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Related Experiment Video

Updated: Mar 27, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

SEPARATING THE CAUSES AND CONSEQUENCES IN DISEASE TRANSCRIPTOME.

Yong Fuga Li¹, Fuxiao Xin, Russ B Altman

¹Department of Bioengineering, Stanford University, USA2Stanford Genome Technology Center, Stanford University, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|January 19, 2016

Summary

This summary is machine-generated.

Identifying complex disease causes is challenging. Our diseaseExPatho approach integrates transcriptome, regulome, and genome-wide association study (GWAS) data to pinpoint causal gene modules, even when not differentially expressed.

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Last Updated: Mar 27, 2026

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Published on: March 5, 2022

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Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Area of Science:

Genomics
Systems Biology
Computational Biology

Background:

Complex diseases have multifactorial causes and heterogeneous phenotypes, complicating research.
Transcriptome profiling offers insights but struggles to disentangle disease causes from consequences.
Genome-wide association studies (GWAS) identify genetic factors but lack comprehensive pathogenetic views.

Purpose of the Study:

To develop and validate diseaseExPatho, a novel computational approach for separating causal and consequential signals in disease transcriptomes.
To integrate transcriptome data, regulome knowledge, and GWAS results for improved disease mechanism discovery.

Main Methods:

diseaseExPatho computationally deconvolutes gene expression data into modules.
Modules are hierarchically ranked using a novel regulome-based algorithm.
Potential causal gene modules are labeled by correlating with GWAS data.

Main Results:

Putative causal modules were identified that were not necessarily differentially expressed.
Strongly differentially expressed modules often lacked enrichment for top GWAS variations.
Regulome-based module ranking consistently prioritized causal modules across six diseases.

Conclusions:

diseaseExPatho effectively separates disease causes and consequences within transcriptomes.
The approach can prioritize causal pathways in complex diseases, with or without GWAS data.
This method aids in understanding disease pathogenesis for both common and rare conditions.