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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: Sep 2, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

SMaSH: a scalable, general marker gene identification framework for single-cell RNA-sequencing.

M E Nelson^1,2,3,4, S G Riva^5,6,7,8, A Cvejic^9,10,11

¹European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK. nelson@ebi.ac.uk.

BMC Bioinformatics

|August 8, 2022

Summary

This summary is machine-generated.

We developed SMaSH, a computational framework to identify key marker genes from single-cell RNA sequencing data. This tool reliably characterizes specific cell populations and improves downstream analyses like spatial transcriptomics.

Keywords:

Feature selection Marker genes Single-cell RNA-sequencing

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Last Updated: Sep 2, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Published on: March 12, 2021

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Area of Science:

Genomics
Bioinformatics
Cell Biology

Background:

Single-cell RNA sequencing (scRNA-seq) enables the study of cellular heterogeneity.
Identifying cell type-specific marker genes is crucial for understanding gene regulation and isolating specific cell phenotypes.
Existing methods lack emphasis on specific cell phenotypes for downstream applications like spatial transcriptomics.

Purpose of the Study:

To present SMaSH, a computational framework for extracting key marker genes from scRNA-seq data.
To reliably characterize highly-specific and niche cell populations.
To facilitate downstream experimental protocols such as differential gene expression and spatial transcriptomics.

Main Methods:

Development of the SMaSH computational framework.
Application of SMaSH to single-cell RNA sequencing datasets.
Evaluation of SMaSH marker genes on spatial transcriptomics data.

Main Results:

SMaSH extracts robust and biologically relevant marker genes.
SMaSH outperforms existing computational approaches for general marker gene calculation.
SMaSH markers effectively identify localized compartments in mouse cortex spatial transcriptomics data.

Conclusions:

SMaSH is a novel methodology for calculating robust marker genes from large scRNA-seq datasets.
SMaSH has implications for gene identification in spatial transcriptomics experiments.
SMaSH is integrated with ScanPy, offering a valuable bioinformatics tool for cell type characterization and validation.