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QuasR: quantification and annotation of short reads in R

Summary: QuasR is a package for the integrated analysis of high-throughput sequencing data in R, covering all steps from read preprocessing, alignment and quality control to quantification. QuasR supports different experiment types (including RNA-seq, ChIP-seq and Bis-seq) and analysis variants (e.g...

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Detalles Bibliográficos
Autores principales: Gaidatzis, Dimos, Lerch, Anita, Hahne, Florian, Stadler, Michael B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4382904/
https://www.ncbi.nlm.nih.gov/pubmed/25417205
http://dx.doi.org/10.1093/bioinformatics/btu781
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author Gaidatzis, Dimos
Lerch, Anita
Hahne, Florian
Stadler, Michael B.
author_facet Gaidatzis, Dimos
Lerch, Anita
Hahne, Florian
Stadler, Michael B.
author_sort Gaidatzis, Dimos
collection PubMed
description Summary: QuasR is a package for the integrated analysis of high-throughput sequencing data in R, covering all steps from read preprocessing, alignment and quality control to quantification. QuasR supports different experiment types (including RNA-seq, ChIP-seq and Bis-seq) and analysis variants (e.g. paired-end, stranded, spliced and allele-specific), and is integrated in Bioconductor so that its output can be directly processed for statistical analysis and visualization. Availability and implementation: QuasR is implemented in R and C/C++. Source code and binaries for major platforms (Linux, OS X and MS Windows) are available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/QuasR.html). The package includes a ‘vignette’ with step-by-step examples for typical work flows. Contact: michael.stadler@fmi.ch Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-43829042015-04-08 QuasR: quantification and annotation of short reads in R Gaidatzis, Dimos Lerch, Anita Hahne, Florian Stadler, Michael B. Bioinformatics Applications Notes Summary: QuasR is a package for the integrated analysis of high-throughput sequencing data in R, covering all steps from read preprocessing, alignment and quality control to quantification. QuasR supports different experiment types (including RNA-seq, ChIP-seq and Bis-seq) and analysis variants (e.g. paired-end, stranded, spliced and allele-specific), and is integrated in Bioconductor so that its output can be directly processed for statistical analysis and visualization. Availability and implementation: QuasR is implemented in R and C/C++. Source code and binaries for major platforms (Linux, OS X and MS Windows) are available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/QuasR.html). The package includes a ‘vignette’ with step-by-step examples for typical work flows. Contact: michael.stadler@fmi.ch Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2015-04-01 2014-12-09 /pmc/articles/PMC4382904/ /pubmed/25417205 http://dx.doi.org/10.1093/bioinformatics/btu781 Text en © The Author 2014. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Gaidatzis, Dimos
Lerch, Anita
Hahne, Florian
Stadler, Michael B.
QuasR: quantification and annotation of short reads in R
title QuasR: quantification and annotation of short reads in R
title_full QuasR: quantification and annotation of short reads in R
title_fullStr QuasR: quantification and annotation of short reads in R
title_full_unstemmed QuasR: quantification and annotation of short reads in R
title_short QuasR: quantification and annotation of short reads in R
title_sort quasr: quantification and annotation of short reads in r
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4382904/
https://www.ncbi.nlm.nih.gov/pubmed/25417205
http://dx.doi.org/10.1093/bioinformatics/btu781
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