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TRAPR: R Package for Statistical Analysis and Visualization of RNA-Seq Data

High-throughput transcriptome sequencing, also known as RNA sequencing (RNA-Seq), is a standard technology for measuring gene expression with unprecedented accuracy. Numerous bioconductor packages have been developed for the statistical analysis of RNA-Seq data. However, these tools focus on specifi...

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Detalles Bibliográficos
Autores principales: Lim, Jae Hyun, Lee, Soo Youn, Kim, Ju Han
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korea Genome Organization 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5389949/
https://www.ncbi.nlm.nih.gov/pubmed/28416950
http://dx.doi.org/10.5808/GI.2017.15.1.51
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author Lim, Jae Hyun
Lee, Soo Youn
Kim, Ju Han
author_facet Lim, Jae Hyun
Lee, Soo Youn
Kim, Ju Han
author_sort Lim, Jae Hyun
collection PubMed
description High-throughput transcriptome sequencing, also known as RNA sequencing (RNA-Seq), is a standard technology for measuring gene expression with unprecedented accuracy. Numerous bioconductor packages have been developed for the statistical analysis of RNA-Seq data. However, these tools focus on specific aspects of the data analysis pipeline, and are difficult to appropriately integrate with one another due to their disparate data structures and processing methods. They also lack visualization methods to confirm the integrity of the data and the process. In this paper, we propose an R-based RNA-Seq analysis pipeline called TRAPR, an integrated tool that facilitates the statistical analysis and visualization of RNA-Seq expression data. TRAPR provides various functions for data management, the filtering of low-quality data, normalization, transformation, statistical analysis, data visualization, and result visualization that allow researchers to build customized analysis pipelines.
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spelling pubmed-53899492017-04-17 TRAPR: R Package for Statistical Analysis and Visualization of RNA-Seq Data Lim, Jae Hyun Lee, Soo Youn Kim, Ju Han Genomics Inform Application Note High-throughput transcriptome sequencing, also known as RNA sequencing (RNA-Seq), is a standard technology for measuring gene expression with unprecedented accuracy. Numerous bioconductor packages have been developed for the statistical analysis of RNA-Seq data. However, these tools focus on specific aspects of the data analysis pipeline, and are difficult to appropriately integrate with one another due to their disparate data structures and processing methods. They also lack visualization methods to confirm the integrity of the data and the process. In this paper, we propose an R-based RNA-Seq analysis pipeline called TRAPR, an integrated tool that facilitates the statistical analysis and visualization of RNA-Seq expression data. TRAPR provides various functions for data management, the filtering of low-quality data, normalization, transformation, statistical analysis, data visualization, and result visualization that allow researchers to build customized analysis pipelines. Korea Genome Organization 2017-03 2017-03-29 /pmc/articles/PMC5389949/ /pubmed/28416950 http://dx.doi.org/10.5808/GI.2017.15.1.51 Text en Copyright © 2017 by the Korea Genome Organization http://creativecommons.org/licenses/by-nc/4.0/ It is identical to the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/).
spellingShingle Application Note
Lim, Jae Hyun
Lee, Soo Youn
Kim, Ju Han
TRAPR: R Package for Statistical Analysis and Visualization of RNA-Seq Data
title TRAPR: R Package for Statistical Analysis and Visualization of RNA-Seq Data
title_full TRAPR: R Package for Statistical Analysis and Visualization of RNA-Seq Data
title_fullStr TRAPR: R Package for Statistical Analysis and Visualization of RNA-Seq Data
title_full_unstemmed TRAPR: R Package for Statistical Analysis and Visualization of RNA-Seq Data
title_short TRAPR: R Package for Statistical Analysis and Visualization of RNA-Seq Data
title_sort trapr: r package for statistical analysis and visualization of rna-seq data
topic Application Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5389949/
https://www.ncbi.nlm.nih.gov/pubmed/28416950
http://dx.doi.org/10.5808/GI.2017.15.1.51
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