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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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Related Experiment Video

Updated: Oct 5, 2025

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

Comparative analysis of common alignment tools for single-cell RNA sequencing.

Ralf Schulze Brüning^1,2, Lukas Tombor^1,3, Marcel H Schulz^1,2,3

¹Institute of Cardiovascular Regeneration, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany.

|January 27, 2022

Summary

This study benchmarks single-cell RNA sequencing alignment tools, revealing significant performance differences. STARsolo, Cell Ranger, Alevin-fry, and Alevin offer comparable gene detection, while Kallisto shows potential mapping artifacts.

Keywords:

aligners benchmarking mappers mapping-algorithms single-cell RNA sequencing transcriptomics

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Last Updated: Oct 5, 2025

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

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Published on: June 24, 2021

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Published on: March 7, 2018

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) necessitates specialized bioinformatic tools for tasks like unique molecular identifier (UMI) quantification and cell barcode correction.
The rapid advancement of scRNA-seq technologies drives the development of novel computational approaches.

Purpose of the Study:

To benchmark common alignment tools for single-cell RNA sequencing data.
To evaluate variations in whitelisting, gene quantification, and overall performance across different tools.
To assess potential impacts on downstream analyses such as clustering and differential gene expression detection.

Main Methods:

Benchmarking of Cell Ranger (v6), STARsolo, Kallisto, Alevin, and Alevin-fry.
Utilized three published scRNA-seq datasets from human and mouse samples.
Data generated using different versions of the 10X sequencing protocol.

Main Results:

Observed striking differences in mapper runtimes.
Kallisto and Alevin exhibited variations in valid cell counts and gene detection per cell.
Kallisto reported a high cell count but included an overrepresentation of low-content cells; Alevin rarely reported such cells.
STARsolo, Cell Ranger 6, Alevin-fry, and Alevin yielded similar gene sets, whereas Kallisto identified potential mapping artifacts from Vmn and Olfr gene families.
Differences in mitochondrial content were noted based on annotation set usage (prefiltered vs. full).

Conclusions:

This study offers a detailed comparison of prevalent single-cell RNA sequencing mappers.
Highlights specific performance characteristics and potential biases of these tools when applied to 10X Genomics data.