Omar Wagih1. 1. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
Abstract
SUMMARY: Sequence logos have become a crucial visualization method for studying underlying sequence patterns in the genome. Despite this, there remains a scarcity of software packages that provide the versatility often required for such visualizations. ggseqlogo is an R package built on the ggplot2 package that aims to address this issue. ggseqlogo offers native illustration of publication-ready DNA, RNA and protein sequence logos in a highly customizable fashion with features including multi-logo plots, qualitative and quantitative colour schemes, annotation of logos and integration with other plots. The package is intuitive to use and seamlessly integrates into R analysis pipelines. AVAILABILITY AND IMPLEMENTATION: ggseqlogo is released under the GNU licence and is freely available via CRAN-The Comprehensive R Archive Network https://cran.r-project.org/web/packages/ggseqlogo. A detailed tutorial can be found at https://omarwagih.github.io/ggseqlogo. CONTACT: wagih@ebi.ac.uk.
SUMMARY: Sequence logos have become a crucial visualization method for studying underlying sequence patterns in the genome. Despite this, there remains a scarcity of software packages that provide the versatility often required for such visualizations. ggseqlogo is an R package built on the ggplot2 package that aims to address this issue. ggseqlogo offers native illustration of publication-ready DNA, RNA and protein sequence logos in a highly customizable fashion with features including multi-logo plots, qualitative and quantitative colour schemes, annotation of logos and integration with other plots. The package is intuitive to use and seamlessly integrates into R analysis pipelines. AVAILABILITY AND IMPLEMENTATION: ggseqlogo is released under the GNU licence and is freely available via CRAN-The Comprehensive R Archive Network https://cran.r-project.org/web/packages/ggseqlogo. A detailed tutorial can be found at https://omarwagih.github.io/ggseqlogo. CONTACT: wagih@ebi.ac.uk.
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