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Revealing the History and Mystery of RNA-Seq

Advances in RNA-sequencing technologies have led to the development of intriguing experimental setups, a massive accumulation of data, and high demand for tools to analyze it. To answer this demand, computational scientists have developed a myriad of data analysis pipelines, but it is less often con...

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Detalles Bibliográficos
Autores principales: Gondane, Aishwarya, Itkonen, Harri M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047236/
https://www.ncbi.nlm.nih.gov/pubmed/36975490
http://dx.doi.org/10.3390/cimb45030120
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author Gondane, Aishwarya
Itkonen, Harri M.
author_facet Gondane, Aishwarya
Itkonen, Harri M.
author_sort Gondane, Aishwarya
collection PubMed
description Advances in RNA-sequencing technologies have led to the development of intriguing experimental setups, a massive accumulation of data, and high demand for tools to analyze it. To answer this demand, computational scientists have developed a myriad of data analysis pipelines, but it is less often considered what the most appropriate one is. The RNA-sequencing data analysis pipeline can be divided into three major parts: data pre-processing, followed by the main and downstream analyses. Here, we present an overview of the tools used in both the bulk RNA-seq and at the single-cell level, with a particular focus on alternative splicing and active RNA synthesis analysis. A crucial part of data pre-processing is quality control, which defines the necessity of the next steps; adapter removal, trimming, and filtering. After pre-processing, the data are finally analyzed using a variety of tools: differential gene expression, alternative splicing, and assessment of active synthesis, the latter requiring dedicated sample preparation. In brief, we describe the commonly used tools in the sample preparation and analysis of RNA-seq data.
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spelling pubmed-100472362023-03-29 Revealing the History and Mystery of RNA-Seq Gondane, Aishwarya Itkonen, Harri M. Curr Issues Mol Biol Review Advances in RNA-sequencing technologies have led to the development of intriguing experimental setups, a massive accumulation of data, and high demand for tools to analyze it. To answer this demand, computational scientists have developed a myriad of data analysis pipelines, but it is less often considered what the most appropriate one is. The RNA-sequencing data analysis pipeline can be divided into three major parts: data pre-processing, followed by the main and downstream analyses. Here, we present an overview of the tools used in both the bulk RNA-seq and at the single-cell level, with a particular focus on alternative splicing and active RNA synthesis analysis. A crucial part of data pre-processing is quality control, which defines the necessity of the next steps; adapter removal, trimming, and filtering. After pre-processing, the data are finally analyzed using a variety of tools: differential gene expression, alternative splicing, and assessment of active synthesis, the latter requiring dedicated sample preparation. In brief, we describe the commonly used tools in the sample preparation and analysis of RNA-seq data. MDPI 2023-02-24 /pmc/articles/PMC10047236/ /pubmed/36975490 http://dx.doi.org/10.3390/cimb45030120 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Gondane, Aishwarya
Itkonen, Harri M.
Revealing the History and Mystery of RNA-Seq
title Revealing the History and Mystery of RNA-Seq
title_full Revealing the History and Mystery of RNA-Seq
title_fullStr Revealing the History and Mystery of RNA-Seq
title_full_unstemmed Revealing the History and Mystery of RNA-Seq
title_short Revealing the History and Mystery of RNA-Seq
title_sort revealing the history and mystery of rna-seq
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047236/
https://www.ncbi.nlm.nih.gov/pubmed/36975490
http://dx.doi.org/10.3390/cimb45030120
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