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dc.creatorAlexiou A., Zisis D., Kavakiotis I., Miliotis M., Koussounadis A., Karagkouni D., Hatzigeorgiou A.G.en
dc.date.accessioned2023-01-31T07:30:52Z
dc.date.available2023-01-31T07:30:52Z
dc.date.issued2021
dc.identifier10.3390/genes12010046
dc.identifier.issn19994907
dc.identifier.urihttp://hdl.handle.net/11615/70420
dc.description.abstractmicroRNAs (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.en
dc.language.isoenen
dc.sourceForestsen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85098779748&doi=10.3390%2fgenes12010046&partnerID=40&md5=3ba9b7918a665a46205ff6d9b37bf6af
dc.subjectData handlingen
dc.subjectData miningen
dc.subjectGene expressionen
dc.subjectPipelinesen
dc.subjectRNAen
dc.subjectBiology and medicineen
dc.subjectData preprocessingen
dc.subjectExpression analysisen
dc.subjectExpression profileen
dc.subjectHigh throughputen
dc.subjectNext-generation sequencingen
dc.subjectPathological conditionsen
dc.subjectPost-transcriptionalen
dc.subjectQuality controlen
dc.subjectbioinformaticsen
dc.subjectdata processingen
dc.subjectgene expressionen
dc.subjectRNAen
dc.subjectCodingen
dc.subjectData Processingen
dc.subjectMapsen
dc.subjectNucleic Acidsen
dc.subjectQuality Controlen
dc.subjectRegulatorsen
dc.subjectStatistical Analysisen
dc.subjectmicroRNAen
dc.subjectanimalen
dc.subjectbenchmarkingen
dc.subjectbiologyen
dc.subjectclassificationen
dc.subjectdata miningen
dc.subjectgene expression regulationen
dc.subjectgene libraryen
dc.subjectgenetic databaseen
dc.subjectgeneticsen
dc.subjecthigh throughput sequencingen
dc.subjecthumanen
dc.subjectmetabolismen
dc.subjectmouseen
dc.subjectproceduresen
dc.subjectsequence analysisen
dc.subjectsoftwareen
dc.subjectAnimalsen
dc.subjectBenchmarkingen
dc.subjectComputational Biologyen
dc.subjectData Miningen
dc.subjectDatabases, Geneticen
dc.subjectGene Expression Regulationen
dc.subjectGene Libraryen
dc.subjectHigh-Throughput Nucleotide Sequencingen
dc.subjectHumansen
dc.subjectMiceen
dc.subjectMicroRNAsen
dc.subjectSequence Analysis, RNAen
dc.subjectSoftwareen
dc.subjectMDPI AGen
dc.titleDiana-map: Analyzing mirna from raw ngs data to quantificationen
dc.typejournalArticleen


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