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Updated: Dec 13, 2025

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

A comprehensive rat transcriptome built from large scale RNA-seq-based annotation.

Xiangjun Ji^1,2, Peng Li^1,3, James C Fuscoe⁴

¹Center for Bioinformatics and Computational Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China.

Nucleic Acids Research

|August 5, 2020

Summary

This summary is machine-generated.

A new Rat Transcriptome Re-annotation (RTR) significantly expands the rat gene and transcript annotations using extensive RNA-seq data. This resource improves understanding of gene expression and disease models in rats.

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Area of Science:

Biomedical research
Genomics
Transcriptomics

Background:

Rats are crucial model organisms for studying human diseases.
Existing rat transcriptome annotations are incomplete, limiting research.
Comprehensive genomic data is essential for accurate gene expression studies.

Purpose of the Study:

To construct a comprehensive and highly accurate rat transcriptome annotation.
To provide an improved reference for rat genetics and gene expression studies.
To facilitate research in rat disease and toxicity models.

Main Methods:

Utilized RNA-sequencing data from 320 samples across 11 rat organs (SEQC consortium).
Developed the Rat Transcriptome Re-annotation (RTR) database.
Annotated genes, transcripts, transcribed regions, and exons.

Main Results:

RTR contains 52,807 genes and 114,152 transcripts.
Identified 73,074 newly annotated transcripts, including coding and long noncoding RNAs.
Tissue type significantly impacts transcript expression patterns, with 11,715 genes ubiquitously expressed.

Conclusions:

The RTR provides a significantly enhanced resource for rat research.
This comprehensive transcriptome aids in understanding gene function and regulation in disease models.
RTR is freely available, supporting broader scientific discovery.