Contribution to the knowledge of the genetic diversity and taxonomy of some Iranian Trifolium species

Haerinasab, Maryam; Eslami‐Farouji, Atena

doi:10.1007/s10722-021-01254-w

Cited by 2 publications

(1 citation statement)

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“…Similar methods have been used to study the genetic diversity and evolution of viruses by comparing viral sequences of different historical strains using MSA and clustering analysis [23]. This method is widely used in gene-based sequencing to study gene diversity in plants [24]. PCA analysis is a common strategy for studying correlated genetic elements in samples.…”

Section: Resultsmentioning

confidence: 99%

AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data

Alsamman,

El Allali,

Mokhtar

et al. 2023

PLoS ONE

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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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Section: Resultsmentioning

confidence: 99%