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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: Nov 23, 2025

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

DIANA-mAP: Analyzing miRNA from Raw NGS Data to Quantification.

Athanasios Alexiou^1,2, Dimitrios Zisis², Ioannis Kavakiotis¹

¹DIANA Lab, Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece.

|January 5, 2021

Summary

This summary is machine-generated.

DIANA-mAP is a new automated pipeline for microRNA (miRNA) Next-Generation Sequencing data analysis. It offers efficient quantification and differential expression analysis from raw sequencing data, outperforming existing tools.

Keywords:

NGS analysis bioinformatics expression microRNA pipeline quantification small RNA-Seq

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Published on: October 7, 2025

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

microRNAs (miRNAs) are key post-transcriptional gene regulators implicated in various diseases.
Next-Generation Sequencing (NGS) has transformed biological and medical research, especially small RNA-Seq (sRNA-Seq) for miRNA profiling.
Accurate miRNA quantification is crucial for understanding cellular regulation and disease mechanisms.

Purpose of the Study:

To introduce DIANA-microRNA-Analysis-Pipeline (DIANA-mAP), a fully automated computational pipeline for miRNA NGS data analysis.
To provide an easy, scalable, efficient, and intuitive tool for processing raw sRNA-Seq data.
To emphasize robust data pre-processing for reliable downstream analysis and conclusions.

Main Methods:

Development of a modular and parallelizable computational pipeline (DIANA-mAP).
Integration of automated pre-processing, quantification, and differential expression analysis for sRNA-Seq data.
Implementation of features for high-quality expression results, reports, and graphs.

Main Results:

DIANA-mAP successfully automates miRNA NGS data analysis from raw libraries to differential expression.
The pipeline demonstrated superior performance in pre-processing compared to similar tools, even without adapter knowledge.
The tool generates high-quality outputs suitable for downstream data mining and statistical analysis.

Conclusions:

DIANA-mAP is a freely available, user-friendly, and efficient tool for comprehensive miRNA NGS data analysis.
Its robust pre-processing capabilities enhance the reliability of miRNA expression profiling.
The pipeline supports various deployment options (Docker, standalone) with accessible documentation.