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RgnTX: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity

Colocalization analysis of genomic region sets has been widely adopted to unveil potential functional interactions between corresponding biological attributes, which often serves as the basis for further investigation. A number of methods have been developed for colocalization analysis of genomic el...

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Detalles Bibliográficos
Autores principales: Wang, Yue, Wei, Zhen, Su, Jionglong, Coenen, Frans, Meng, Jia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475473/
https://www.ncbi.nlm.nih.gov/pubmed/37671241
http://dx.doi.org/10.1016/j.csbj.2023.08.021
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author Wang, Yue
Wei, Zhen
Su, Jionglong
Coenen, Frans
Meng, Jia
author_facet Wang, Yue
Wei, Zhen
Su, Jionglong
Coenen, Frans
Meng, Jia
author_sort Wang, Yue
collection PubMed
description Colocalization analysis of genomic region sets has been widely adopted to unveil potential functional interactions between corresponding biological attributes, which often serves as the basis for further investigation. A number of methods have been developed for colocalization analysis of genomic elements. However, none of them explicitly considered the transcriptome heterogeneity and isoform ambiguity, making them less appropriate for analyzing transcriptome elements. Here, we developed RgnTX, an R/Bioconductor tool for the colocalization analysis of transcriptome elements with permutation tests. Different from existing approaches, RgnTX directly takes advantage of transcriptome annotation, and offers high flexibility in the null model to simulate realistic transcriptome-wide background, such as the complex alternative splicing patterns. Importantly, it supports the testing of transcriptome elements without clear isoform association, which is often the real scenario due to technical limitations. Proposed package offers a wide selection of pre-defined functions, easy to be utilized by users for visualizing permutation results, calculating shifted z-scores and conducting multiple hypothesis testing under Benjamini-Hochberg correction. Moreover, with synthetic and real datasets, we show that RgnTX novel testing modes return distinct and more significant results compared to existing genome-based methods. We believe RgnTX should make a useful tool to characterize the randomness of the transcriptome, and for conducting statistical association analysis for genomic region sets within the heterogeneous transcriptome. The package now has been accepted by Bioconductor and is freely available at: https://bioconductor.org/packages/RgnTX.
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spelling pubmed-104754732023-09-05 RgnTX: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity Wang, Yue Wei, Zhen Su, Jionglong Coenen, Frans Meng, Jia Comput Struct Biotechnol J Software/Web server Article Colocalization analysis of genomic region sets has been widely adopted to unveil potential functional interactions between corresponding biological attributes, which often serves as the basis for further investigation. A number of methods have been developed for colocalization analysis of genomic elements. However, none of them explicitly considered the transcriptome heterogeneity and isoform ambiguity, making them less appropriate for analyzing transcriptome elements. Here, we developed RgnTX, an R/Bioconductor tool for the colocalization analysis of transcriptome elements with permutation tests. Different from existing approaches, RgnTX directly takes advantage of transcriptome annotation, and offers high flexibility in the null model to simulate realistic transcriptome-wide background, such as the complex alternative splicing patterns. Importantly, it supports the testing of transcriptome elements without clear isoform association, which is often the real scenario due to technical limitations. Proposed package offers a wide selection of pre-defined functions, easy to be utilized by users for visualizing permutation results, calculating shifted z-scores and conducting multiple hypothesis testing under Benjamini-Hochberg correction. Moreover, with synthetic and real datasets, we show that RgnTX novel testing modes return distinct and more significant results compared to existing genome-based methods. We believe RgnTX should make a useful tool to characterize the randomness of the transcriptome, and for conducting statistical association analysis for genomic region sets within the heterogeneous transcriptome. The package now has been accepted by Bioconductor and is freely available at: https://bioconductor.org/packages/RgnTX. Research Network of Computational and Structural Biotechnology 2023-08-24 /pmc/articles/PMC10475473/ /pubmed/37671241 http://dx.doi.org/10.1016/j.csbj.2023.08.021 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Software/Web server Article
Wang, Yue
Wei, Zhen
Su, Jionglong
Coenen, Frans
Meng, Jia
RgnTX: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity
title RgnTX: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity
title_full RgnTX: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity
title_fullStr RgnTX: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity
title_full_unstemmed RgnTX: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity
title_short RgnTX: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity
title_sort rgntx: colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity
topic Software/Web server Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475473/
https://www.ncbi.nlm.nih.gov/pubmed/37671241
http://dx.doi.org/10.1016/j.csbj.2023.08.021
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