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

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DeepITEH: a deep learning framework for identifying tissue-specific eRNAs from the human genome.

Tianjiao Zhang¹, Liangyu Li¹, Hailong Sun¹

¹College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.

Bioinformatics (Oxford, England)

|June 9, 2023

Summary

DeepITEH accurately identifies enhancer RNAs (eRNAs) using RNA-seq and histone modification data. This deep learning framework improves eRNA prediction in various tissues, aiding cancer research.

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Enhancers are crucial cis-regulatory elements controlling gene expression.
Enhancer RNAs (eRNAs) are transcribed from enhancers and play roles in tissue-specific gene regulation and cancer development.
Current eRNA identification methods struggle with accuracy due to reliance on single data types and lack of tissue specificity.

Purpose of the Study:

To develop a deep learning framework, DeepITEH, for accurate identification of enhancer RNAs (eRNAs).
To leverage both RNA-seq and histone modification data from multiple tissue samples for improved eRNA prediction.
To enhance understanding of eRNA function in gene regulation and cancer.

Main Methods:

DeepITEH utilizes a deep learning approach integrating RNA-seq and histone modification data.
It classifies eRNAs into regularly and accidentally expressed types using multi-sample histone modification data.
The framework combines sequence and epigenetic features for tissue-specific eRNA identification.

Main Results:

DeepITEH demonstrated significantly improved eRNA prediction accuracy across seven out of eight tested tissues (normal and cancer).
Performance was evaluated against state-of-the-art methods including SeqPose, iEnhancer-RD, LSTMAtt, and FRL.
The framework effectively identifies potential eRNAs on the human genome.

Conclusions:

DeepITEH offers a robust and accurate method for identifying tissue-specific eRNAs.
The framework's ability to integrate diverse data types overcomes limitations of existing methods.
DeepITEH provides valuable insights for studying eRNA roles in gene expression and cancer pathogenesis.