Cargando…
clipplotr—a comparative visualization and analysis tool for CLIP data
CLIP technologies are now widely used to study RNA–protein interactions and many data sets are now publicly available. An important first step in CLIP data exploration is the visual inspection and assessment of processed genomic data on selected genes or regions and performing comparisons: either ac...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187674/ https://www.ncbi.nlm.nih.gov/pubmed/36894192 http://dx.doi.org/10.1261/rna.079326.122 |
_version_ | 1785042779872690176 |
---|---|
author | Chakrabarti, Anob M. Capitanchik, Charlotte Ule, Jernej Luscombe, Nicholas M. |
author_facet | Chakrabarti, Anob M. Capitanchik, Charlotte Ule, Jernej Luscombe, Nicholas M. |
author_sort | Chakrabarti, Anob M. |
collection | PubMed |
description | CLIP technologies are now widely used to study RNA–protein interactions and many data sets are now publicly available. An important first step in CLIP data exploration is the visual inspection and assessment of processed genomic data on selected genes or regions and performing comparisons: either across conditions within a particular project, or incorporating publicly available data. However, the output files produced by data processing pipelines or preprocessed files available to download from data repositories are often not suitable for direct comparison and usually need further processing. Furthermore, to derive biological insight it is usually necessary to visualize a CLIP signal alongside other data such as annotations, or orthogonal functional genomic data (e.g., RNA-seq). We have developed a simple, but powerful, command-line tool: clipplotr, which facilitates these visual comparative and integrative analyses with normalization and smoothing options for CLIP data and the ability to show these alongside reference annotation tracks and functional genomic data. These data can be supplied as input to clipplotr in a range of file formats, which will output a publication quality figure. It is written in R and can both run on a laptop computer independently or be integrated into computational workflows on a high-performance cluster. Releases, source code, and documentation are freely available at https://github.com/ulelab/clipplotr. |
format | Online Article Text |
id | pubmed-10187674 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-101876742023-06-01 clipplotr—a comparative visualization and analysis tool for CLIP data Chakrabarti, Anob M. Capitanchik, Charlotte Ule, Jernej Luscombe, Nicholas M. RNA Bioinformatics CLIP technologies are now widely used to study RNA–protein interactions and many data sets are now publicly available. An important first step in CLIP data exploration is the visual inspection and assessment of processed genomic data on selected genes or regions and performing comparisons: either across conditions within a particular project, or incorporating publicly available data. However, the output files produced by data processing pipelines or preprocessed files available to download from data repositories are often not suitable for direct comparison and usually need further processing. Furthermore, to derive biological insight it is usually necessary to visualize a CLIP signal alongside other data such as annotations, or orthogonal functional genomic data (e.g., RNA-seq). We have developed a simple, but powerful, command-line tool: clipplotr, which facilitates these visual comparative and integrative analyses with normalization and smoothing options for CLIP data and the ability to show these alongside reference annotation tracks and functional genomic data. These data can be supplied as input to clipplotr in a range of file formats, which will output a publication quality figure. It is written in R and can both run on a laptop computer independently or be integrated into computational workflows on a high-performance cluster. Releases, source code, and documentation are freely available at https://github.com/ulelab/clipplotr. Cold Spring Harbor Laboratory Press 2023-06 /pmc/articles/PMC10187674/ /pubmed/36894192 http://dx.doi.org/10.1261/rna.079326.122 Text en © 2023 Chakrabarti et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society https://creativecommons.org/licenses/by/4.0/This article, published in RNA, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Bioinformatics Chakrabarti, Anob M. Capitanchik, Charlotte Ule, Jernej Luscombe, Nicholas M. clipplotr—a comparative visualization and analysis tool for CLIP data |
title | clipplotr—a comparative visualization and analysis tool for CLIP data |
title_full | clipplotr—a comparative visualization and analysis tool for CLIP data |
title_fullStr | clipplotr—a comparative visualization and analysis tool for CLIP data |
title_full_unstemmed | clipplotr—a comparative visualization and analysis tool for CLIP data |
title_short | clipplotr—a comparative visualization and analysis tool for CLIP data |
title_sort | clipplotr—a comparative visualization and analysis tool for clip data |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187674/ https://www.ncbi.nlm.nih.gov/pubmed/36894192 http://dx.doi.org/10.1261/rna.079326.122 |
work_keys_str_mv | AT chakrabartianobm clipplotracomparativevisualizationandanalysistoolforclipdata AT capitanchikcharlotte clipplotracomparativevisualizationandanalysistoolforclipdata AT ulejernej clipplotracomparativevisualizationandanalysistoolforclipdata AT luscombenicholasm clipplotracomparativevisualizationandanalysistoolforclipdata |