Back to Search Start Over

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

Authors :
Dimitrios Zisis
Athanasios Alexiou
Dimitra Karagkouni
Marios Miliotis
Ioannis Kavakiotis
Antonis Koussounadis
Artemis G. Hatzigeorgiou
Source :
Genes, Vol 12, Iss 46, p 46 (2021), DOAJ-Articles, UnpayWall, ORCID, Microsoft Academic Graph, Multidisciplinary Digital Publishing Institute, PubMed Central, Volume 12, Genes, Issue 1
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

microRNAs (miRNAs) are small non-coding RNAs (~22 nts) that are considered central post-transcriptional regulators of gene expression and key components in many pathological conditions. Next-Generation Sequencing (NGS) technologies have led to inexpensive, massive data production, revolutionizing every research aspect in the fields of biology and medicine. Particularly, small RNA-Seq (sRNA-Seq) enables small non-coding RNA quantification on a high-throughput scale, providing a closer look into the expression profiles of these crucial regulators within the cell. Here, we present DIANA-microRNA-Analysis-Pipeline (DIANA-mAP), a fully automated computational pipeline that allows the user to perform miRNA NGS data analysis from raw sRNA-Seq libraries to quantification and Differential Expression Analysis in an easy, scalable, efficient, and intuitive way. Emphasis has been given to data pre-processing, an early, critical step in the analysis for the robustness of the final results and conclusions. Through modularity, parallelizability and customization, DIANA-mAP produces high quality expression results, reports and graphs for downstream data mining and statistical analysis. In an extended evaluation, the tool outperforms similar tools providing pre-processing without any adapter knowledge. Closing, DIANA-mAP is a freely available tool. It is available dockerized with no dependency installations or standalone, accompanied by an installation manual through Github.

Details

Language :
English
ISSN :
20734425
Volume :
12
Issue :
2073-4425
Database :
OpenAIRE
Journal :
Genes
Accession number :
edsair.doi.dedup.....7c53bf817d58d8de122f201fb624899a