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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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A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

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MapMan Visualization of RNA-Seq Data Using Mercator4 Functional Annotations.

Marie Bolger¹, Rainer Schwacke², Björn Usadel²

¹Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich, Jülich, Germany. m.bolger@fz-juelich.de.

Methods in Molecular Biology (Clifton, N.J.)

|August 27, 2021

Summary

This summary is machine-generated.

Interpreting complex plant omics data is challenging. This chapter presents MapMan visualization software, MapMan4 ontology, and Mercator4 annotation to aid biological process discovery.

Keywords:

Functional annotation MapMan Mercator4 RNA-Seq visualization Web-based tool

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Last Updated: Oct 22, 2025

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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:

Plant biology
Bioinformatics
Genomics

Background:

Plant omics research generates large datasets from multiple samples and time points.
Interpreting these complex datasets to understand biological processes is a significant challenge.

Purpose of the Study:

To present a solution for interpreting complex plant omics data.
To introduce the MapMan transcript visualization application, MapMan4 ontology, and Mercator4 annotation process.

Main Methods:

Utilizing the MapMan transcript visualization application.
Employing the MapMan4 ontology for data organization.
Using the Mercator4 online annotation process for gene function assignment.

Main Results:

The MapMan tool facilitates the interpretation of large-scale plant omics data.
MapMan enables visualization of gene expression patterns across different conditions and time points.
The integrated MapMan4 ontology and Mercator4 annotation provide a framework for biological insight.

Conclusions:

MapMan, MapMan4 ontology, and Mercator4 offer a powerful integrated solution for plant omics data analysis.
This approach aids researchers in gaining deeper insights into plant biological processes.
The described tools enhance the discoverability of biological patterns within complex omics datasets.