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General Transcription Factors

General Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Hypergraph factorization for multi-tissue gene expression imputation.

Ramon Viñas¹, Chaitanya K Joshi¹, Dobrik Georgiev¹

¹Department of Computer Science and Technology, University of Cambridge.

Nature Machine Intelligence

|September 29, 2023

Summary

This summary is machine-generated.

We developed HYFA (Hypergraph Factorisation), a novel method for integrating gene expression across multiple tissues and cell types. HYFA accurately imputes missing data, enhancing the discovery of genetic variations influencing gene regulation.

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Area of Science:

Genomics
Bioinformatics
Systems Biology

Background:

Integrating multi-tissue and cell-type gene expression is vital for understanding biological mechanisms in disease and homeostasis.
Existing methods struggle with uncollected tissues and often require genotype data, raising privacy and availability issues.

Purpose of the Study:

To introduce HYFA (Hypergraph Factorisation), a genotype-agnostic graph representation learning approach for joint gene expression imputation.
To enable integration of multi-tissue and cell-type transcriptomic data, even with missing tissues per individual.

Main Methods:

HYFA utilizes a parameter-efficient graph representation learning framework.
It incorporates strong inductive biases to leverage shared regulatory architectures across tissues and genes.
The method is genotype-agnostic and handles a variable number of collected tissues.

Main Results:

HYFA demonstrated superior performance compared to existing methods on Genotype-Tissue Expression (GTEx) data, particularly with multiple reference tissues.
The HYFA-imputed dataset significantly improved the identification of replicable regulatory genetic variations (eQTLs).

Conclusions:

HYFA offers an effective and scalable solution for integrating diverse transcriptome biorepositories.
This approach accelerates the analysis of multi-tissue and cell-type gene expression, advancing disease mechanism and homeostasis research.