AI Article Synopsis
- Multiple sequence alignment (MSA) is crucial for studying genetic variations linked to traits in organisms, but post-analysis can be tough for non-programmers, especially with large data sets.
- Currently, there are no tools for comprehensive post-MSA analysis that integrate gene data with annotations for effective clustering.
- "AlignStatPlot" is a new, user-friendly R package and online tool designed for MSA and subsequent analysis, offering improved visualization and efficiency compared to existing options, allowing researchers to better communicate their results.
Article Abstract
Multiple sequence alignment (MSA) is essential for understanding genetic variations controlling phenotypic traits in all living organisms. The post-analysis of MSA results is a difficult step for researchers who do not have programming skills. Especially those working with large scale data and looking for potential variations or variable sample groups. Generating bi-allelic data and the comparison of wild and alternative gene forms are important steps in population genetics. Customising MSA visualisation for a single page view is difficult, making viewing potential indels and variations challenging. There are currently no bioinformatics tools that permit post-MSA analysis, in which data on gene and single nucleotide scales could be combined with gene annotations and used for cluster analysis. We introduce "AlignStatPlot," a new R package and online tool that is well-documented and easy-to use for MSA and post-MSA analysis. This tool performs both traditional and cutting-edge analyses on sequencing data and generates new visualisation methods for MSA results. When compared to currently available tools, AlignStatPlot provides a robust ability to handle and visualise diversity data, while the online version will save time and encourage researchers to focus on explaining their findings. It is a simple tool that can be used in conjunction with population genetics software.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511070 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0291204 | PLOS |
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