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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: May 1, 2026

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

TSSAR: TSS annotation regime for dRNA-seq data.

Fabian Amman¹, Michael T Wolfinger, Ronny Lorenz

¹Bioinformatics Group, Department of Computer Science and the Interdisciplinary Center for Bioinformatic, University of Leipzig, Härtelstraße 16-18, 04107 Leipzig, Germany. afabian@bioinf.uni-leipzig.de.

BMC Bioinformatics

|March 29, 2014

Summary

This summary is machine-generated.

TSSAR automates the identification of transcription start sites (TSS) from differential RNA sequencing (dRNA-seq) data. This tool provides accurate and efficient analysis, improving upon manual methods for bacterial operon studies.

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Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

Differential RNA sequencing (dRNA-seq) is crucial for bacterial operon and transcription start site (TSS) analysis.
Manual annotation of dRNA-seq data is labor-intensive and prone to subjective bias.

Purpose of the Study:

To develop TSSAR, an automated tool for de novo TSS annotation from dRNA-seq data.
To establish a statistical foundation for identifying primary transcripts using dRNA-seq library statistics.

Main Methods:

TSSAR employs statistical modeling based on Poisson and Skellam distributions to analyze dRNA-seq library counts.
The tool identifies significantly enriched primary transcripts by analyzing read distribution at genomic positions.

Main Results:

TSSAR demonstrated superior performance in reproducing manual TSS annotations compared to static cutoff methods.
The tool accurately reproduced experimentally validated TSS in *H. pylori*, outperforming alternative approaches.

Conclusions:

Automated analysis of dRNA-seq data using TSSAR enhances the technique's utility and enables sophisticated transcriptomal studies.
TSSAR is available as a user-friendly RESTful Web service and a standalone version, promoting broader application in high-throughput dRNA-seq analysis.