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Diana-map: Analyzing mirna from raw ngs data to quantification

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Autor
Alexiou A., Zisis D., Kavakiotis I., Miliotis M., Koussounadis A., Karagkouni D., Hatzigeorgiou A.G.
Fecha
2021
Language
en
DOI
10.3390/genes12010046
Materia
Data handling
Data mining
Gene expression
Pipelines
RNA
Biology and medicine
Data preprocessing
Expression analysis
Expression profile
High throughput
Next-generation sequencing
Pathological conditions
Post-transcriptional
Quality control
bioinformatics
data processing
gene expression
RNA
Coding
Data Processing
Maps
Nucleic Acids
Quality Control
Regulators
Statistical Analysis
microRNA
animal
benchmarking
biology
classification
data mining
gene expression regulation
gene library
genetic database
genetics
high throughput sequencing
human
metabolism
mouse
procedures
sequence analysis
software
Animals
Benchmarking
Computational Biology
Data Mining
Databases, Genetic
Gene Expression Regulation
Gene Library
High-Throughput Nucleotide Sequencing
Humans
Mice
MicroRNAs
Sequence Analysis, RNA
Software
MDPI AG
Mostrar el registro completo del ítem
Resumen
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. © 2020 by the authors.
URI
http://hdl.handle.net/11615/70420
Colecciones
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ. [19735]

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