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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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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

scPipe: an extended preprocessing pipeline for comprehensive single-cell ATAC-Seq data integration in R/Bioconductor.

Shanika L Amarasinghe^1,2, Phil Yang¹, Oliver Voogd¹

¹The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia.

NAR Genomics and Bioinformatics

|December 4, 2023

Summary

The scPipe R package now supports single-cell ATAC-Seq and multi-modal data analysis. It offers robust preprocessing and quality control for single-cell genomics, enabling downstream analyses in R.

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-Seq) analysis relies on flexible bioinformatics tools.
Emerging single-cell technologies like ATAC-Seq require adaptable analytical pipelines.
Existing tools may not fully support multi-modal single-cell data integration.

Purpose of the Study:

To enhance the scPipe R/Bioconductor package for single-cell ATAC-Seq and multi-modal (RNA-Seq and ATAC-Seq) data analysis.
To develop a comprehensive preprocessing pipeline for diverse single-cell genomics data.
To facilitate downstream analyses of single-cell ATAC-Seq data within the R environment.

Main Methods:

Extended scPipe to process single-cell ATAC-Seq and multi-modal data.
Implemented data cleaning steps including removal of duplicated reads and low-quality cells/features.
Generated a SingleCellExperiment object containing sparse count matrices and metadata for quality control and annotations.

Main Results:

scPipe efficiently identifies true single cells through flexible quality control thresholds.
The package generates a SingleCellExperiment object with comprehensive quality control metrics and feature annotations.
Demonstrated the utility of scPipe for downstream single-cell ATAC-Seq analyses such as dimensionality reduction and motif enrichment.

Conclusions:

The enhanced scPipe package provides a complete beginning-to-end pipeline for single-cell ATAC-Seq and RNA-Seq data analysis in R.
scPipe offers flexibility for users to fine-tune quality control parameters based on various metrics.
The tool empowers researchers to leverage Bioconductor's downstream analysis capabilities for single-cell genomics data.