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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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Updated: Mar 10, 2026

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

Simulation-based comprehensive benchmarking of RNA-seq aligners.

Giacomo Baruzzo¹, Katharina E Hayer², Eun Ji Kim²

¹Department of Information Engineering, University of Padova, Padua, Italy.

|December 13, 2016

Summary

This summary is machine-generated.

Benchmarking RNA-sequencing (RNA-seq) aligners reveals significant performance variation. Optimized parameters improve accuracy, and popular tools are not always the best performers for RNA-seq analysis.

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Published on: August 4, 2016

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate alignment is crucial for RNA-sequencing (RNA-seq) analysis.
The performance of RNA-seq aligners significantly impacts downstream results.
Benchmarking aligner accuracy is essential for reliable data interpretation.

Purpose of the Study:

To comprehensively benchmark 14 common splice-aware RNA-seq aligners.
To evaluate aligner accuracy at base, read, and exon junction levels.
To compare the performance of default versus optimized aligner parameters.

Main Methods:

Utilized simulated RNA-seq data for benchmarking.
Assessed accuracy across base, read, and exon junction levels.
Compared performance using default and optimized parameter settings.

Main Results:

Aligner performance varied significantly based on genome complexity.
Accuracy and popularity of aligners showed a weak correlation.
The most cited aligner demonstrated underperformance, especially with default settings.

Conclusions:

Optimizing aligner parameters is critical for improving RNA-seq analysis accuracy.
Genome complexity influences the choice and performance of RNA-seq aligners.
Relying solely on aligner popularity can lead to suboptimal analysis outcomes.