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Related Experiment Video

Updated: Sep 5, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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A sequence-based global map of regulatory activity for deciphering human genetics.

Kathleen M Chen^1,2, Aaron K Wong², Olga G Troyanskaya^3,4,5

¹Department of Computer Science, Princeton University, Princeton, NJ, USA.

Nature Genetics

|July 11, 2022

Summary

Sei integrates human genetics and sequence data to map regulatory elements, uncovering the genetic basis of diseases and traits. This framework classifies sequence variants by their regulatory activities across diverse cell types.

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Last Updated: Sep 5, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

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Published on: May 31, 2011

Area of Science:

Genomics
Computational Biology
Human Genetics

Background:

Epigenomic profiling identifies regulatory elements but lacks systematic mapping to sequence/variant functions.
Understanding the regulatory basis of traits and diseases remains a significant challenge in human genetics.

Purpose of the Study:

To develop a framework (Sei) for integrating human genetics and sequence data.
To systematically map regulatory activities of genomic sequences and variants.
To discover the regulatory basis of complex traits and diseases.

Main Methods:

Utilized deep learning to predict 21,907 chromatin profiles across >1,300 cell lines and tissues.
Developed a vocabulary of regulatory activities termed 'sequence classes'.
Integrated human genetics data with sequence information.

Main Results:

Sei provides a global classification and quantification of sequence and variant effects.
Identified cell type-specific enhancer functions and their regulatory activities.
Predictions are supported by tissue-specific expression, expression quantitative trait loci (eQTLs), and evolutionary constraint data.

Conclusions:

Sei elucidates the tissue-specific regulatory architecture of complex traits.
Enables generation of mechanistic hypotheses for pathogenic mutations.
Offers a valuable resource for understanding the regulatory basis of human health and disease.