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Updated: Mar 23, 2026

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

EPIG-Seq: extracting patterns and identifying co-expressed genes from RNA-Seq data.

Jianying Li^1,2,3, Pierre R Bushel^4,5

¹Integrative Bioinformatics Group, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA.

|March 24, 2016

Summary

This summary is machine-generated.

We developed EPIG-Seq, a novel software for analyzing RNA sequencing (RNA-Seq) count data to identify co-expressed genes. This pattern-based approach overcomes limitations of existing methods, enabling biologically meaningful discoveries even with small sample sizes.

Keywords:

Cancer Clustering EPIG-Seq Gene expression Pattern analysis RNA-Seq Toxicogenomics

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Last Updated: Mar 23, 2026

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

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Published on: November 11, 2014

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

RNA sequencing (RNA-Seq) generates genome-wide gene expression data, which is count-based and unsuitable for standard normal distribution models.
Existing normalization methods for RNA-Seq data have limitations that can negatively affect downstream analysis.
Current count-based analysis methods are primarily for pairwise or multiclass comparisons, lacking pattern-based approaches for identifying co-expressed genes.

Purpose of the Study:

To adapt and develop a novel methodology, EPIG-Seq, for pattern-based analysis of RNA sequencing count data.
To identify statistically significant clusters of co-expressed genes across various experimental conditions.
To provide a robust tool for discovering biologically relevant gene expression patterns.

Main Methods:

Adapted the Extracting Patterns and Identifying Genes (EPIG-Seq) methodology for RNA-Seq count data.
Utilized count-based correlation for gene similarity, quasi-Poisson modeling for data dispersion, and a location parameter for differential expression magnitude.
Implemented a two-step process: pattern profile extraction and subsequent gene clustering to pattern seeds with statistical significance computation.

Main Results:

EPIG-Seq effectively analyzes count-level RNA-Seq data, accommodating inflated zero counts.
Identified statistically significant clusters of co-expressed genes across experimental conditions.
Demonstrated reliable results with small sample sizes, considering dispersion in replicate data.
Successfully applied EPIG-Seq to toxicogenomics and cancer datasets to identify biologically relevant co-expressed genes.

Conclusions:

EPIG-Seq offers a unique pattern analysis approach for RNA-Seq count data across experimental groups.
The software enables the extraction of biologically meaningful co-expressed genes linked to coordinated regulation.
EPIG-Seq provides a valuable tool for uncovering complex gene expression patterns in RNA-Seq studies.