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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.seq2: A quality control and analysis pipeline for parallel single cell transcriptome and epigenome data.

Chengchen Zhao¹, Sheng'en Hu¹, Xiao Huo¹

¹Translational Medical Center for Stem Cell Therapy & Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Science and Technology, Shanghai Key Laboratory of Signaling and Disease Research, Tongji University, Shanghai, China.

|July 4, 2017

Summary

This summary is machine-generated.

Dr.seq2 is a new pipeline for analyzing single-cell transcriptome and epigenome data. This tool offers quality control and cell heterogeneity analysis for multiple data types, including single-cell ATAC-seq.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell technologies like scATAC-seq are powerful but often lack integrated analysis tools.
Existing methods are typically designed for single data types, limiting comprehensive analysis.
There is a need for systematic approaches to handle diverse single-cell epigenome and transcriptome data.

Purpose of the Study:

To develop a unified Quality Control (QC) and analysis pipeline for multiple single-cell transcriptome and epigenome data types.
To enable robust cell-to-cell heterogeneity analysis on complex, high-dimensional datasets.
To provide a systematic solution for analyzing scATAC-seq and Drop-ChIP data alongside transcriptome data.

Main Methods:

Developed Dr.seq2, a comprehensive bioinformatics pipeline.
Integrated Quality Control (QC) measurements for assessing data quality.
Implemented algorithms for cell heterogeneity analysis across multiple single-cell data types.

Main Results:

Dr.seq2 provides four groups of QC measurements.
The pipeline successfully performed cell heterogeneity analysis.
Demonstrated reliable results on existing single-cell transcriptome and epigenome datasets.

Conclusions:

Dr.seq2 is a systematic and comprehensive pipeline for parallel single-cell transcriptome and epigenome data analysis.
The tool addresses the need for integrated QC and heterogeneity analysis.
Dr.seq2 facilitates deeper insights into cellular complexity from multi-modal single-cell data.