Date Published: May 15, 2019
Publisher: Public Library of Science
Author(s): Weiwei Zhang, Cheng Wang, Xuan Zhang, Alexandre G. de Brevern.
With the development of technology, an enormous amount of sequencing data is being generated rapidly. However, transforming this data into patient care is a critical challenge. There are two difficulties: how to integrate functional information into mutation interpretation and how to make the integration easy to apply. One solution is to visualize amino acid changes with protein structure and function in web app platform. There are multiple existing tools for plotting mutations, but the majority of them requires programming skills that are not common background for clinicians or researchers. Furthermore, the recurrent mutations are the focus and the recurrence cutoff varies. Yet, none of the current software offers customer-defined cutoff. Thus, we developed this user-friendly web-based tool, Mutplot (https://bioinformaticstools.shinyapps.io/lollipop/). Mutplot retrieves up-to-date domain information from the protein resource UniProt (https://www.uniprot.org/), integrates the submitted mutation information and produces lollipop diagrams with annotations and highlighted candidates. It offers flexible output options. For data that follows security standards, the app can also be hosted in web servers inside a firewall or computers without internet with Uniprot database stored on them. Altogether, Mutplot is an excellent tool for visualizing protein mutations, especially for clinicians or researchers without any bioinformatics background.
The development of sequencing technology has revolutionized cancer studies. After almost two decades of development, Next-Generation Sequencing (NGS) is fast and affordable. It has made precision medicine a clinical reality. NSG provides comprehensive big data to individualize therapies in clinical settings and expand research information. Though this technological advancement has created more opportunities for treatment and research, it has also created a problem of efficiently synthesizing and summarizing the resulting data because they are so large and detailed. Manually filtering big data increases the chance of errors and organizing it is time-consuming. Big data is also difficult to effectively present. Software circumvents all of these problems. Several tools are available for this purpose. However, most are designed for users with programming backgrounds. This excludes hospital and the majority of institution users who do not have such a training. Mutplot offers functions work in web browser and provides flexibility for easy customization. It was designed specifically for clinicians and researchers to use on their own. It translates abstract big data into visual results. In addition, Mutplot is an open source tool works in all platforms and can be easily integrated inside of firewall for security purpose.
Mutplot includes a complete workflow for visualizing various protein mutations (Fig 1). After inputting a file (tab-delimited or comma-delimited format) with variants information (the required four columns are named Hugo_Symbol, Sample_ID, Protein_Change, and Mutaiton_Type, S1 Table), Mutplot automatically connects to the most updated protein information from the UniProt  database. A total number of 409 oncogenes and tumor suppressor genes are incorporated using a drop-down menu (S2 Table). Mutplot retrieves the domain information for the selected gene. The highlight options for amino acid frequency threshold are set as 1, 2, 3, 4, 5, 10, 15, 20, 25, 30. Both genes and highlight threshold options can be expanded by simply customizing the source code. The instruction is deposited in GitHub: https://github.com/VivianBailey/Mutplot.
We showed comparisons between Mutplot and Lollipops using the same example data. Fig 2 shows the same mutation settings in Lollipops and Mutplot. Lollipops was not designed for group patients analysis. Thus, it does not provide quantitative sample frequency information. Therefore, its ability to design target therapies based on recurrent mutations is limited. Mutplot is suitable for both single patient and group patients analyses. Mutplot also displays mutation types besides domain information and amino acid alterations. This provides important clues in regard to possible ways these mutations change protein functions. For example, missense mutation substitutes one amino acid in the protein, while nonsense mutation produces a truncated protein with transformed function or no function. In addition, Mutplot addresses the overlapping annotations issue by moving the labels. See the S1 File for details regarding lollipops and Mutplot comparison.
Big data is changing the scientific landscape dramatically. It brings significant cost advantages and faster and better approaches for decision-making. With the development of sequencing technology, we are getting such a huge amount of genome information but we don’t have the matching analysis power. More and more software and packages are available, but the majority of them are run by one or more programming languages. Scientists and physicians, who eventually need to draw conclusions or make decisions, have to rely on other bioinformatics. This is time-consuming for these decision makers, especially in precise medicine. Thus, easy-to-handle big data tools are in serious need.