» Articles » PMID: 29036507

Ggseqlogo: a Versatile R Package for Drawing Sequence Logos

Overview
Journal Bioinformatics
Specialty Biology
Date 2017 Oct 17
PMID 29036507
Citations 425
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Cholesterol efflux protein, ABCA1, supports anti-cancer functions of myeloid immune cells.

Bendre S, Wang Y, Hajyousif B, K C R, Rajendra K, Bhogale S bioRxiv. 2025; .

PMID: 40027727 PMC: 11870514. DOI: 10.1101/2025.02.19.638515.


The endogenous antigen-specific CD8 T cell repertoire is composed of unbiased and biased clonotypes with differential fate commitments.

Abdullah L, Emiliani F, Vaidya C, Stuart H, Musial S, Kolling F Immunity. 2025; 58(3):601-615.e9.

PMID: 40020673 PMC: 11903169. DOI: 10.1016/j.immuni.2025.02.001.


Structural insights into spliceosome fidelity: DHX35-GPATCH1- mediated rejection of aberrant splicing substrates.

Li Y, Fischer P, Wang M, Zhou Q, Song A, Yuan R Cell Res. 2025; .

PMID: 40016598 DOI: 10.1038/s41422-025-01084-w.


Reconstitution of SPO11-dependent double-strand break formation.

Zheng Z, Zheng L, Arter M, Liu K, Yamada S, Ontoso D Nature. 2025; .

PMID: 39972129 DOI: 10.1038/s41586-025-08601-2.


Probabilistic and machine-learning methods for predicting local rates of transcription elongation from nascent RNA sequencing data.

Liu L, Zhao Y, Hassett R, Toneyan S, Koo P, Siepel A Nucleic Acids Res. 2025; 53(4).

PMID: 39964478 PMC: 11833694. DOI: 10.1093/nar/gkaf092.