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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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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Dr.seq: a quality control and analysis pipeline for droplet sequencing.

Xiao Huo¹, Sheng'en Hu¹, Chengchen Zhao¹

¹School of Life Science and Technology, Shanghai Key Laboratory of Signaling and Disease Research, Tongji University, Shanghai 20092, China.

Bioinformatics (Oxford, England)

|May 7, 2016

Summary

Dr.seq is a new quality control (QC) and analysis pipeline for Drop-seq data, essential for refining gene expression analysis from single cells. This tool offers comprehensive QC measurements, improving the reliability of ultra-high-dimensional datasets.

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Last Updated: Mar 21, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

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Published on: June 8, 2020

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

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High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing

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Published on: May 21, 2020

Area of Science:

Single-cell genomics
Bioinformatics
Computational biology

Background:

Drop-seq enables simultaneous gene expression analysis of thousands of individual cells.
Current Drop-seq data analysis necessitates robust quality control (QC) and refinement steps.
Ultra-high-dimensional datasets from single-cell technologies require specialized tools for QC and cell relationship determination.

Purpose of the Study:

To develop a comprehensive quality control (QC) and analysis pipeline for Drop-seq data.
To provide dedicated QC measurements across multiple data levels.
To facilitate the determination of cell relationships within large single-cell datasets.

Main Methods:

Development of Dr.seq, a novel QC and analysis pipeline tailored for Drop-seq data.
Implementation of four distinct groups of QC measurements: reads level, bulk-cell level, individual-cell level, and cell-clustering level.
Validation of Dr.seq using both simulated and published Drop-seq datasets.

Main Results:

Dr.seq provides a comprehensive suite of QC metrics for Drop-seq data.
The pipeline effectively assesses data quality at reads, bulk-cell, individual-cell, and cell-clustering levels.
Assessment on simulated and published data confirmed the reliability and accuracy of Dr.seq.

Conclusions:

Dr.seq offers a valuable and comprehensive solution for Drop-seq data quality control and analysis.
The pipeline is designed for ease of use and extensibility to other droplet-based single-cell sequencing technologies.
Dr.seq enhances the reliability of gene expression analysis from large-scale single-cell experiments.