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Streamlining Quantitative Analysis of Long RNA Sequencing Reads

Transcriptome analyses allow for linking RNA expression profiles to cellular pathways and phenotypes. Despite improvements in sequencing methodology, whole transcriptome analyses are still tedious, especially for methodologies producing long reads. Currently, available data analysis software often l...

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Detalles Bibliográficos
Autores principales: Oeck, Sebastian, Tüns, Alicia I., Hurst, Sebastian, Schramm, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584020/
https://www.ncbi.nlm.nih.gov/pubmed/33019615
http://dx.doi.org/10.3390/ijms21197259
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author Oeck, Sebastian
Tüns, Alicia I.
Hurst, Sebastian
Schramm, Alexander
author_facet Oeck, Sebastian
Tüns, Alicia I.
Hurst, Sebastian
Schramm, Alexander
author_sort Oeck, Sebastian
collection PubMed
description Transcriptome analyses allow for linking RNA expression profiles to cellular pathways and phenotypes. Despite improvements in sequencing methodology, whole transcriptome analyses are still tedious, especially for methodologies producing long reads. Currently, available data analysis software often lacks cost- and time-efficient workflows. Although kit-based workflows and benchtop platforms for RNA sequencing provide software options, e.g., cloud-based tools to analyze basecalled reads, quantitative, and easy-to-use solutions for transcriptome analysis, especially for non-human data, are missing. We therefore developed a user-friendly tool, termed Alignator, for rapid analysis of long RNA reads requiring only FASTQ files and an Ensembl cDNA database reference. After successful mapping, Alignator generates quantitative information for each transcript and provides a table in which sequenced and aligned RNA are stored for further comparative analyses.
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spelling pubmed-75840202020-10-29 Streamlining Quantitative Analysis of Long RNA Sequencing Reads Oeck, Sebastian Tüns, Alicia I. Hurst, Sebastian Schramm, Alexander Int J Mol Sci Communication Transcriptome analyses allow for linking RNA expression profiles to cellular pathways and phenotypes. Despite improvements in sequencing methodology, whole transcriptome analyses are still tedious, especially for methodologies producing long reads. Currently, available data analysis software often lacks cost- and time-efficient workflows. Although kit-based workflows and benchtop platforms for RNA sequencing provide software options, e.g., cloud-based tools to analyze basecalled reads, quantitative, and easy-to-use solutions for transcriptome analysis, especially for non-human data, are missing. We therefore developed a user-friendly tool, termed Alignator, for rapid analysis of long RNA reads requiring only FASTQ files and an Ensembl cDNA database reference. After successful mapping, Alignator generates quantitative information for each transcript and provides a table in which sequenced and aligned RNA are stored for further comparative analyses. MDPI 2020-10-01 /pmc/articles/PMC7584020/ /pubmed/33019615 http://dx.doi.org/10.3390/ijms21197259 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Oeck, Sebastian
Tüns, Alicia I.
Hurst, Sebastian
Schramm, Alexander
Streamlining Quantitative Analysis of Long RNA Sequencing Reads
title Streamlining Quantitative Analysis of Long RNA Sequencing Reads
title_full Streamlining Quantitative Analysis of Long RNA Sequencing Reads
title_fullStr Streamlining Quantitative Analysis of Long RNA Sequencing Reads
title_full_unstemmed Streamlining Quantitative Analysis of Long RNA Sequencing Reads
title_short Streamlining Quantitative Analysis of Long RNA Sequencing Reads
title_sort streamlining quantitative analysis of long rna sequencing reads
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584020/
https://www.ncbi.nlm.nih.gov/pubmed/33019615
http://dx.doi.org/10.3390/ijms21197259
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