Ggmsa: a visual exploration tool for multiple sequence alignment and associated data. Issue 4 (8th June 2022)

• Record Type: Journal Article
• Authors: Zhou, Lang; Feng, Tingze; Xu, Shuangbin; Gao, Fangluan; Lam, Tommy T; Wang, Qianwen; Wu, Tianzhi; Huang, Huina; Zhan, Li; Li, Lin; Guan, Yi; Dai, Zehan; Yu, Guangchuang
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 4(2022)
• Publication Date: 2022-06-08
• Subjects: multiple sequence alignment -- sequence bundle -- sequence recombination -- phylogeny; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac222
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: The identification of the conserved and variable regions in the multiple sequence alignment (MSA) is critical to accelerating the process of understanding the function of genes. MSA visualizations allow us to transform sequence features into understandable visual representations. As the sequence–structure–function relationship gains increasing attention in molecular biology studies, the simple display of nucleotide or protein sequence alignment is not satisfied. A more scalable visualization is required to broaden the scope of sequence investigation. Here we present ggmsa, an R package for mining comprehensive sequence features and integrating the associated data of MSA by a variety of display methods. To uncover sequence conservation patterns, variations and recombination at the site level, sequence bundles, sequence logos, stacked sequence alignment and comparative plots are implemented. ggmsa supports integrating the correlation of MSA sequences and their phenotypes, as well as other traits such as ancestral sequences, molecular structures, molecular functions and expression levels. We also design a new visualization method for genome alignments in multiple alignment format to explore the pattern of within and between species variation. Combining these visual representations with prime knowledge, ggmsa assists researchers in discovering MSA and making decisions. The ggmsa package is open-source software released under the Artistic-2.0 license, and it is freely available on Bioconductor (https://bioconductor.org/packages/ggmsa ) and Github (https://github.com/YuLab-SMU/ggmsa ).