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Related Experiment Video

Updated: Feb 3, 2026

ATAC-Seq Optimization for Cancer Epigenetics Research

ATAC-Seq Optimization for Cancer Epigenetics Research

Published on: June 30, 2022

A neural network based model effectively predicts enhancers from clinical ATAC-seq samples.

Asa Thibodeau¹, Asli Uyar¹, Shubham Khetan^1,2

¹The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.

Scientific Reports

|October 31, 2018

Summary

Predicting Enhancers from ATAC-Seq data (PEAS) is a new tool that identifies enhancers from clinical epigenomes. PEAS accurately predicts enhancers across cell types and reveals individual-specific enhancer activity, aiding disease research.

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Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

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Area of Science:

Genomics
Epigenetics
Computational Biology

Background:

Enhancers are crucial cis-acting regulatory elements controlling gene transcription in a cell-specific manner.
Disease-associated variants are often found in enhancers, highlighting their importance in complex diseases.
Assay for Transposase Accessible Chromatin (ATAC-seq) enables enhancer studies from limited clinical samples, but accurately identifying enhancers remains a challenge.

Purpose of the Study:

To develop a computational model for accurate enhancer prediction from ATAC-seq data, particularly in clinical settings.
To integrate ATAC-seq features with sequence-based information for improved enhancer identification.
To assess the model's performance across diverse cell types and its ability to detect individual-specific enhancer variations.

Main Methods:

Developed a neural network model, Predicting Enhancers from ATAC-Seq data (PEAS), utilizing ATAC-seq features and sequence characteristics (e.g., GC ratio).
Trained and validated PEAS on ATAC-seq data from multiple cell types, including monocytes, T cells, and pancreatic islets.
Applied PEAS to clinical ATAC-seq samples to infer individual-specific enhancers and analyze activity variations.

Main Results:

PEAS successfully recapitulated known enhancers defined by ChromHMM across various cell types.
Models trained on specific cell types demonstrated strong predictive power for enhancers in unseen cell types.
PEAS identified individual-specific enhancers from clinical samples, revealing genetic influences on enhancer activity.

Conclusions:

PEAS is an effective tool for inferring enhancers from ATAC-seq data, offering high accuracy across diverse cell types.
The model facilitates the study of enhancers in disease contexts by leveraging clinical epigenomic data.
PEAS enables the discovery of individual-specific enhancer variations, providing insights into genetic contributions to disease susceptibility.