSEA

Software
Uses deep learning to predict chromatin effects of DNA variants, including TF binding, DNase sensitivity, and histone marks across cell types.
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FNTM

Software
Uses tissue-specific gold standards to build functional gene networks, prioritizing relevant data and enabling cross-tissue gene interaction comparisons.

GIANT

Software
Builds tissue-specific gene networks, enables cross-tissue functional comparison, and reprioritizes GWAS signals to identify disease-associated genes.
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humanbase

Software
Uses machine learning on large genomic datasets to discover new biological gene associations beyond existing literature knowledge.

Flatiron Community

Genomics Meeting

Our informal weekly meetings provide open spaces for researchers to discuss findings and explore new ideas.

Learn More