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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: Jun 13, 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

Chevreul: An R Bioconductor Package for Exploratory Analysis of Full-Length Single Cell Sequencing.

Kevin Stachelek^1,2, Bhavana Bhat¹, David Cobrinik^1,3,4,5

¹The Vision Center, Department of Surgery, and Saban Research Institute, Children's Hospital Los Angeles, Los Angeles, CA, USA.

Biorxiv : the Preprint Server for Biology

|June 12, 2025

Summary

This summary is machine-generated.

Chevreul is a new R package for single-cell RNA sequencing (scRNA-seq) analysis. It offers user-friendly tools for full-length transcript analysis, batch correction, and visualization, empowering researchers without extensive programming experience.

Keywords:

Bioinformatics Biomedical Science

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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) generates high-dimensional data requiring specialized analysis tools.
Existing scRNA-seq packages often lack comprehensive support for full-length transcript analysis and isoform inference.
User-friendly interfaces are crucial for broader accessibility in scRNA-seq data analysis.

Purpose of the Study:

To introduce Chevreul, an open-source R package and Shiny app for scRNA-seq data processing and visualization.
To provide advanced capabilities for full-length RNA sequencing data analysis, including exon coverage and transcript isoform inference.
To facilitate batch correction and enable exploratory data analysis for researchers with varying programming expertise.

Main Methods:

Development of an R package and an interactive R Shiny application.
Implementation of functions for batch integration, quality control, normalization, dimensionality reduction, and clustering.
Integration of visualization tools for displaying gene/transcript expression on various embeddings (PCA, tSNE, UMAP) and statistical evaluation of differential expression.

Main Results:

Chevreul enables comprehensive processing and visualization of scRNA-seq data through user-friendly functions.
The package supports full-length RNA sequencing data analysis, including specialized tools for isoform-level analysis and alternative splicing detection.
Interactive visualization in the Shiny app allows dynamic exploration of gene expression and differential expression analysis without extensive programming.

Conclusions:

Chevreul enhances scRNA-seq data analysis by offering ease of use, advanced isoform analysis, and robust visualization capabilities.
The tool democratizes the analysis of full-length scRNA-seq data, particularly for researchers lacking extensive bioinformatics programming experience.
Chevreul represents a valuable addition to the R Bioconductor ecosystem for single-cell genomics research.