The sequence alignment visualization of large gene sets analyzed with the AlignStatPlot package.

The sequence alignment visualization of large gene sets analyzed with the AlignStatPlot package.</p

S5 Fig -

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.</div

Based on the results of the MSA study, the PCA plot was created for certain SNPs using the AlignStatPlot tool.

Based on the results of the MSA study, the PCA plot was created for certain SNPs using the AlignStatPlot tool.</p

Sequence similarity matrix of the studied case study data with tree of group B.

Correlation of sequence similarity generated using MSA analysis combined with phylogenetic tree group B. (JPG)</p

Some sequence alignment statistics visualization were generated using AlignStatPlot, both with online and local tools.

The figures include (A) MSA analysis results for a low number of sequences (15 sequences) and (B) for a large number of sequences (15–300 sequences), showing shared regions between aligned sequences. Additionally, (C) displays nucleotide frequency across the MSA, (D) represents the heatmap of the sequence dissimilarity matrix, (E) integrates the phylogenetic tree with sequence annotation, (F) showcases the PCA analysis performed on the studied samples using their sequence variation, and (G) presents nucleotide frequency across the MSA. Furthermore, there is a clustering analysis of MSA-generated SNPs visualized as PCA (H), and their location on gene sequences combined with the phylogenetic tree (I).</p

The PCA plot constructed for some genes with the AlignStatPlot package based on the findings of the MSA analysis of genetic variation.

The PCA plot constructed for some genes with the AlignStatPlot package based on the findings of the MSA analysis of genetic variation.</p

Information about the sequences used to validate the AlignStatPlot package.

Information about the sequences used to validate the AlignStatPlot package.</p

Sequence similarity matrix of the studied case study data with tree of group A.

Correlation of sequence similarity generated using MSA analysis combined with phylogenetic tree group A. (JPG)</p

The AlignStatPlot flowchart illustrates the analysis workflow, showcasing the network of steps involved, as well as the possible input options and expected results and visualizations.

The AlignStatPlot flowchart illustrates the analysis workflow, showcasing the network of steps involved, as well as the possible input options and expected results and visualizations.</p

An overview of the data analysis.

An overview of the data analysis findings that were used to verify the alignstatplot tool. (DOCX)</p