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TSSr: an R package for comprehensive analyses of TSS sequencing data

Transcription initiation is regulated in a highly organized fashion to ensure proper cellular functions. Accurate identification of transcription start sites (TSSs) and quantitative characterization of transcription initiation activities are fundamental steps for studies of regulated transcriptions...

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Autores principales: Lu, Zhaolian, Berry, Keenan, Hu, Zhenbin, Zhan, Yu, Ahn, Tae-Hyuk, Lin, Zhenguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8598296/
https://www.ncbi.nlm.nih.gov/pubmed/34805991
http://dx.doi.org/10.1093/nargab/lqab108
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author Lu, Zhaolian
Berry, Keenan
Hu, Zhenbin
Zhan, Yu
Ahn, Tae-Hyuk
Lin, Zhenguo
author_facet Lu, Zhaolian
Berry, Keenan
Hu, Zhenbin
Zhan, Yu
Ahn, Tae-Hyuk
Lin, Zhenguo
author_sort Lu, Zhaolian
collection PubMed
description Transcription initiation is regulated in a highly organized fashion to ensure proper cellular functions. Accurate identification of transcription start sites (TSSs) and quantitative characterization of transcription initiation activities are fundamental steps for studies of regulated transcriptions and core promoter structures. Several high-throughput techniques have been developed to sequence the very 5′end of RNA transcripts (TSS sequencing) on the genome scale. Bioinformatics tools are essential for processing, analysis, and visualization of TSS sequencing data. Here, we present TSSr, an R package that provides rich functions for mapping TSS and characterizations of structures and activities of core promoters based on all types of TSS sequencing data. Specifically, TSSr implements several newly developed algorithms for accurately identifying TSSs from mapped sequencing reads and inference of core promoters, which are a prerequisite for subsequent functional analyses of TSS data. Furthermore, TSSr also enables users to export various types of TSS data that can be visualized by genome browser for inspection of promoter activities in association with other genomic features, and to generate publication-ready TSS graphs. These user-friendly features could greatly facilitate studies of transcription initiation based on TSS sequencing data. The source code and detailed documentations of TSSr can be freely accessed at https://github.com/Linlab-slu/TSSr.
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spelling pubmed-85982962021-11-18 TSSr: an R package for comprehensive analyses of TSS sequencing data Lu, Zhaolian Berry, Keenan Hu, Zhenbin Zhan, Yu Ahn, Tae-Hyuk Lin, Zhenguo NAR Genom Bioinform Standard Article Transcription initiation is regulated in a highly organized fashion to ensure proper cellular functions. Accurate identification of transcription start sites (TSSs) and quantitative characterization of transcription initiation activities are fundamental steps for studies of regulated transcriptions and core promoter structures. Several high-throughput techniques have been developed to sequence the very 5′end of RNA transcripts (TSS sequencing) on the genome scale. Bioinformatics tools are essential for processing, analysis, and visualization of TSS sequencing data. Here, we present TSSr, an R package that provides rich functions for mapping TSS and characterizations of structures and activities of core promoters based on all types of TSS sequencing data. Specifically, TSSr implements several newly developed algorithms for accurately identifying TSSs from mapped sequencing reads and inference of core promoters, which are a prerequisite for subsequent functional analyses of TSS data. Furthermore, TSSr also enables users to export various types of TSS data that can be visualized by genome browser for inspection of promoter activities in association with other genomic features, and to generate publication-ready TSS graphs. These user-friendly features could greatly facilitate studies of transcription initiation based on TSS sequencing data. The source code and detailed documentations of TSSr can be freely accessed at https://github.com/Linlab-slu/TSSr. Oxford University Press 2021-11-12 /pmc/articles/PMC8598296/ /pubmed/34805991 http://dx.doi.org/10.1093/nargab/lqab108 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Standard Article
Lu, Zhaolian
Berry, Keenan
Hu, Zhenbin
Zhan, Yu
Ahn, Tae-Hyuk
Lin, Zhenguo
TSSr: an R package for comprehensive analyses of TSS sequencing data
title TSSr: an R package for comprehensive analyses of TSS sequencing data
title_full TSSr: an R package for comprehensive analyses of TSS sequencing data
title_fullStr TSSr: an R package for comprehensive analyses of TSS sequencing data
title_full_unstemmed TSSr: an R package for comprehensive analyses of TSS sequencing data
title_short TSSr: an R package for comprehensive analyses of TSS sequencing data
title_sort tssr: an r package for comprehensive analyses of tss sequencing data
topic Standard Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8598296/
https://www.ncbi.nlm.nih.gov/pubmed/34805991
http://dx.doi.org/10.1093/nargab/lqab108
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