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Integrative approaches for analysis of mRNA and microRNA high-throughput data

Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such as miRNAs, lncRNAs, piRNAs and many other RNA species. In silico analysis of datasets, represent...

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Detalles Bibliográficos
Autores principales: Nazarov, Petr V., Kreis, Stephanie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895676/
https://www.ncbi.nlm.nih.gov/pubmed/33680358
http://dx.doi.org/10.1016/j.csbj.2021.01.029
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author Nazarov, Petr V.
Kreis, Stephanie
author_facet Nazarov, Petr V.
Kreis, Stephanie
author_sort Nazarov, Petr V.
collection PubMed
description Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such as miRNAs, lncRNAs, piRNAs and many other RNA species. In silico analysis of datasets, representing only one RNA species is well established and a variety of tools and pipelines are available. However, attaining a more systematic view of how different players come together to regulate the expression of a gene or a group of genes requires a more intricate approach to data analysis. To fully understand complex transcriptional networks, datasets representing different RNA species need to be integrated. In this review, we will focus on miRNAs as key post-transcriptional regulators summarizing current computational approaches for miRNA:target gene prediction as well as new data-driven methods to tackle the problem of comprehensively and accurately dissecting miRNome-targetome interactions.
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spelling pubmed-78956762021-03-04 Integrative approaches for analysis of mRNA and microRNA high-throughput data Nazarov, Petr V. Kreis, Stephanie Comput Struct Biotechnol J Review Article Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such as miRNAs, lncRNAs, piRNAs and many other RNA species. In silico analysis of datasets, representing only one RNA species is well established and a variety of tools and pipelines are available. However, attaining a more systematic view of how different players come together to regulate the expression of a gene or a group of genes requires a more intricate approach to data analysis. To fully understand complex transcriptional networks, datasets representing different RNA species need to be integrated. In this review, we will focus on miRNAs as key post-transcriptional regulators summarizing current computational approaches for miRNA:target gene prediction as well as new data-driven methods to tackle the problem of comprehensively and accurately dissecting miRNome-targetome interactions. Research Network of Computational and Structural Biotechnology 2021-01-26 /pmc/articles/PMC7895676/ /pubmed/33680358 http://dx.doi.org/10.1016/j.csbj.2021.01.029 Text en © 2021 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review Article
Nazarov, Petr V.
Kreis, Stephanie
Integrative approaches for analysis of mRNA and microRNA high-throughput data
title Integrative approaches for analysis of mRNA and microRNA high-throughput data
title_full Integrative approaches for analysis of mRNA and microRNA high-throughput data
title_fullStr Integrative approaches for analysis of mRNA and microRNA high-throughput data
title_full_unstemmed Integrative approaches for analysis of mRNA and microRNA high-throughput data
title_short Integrative approaches for analysis of mRNA and microRNA high-throughput data
title_sort integrative approaches for analysis of mrna and microrna high-throughput data
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895676/
https://www.ncbi.nlm.nih.gov/pubmed/33680358
http://dx.doi.org/10.1016/j.csbj.2021.01.029
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