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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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3' End Sequencing Library Preparation with A-seq2

3' End Sequencing Library Preparation with A-seq2

Published on: October 10, 2017

End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data.

Alan Derr¹, Chaoxing Yang², Rapolas Zilionis³

¹Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts 01655, USA.

Genome Research

|July 30, 2016

Summary

The End Sequence Analysis Toolkit (ESAT) corrects computational artifacts in RNA end-sequencing data. This new tool reveals biological insights, like LPS-stimulated shifts in 3'-isoforms and pancreatic islet cell heterogeneity.

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Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

RNA sequencing (RNA-seq) protocols focusing on transcript termini are useful when template quantity is limited.
Standard analytical methods for global RNA-seq can introduce computational artifacts when applied to end-sequencing data.

Purpose of the Study:

To develop and validate a computational toolkit, the End Sequence Analysis Toolkit (ESAT), specifically designed for RNA end-sequencing data.
To address the limitations of conventional computational methods in analyzing transcript termini data.

Main Methods:

Creation of the End Sequence Analysis Toolkit (ESAT).
Comparison of end-sequencing and bulk RNA-seq using dendritic cell RNA stimulated with lipopolysaccharide (LPS).
Application of ESAT to droplet-based microfluidics-generated cDNA libraries from individual pancreatic islet cells.

Main Results:

ESAT detected an LPS-stimulated shift to shorter 3 -isoforms in dendritic cells, an artifact not identified by conventional methods.
ESAT analysis of single-cell pancreatic islet data revealed nine distinct cell types and three distinct beta-cell subtypes.
The toolkit identified complex interactions between hormone secretion and vascularization within pancreatic islets.

Conclusions:

ESAT provides a necessary and broadly applicable computational pipeline for analyzing both bulk and single-cell RNA end-sequencing data.
The toolkit overcomes computational artifacts, enabling more accurate biological discoveries from transcript termini sequencing.