Abstract
BackgroundNext-generation sequencing has enabled examination of variation at the DNA sequence level and can be further enhanced by evaluation of the variants at the protein level. One powerful method is to visualize these data often revealing patterns not immediately apparent in a text version of the same data. Many investigators are interested in knowing where their amino acid changes reside within a protein. Clustering of variation within a protein versus non-clustering can show interesting aspects of the biological changes happening in disease.FindingWe describe a freely available tool, Plot Protein, executable from the command line or utilized as a graphical interface through a web browser, to enable visualization of amino acid changes at the protein level. This allows researchers to plot variation from their sequencing studies in a quick and uniform way. The features available include plotting amino acid changes, domains, post-translational modifications, reference sequence, conservation, conservation score, and also zoom capabilities. Herein we provide a case example using this tool to examine the RET protein and we demonstrate how clustering of mutations within the protein in Multiple Endocrine Neoplasia 2A (MEN2A) reveals important information about disease mechanism.ConclusionsPlot Protein is a useful tool for investigating amino acid changes and their localization within proteins. Command line and web server versions of this software are described that enable users to derive visual knowledge about their mutations.
Highlights
Next-generation sequencing has enabled examination of variation at the DNA sequence level and can be further enhanced by evaluation of the variants at the protein level
Plot Protein is a useful tool for investigating amino acid changes and their localization within proteins
Command line and web server versions of this software are described that enable users to derive visual knowledge about their mutations
Summary
Next-generation sequencing has enabled examination of variation at the DNA sequence level and can be further enhanced by evaluation of the variants at the protein level. Clustering of variation within a protein versus non-clustering can show interesting aspects of the biological changes happening in disease. Finding: We describe a freely available tool, Plot Protein, executable from the command line or utilized as a graphical interface through a web browser, to enable visualization of amino acid changes at the protein level. This allows researchers to plot variation from their sequencing studies in a quick and uniform way. We provide a case example using this tool to examine the RET protein and we demonstrate how clustering of mutations within the protein in Multiple Endocrine Neoplasia 2A (MEN2A) reveals important information about disease mechanism
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