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ggmsa: a visual exploration tool for multiple sequence alignment and associated data.

Lang Zhou1,2, Tingze Feng1, Shuangbin Xu1

  • 1Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.

Briefings in Bioinformatics
|June 7, 2022
PubMed
Summary
This summary is machine-generated.

ggmsa is a new R package that enhances multiple sequence alignment (MSA) visualization. It aids researchers in uncovering gene function and evolutionary patterns through diverse display methods and integrated data analysis.

Keywords:
multiple sequence alignmentphylogenysequence bundlesequence recombination

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Understanding gene function relies on identifying conserved and variable regions in multiple sequence alignments (MSA).
  • Existing MSA visualizations are insufficient for the growing complexity of sequence-structure-function relationship studies.
  • There is a need for scalable visualization tools to analyze broader sequence datasets.

Purpose of the Study:

  • Introduce ggmsa, an R package designed for comprehensive analysis and visualization of multiple sequence alignments.
  • Provide researchers with advanced tools to mine sequence features and integrate associated biological data.
  • Facilitate the discovery of sequence conservation, variation, and recombination patterns.

Main Methods:

  • Developed ggmsa, an R package offering diverse visualization methods including sequence bundles, logos, stacked alignments, and comparative plots.
  • Implemented site-level analysis for conservation, variation, and recombination.
  • Integrated correlation analysis between MSA sequences and various biological data (phenotypes, ancestral sequences, structures, functions, expression levels).
  • Introduced novel visualization for genome alignments to explore inter- and intra-species variation.

Main Results:

  • ggmsa enables detailed exploration of sequence features and conservation patterns.
  • The package facilitates the integration of diverse biological data with MSA.
  • Novel visualization methods enhance the understanding of genomic variations across species.
  • ggmsa assists researchers in making informed decisions by combining visual representations with biological knowledge.

Conclusions:

  • ggmsa provides a powerful and flexible platform for MSA analysis and visualization.
  • The package supports a wide range of applications in molecular biology and genomics research.
  • ggmsa is freely available as open-source software, promoting accessibility and collaboration.