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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
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Updated: Jul 29, 2025

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts

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Published on: January 2, 2018

Quantifying transcriptome diversity: a review.

Emma F Jones¹, Anisha Haldar¹, Vishal H Oza¹

¹The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA.

Briefings in Functional Genomics

|May 24, 2023

Summary

This summary is machine-generated.

Gene expression diversity drives phenotypic variation. This review clarifies transcriptome diversity at gene and isoform levels, crucial for understanding biological complexity.

Keywords:

gene expression gene-level diversity isoform-level diversity transcript diversity transcriptional variation transcriptome diversity

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Last Updated: Jul 29, 2025

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts

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Published on: January 2, 2018

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Published on: September 18, 2021

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

Gene expression heterogeneity influences protein diversity, function, and phenotypic variation.
Ambiguous terminology in gene expression diversity can lead to misinterpretation of biological data.
Understanding transcriptome diversity is essential for a comprehensive view of biological systems.

Approach:

Define transcriptome diversity, encompassing gene-level (across samples/genes) and isoform-level (alternative splicing) heterogeneity.
Review modulators and quantification methods for gene-level transcriptome diversity.
Discuss the role of alternative splicing in isoform-level diversity and its quantification.

Key Points:

Transcriptome diversity encompasses both gene-level and isoform-level heterogeneity.
Alternative splicing is a key driver of isoform-level diversity.
Computational tools are available for quantifying transcriptome diversity from high-throughput sequencing data.

Conclusions:

Accurate measurement of transcriptome diversity provides a more complete understanding of biological heterogeneity.
Clarifying terminology and quantification methods enhances the study of gene expression diversity.
Future applications of transcriptome diversity analysis hold significant potential in various biological fields.