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BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis
BACKGROUND: RNA sequencing is an indispensable research tool used in a broad range of transcriptome analysis studies. The most common application of RNA Sequencing is differential expression analysis and it is used to determine genetic loci with distinct expression across different conditions. An em...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7761193/ https://www.ncbi.nlm.nih.gov/pubmed/33391870 http://dx.doi.org/10.7717/peerj.10469 |
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author | Dimitrov, Daniel Gu, Quan |
author_facet | Dimitrov, Daniel Gu, Quan |
author_sort | Dimitrov, Daniel |
collection | PubMed |
description | BACKGROUND: RNA sequencing is an indispensable research tool used in a broad range of transcriptome analysis studies. The most common application of RNA Sequencing is differential expression analysis and it is used to determine genetic loci with distinct expression across different conditions. An emerging field called single-cell RNA sequencing is used for transcriptome profiling at the individual cell level. The standard protocols for both of these approaches include the processing of sequencing libraries and result in the generation of count matrices. An obstacle to these analyses and the acquisition of meaningful results is that they require programing expertise. Although some effort has been directed toward the development of user-friendly RNA-Seq analysis analysis tools, few have the flexibility to explore both Bulk and single-cell RNA sequencing. IMPLEMENTATION: BingleSeq was developed as an intuitive application that provides a user-friendly solution for the analysis of count matrices produced by both Bulk and Single-cell RNA-Seq experiments. This was achieved by building an interactive dashboard-like user interface which incorporates three state-of-the-art software packages for each type of the aforementioned analyses. Furthermore, BingleSeq includes additional features such as visualization techniques, extensive functional annotation analysis and rank-based consensus for differential gene analysis results. As a result, BingleSeq puts some of the best reviewed and most widely used packages and tools for RNA-Seq analyses at the fingertips of biologists with no programing experience. AVAILABILITY: BingleSeq is as an easy-to-install R package available on GitHub at https://github.com/dbdimitrov/BingleSeq/. |
format | Online Article Text |
id | pubmed-7761193 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77611932020-12-31 BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis Dimitrov, Daniel Gu, Quan PeerJ Bioinformatics BACKGROUND: RNA sequencing is an indispensable research tool used in a broad range of transcriptome analysis studies. The most common application of RNA Sequencing is differential expression analysis and it is used to determine genetic loci with distinct expression across different conditions. An emerging field called single-cell RNA sequencing is used for transcriptome profiling at the individual cell level. The standard protocols for both of these approaches include the processing of sequencing libraries and result in the generation of count matrices. An obstacle to these analyses and the acquisition of meaningful results is that they require programing expertise. Although some effort has been directed toward the development of user-friendly RNA-Seq analysis analysis tools, few have the flexibility to explore both Bulk and single-cell RNA sequencing. IMPLEMENTATION: BingleSeq was developed as an intuitive application that provides a user-friendly solution for the analysis of count matrices produced by both Bulk and Single-cell RNA-Seq experiments. This was achieved by building an interactive dashboard-like user interface which incorporates three state-of-the-art software packages for each type of the aforementioned analyses. Furthermore, BingleSeq includes additional features such as visualization techniques, extensive functional annotation analysis and rank-based consensus for differential gene analysis results. As a result, BingleSeq puts some of the best reviewed and most widely used packages and tools for RNA-Seq analyses at the fingertips of biologists with no programing experience. AVAILABILITY: BingleSeq is as an easy-to-install R package available on GitHub at https://github.com/dbdimitrov/BingleSeq/. PeerJ Inc. 2020-12-22 /pmc/articles/PMC7761193/ /pubmed/33391870 http://dx.doi.org/10.7717/peerj.10469 Text en © 2020 Dimitrov and Gu https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Dimitrov, Daniel Gu, Quan BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis |
title | BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis |
title_full | BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis |
title_fullStr | BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis |
title_full_unstemmed | BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis |
title_short | BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis |
title_sort | bingleseq: a user-friendly r package for bulk and single-cell rna-seq data analysis |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7761193/ https://www.ncbi.nlm.nih.gov/pubmed/33391870 http://dx.doi.org/10.7717/peerj.10469 |
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