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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7584020 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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