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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: Aug 23, 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

Multi-level cellular and functional annotation of single-cell transcriptomes using scPipeline.

Nicholas Mikolajewicz^1,2, Rafael Gacesa¹, Magali Aguilera-Uribe^1,2,3

¹Donnelly Centre, University of Toronto, Toronto, ON, Canada.

Communications Biology

|October 28, 2022

Summary

This summary is machine-generated.

scPipeline is a new toolbox for single-cell RNA sequencing (scRNA-seq) analysis. It provides modular workflows for cell annotation and user-friendly reports, improving the understanding of complex biological systems.

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Last Updated: Aug 23, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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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

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) provides high-resolution insights into cellular heterogeneity and gene expression.
Analyzing scRNA-seq data is computationally intensive and requires specialized tools for accurate interpretation.

Purpose of the Study:

To introduce scPipeline, a novel toolbox for scRNA-seq data analysis.
To offer modular workflows for multi-level cell annotation and user-friendly reporting.
To enhance the accuracy and efficiency of scRNA-seq data interpretation.

Main Methods:

Development of scPipeline, a toolbox integrating existing methods with new analytical approaches.
Implementation of co-dependency index (CDI)-based differential expression analysis.
Cluster resolution optimization using a marker-specificity criterion.
Marker-based cell-type annotation utilizing Miko scoring.
Gene program discovery via scale-free shared nearest neighbor network (SSN) analysis.

Main Results:

scPipeline offers modular workflows for comprehensive scRNA-seq data analysis.
The toolbox incorporates advanced methods for cell annotation, including CDI, marker-specificity, Miko scoring, and SSN analysis.
Validation across diverse scRNA-seq datasets demonstrates the utility of scPipeline for developmental and immunological atlases.

Conclusions:

scPipeline provides a flexible and robust computational framework for in-depth scRNA-seq analysis.
The toolbox facilitates accurate and user-friendly cellular transcriptomic annotation.
scPipeline advances the functional interrogation of complex biological systems at single-cell resolution.