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RNA-seq

RNA-seq

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.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Related Experiment Video

Updated: Sep 10, 2025

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

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

Published on: September 18, 2021

Tree-based differential testing using inferential uncertainty for RNA-seq.

Noor P Singh¹, Euphy Wu², Jason Fan¹

¹Department of Computer Science, University of Maryland, College Park, Maryland 20742, USA.

Genome Research

|August 21, 2025

Summary

This summary is machine-generated.

Identifying differential gene expression is challenging due to transcript abundance uncertainty. Our new method, mehenDi, uses a tree structure to find significant expression changes, including those missed by other approaches.

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Last Updated: Sep 10, 2025

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Differential expression analysis of RNA-sequencing data is critical for biological insights.
Transcript abundance estimation uncertainties can lead to false positives or reduced statistical power.
Existing methods often struggle to effectively incorporate this uncertainty into differential testing.

Purpose of the Study:

To introduce mehenDi, a novel method for differential transcript expression analysis.
To leverage a hierarchical tree structure (TreeTerminus) to manage uncertainty in transcript abundance.
To identify differentially expressed transcripts and transcript groups, including those at inner nodes of the tree.

Main Methods:

Utilized the TreeTerminus hierarchical structure to represent transcript relationships and uncertainty.
Developed mehenDi for data-driven differential testing on the tree structure.
Selected nodes (transcripts or inner nodes) to maximize signal while controlling for abundance uncertainty.
Compared mehenDi against existing tree-based and uncertainty-aware differential expression methods.

Main Results:

mehenDi successfully identified differentially expressed inner nodes, revealing signals missed by transcript-only analysis.
The method demonstrated robust performance on both simulated and experimental RNA-seq datasets.
mehenDi effectively balances the detection of differential expression with the control of uncertainty.

Conclusions:

mehenDi offers an advanced approach to differential expression analysis by incorporating transcriptomic hierarchy and uncertainty.
The method enhances the discovery of biologically relevant differential expression signals.
mehenDi provides a powerful tool for transcriptomic data interpretation.