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      Lollipops in the Clinic: Information Dense Mutation Plots for Precision Medicine

      research-article
      1 , 2 , * , 1 , 2
      PLoS ONE
      Public Library of Science

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          Abstract

          Introduction

          Concise visualization is critical to present large amounts of information in a minimal space that can be interpreted quickly. Clinical applications in precision medicine present an important use case due to the time dependent nature of the interpretations, although visualization is increasingly necessary across the life sciences. In this paper we describe the Lollipops software for the presentation of panel or exome sequencing results. Source code and binaries are freely available at https://github.com/pbnjay/lollipops. Although other software and web resources exist to produce lollipop diagrams, these packages are less suited to clinical applications. The demands of precision medicine require the ability to easily fit into a workflow and incorporate external information without manual intervention.

          Results

          The Lollipops software provides a simple command line interface that only requires an official gene symbol and mutation list making it easily scriptable. External information is integrated using the publicly available Uniprot and Pfam resources. Heuristics are used to select the most informative components and condense them for a concise plot. The output is a flexible Scalable Vector Graphic (SVG) diagram that can be displayed in a web page or graphic illustration tool.

          Conclusion

          The Lollipops software creates information-dense, publication-quality mutation plots for automated pipelines and high-throughput workflows in precision medicine. The automatic data integration enables clinical data security, and visualization heuristics concisely present knowledge with minimal user configuration.

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          Most cited references3

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          The design and evaluation of a graphical display for laboratory data.

          Advances in healthcare information technology have provided opportunities to present data in new, more effective ways. In this study, we designed a laboratory display that features small, data-dense graphics called sparklines, which have recently been promoted as effective representations of medical data but have not been well studied. The effect of this novel display on physicians' interpretation of data was investigated. Twelve physicians talked aloud as they assessed laboratory data from four patients in a pediatric intensive care unit with the graph display and with a conventional table display. Verbalizations were coded based on the abnormal values and trends identified for each lab variable. The correspondence of interpretations of variables in each display was evaluated, and patterns were investigated across participants. Assessment time was also analyzed. Physicians completed assessments significantly faster with the graphical display (3.6 min vs 4.4 min, p=0.042). When compared across displays, 37% of interpretations did not match. Graphs were more useful when the visual cues in tables did not provide trend information, while slightly abnormal values were easier to identify with tables. Data presentation format can affect how physicians interpret laboratory data. Graphic displays have several advantages over numeric displays but are not always optimal. User, task and data characteristics should be considered when designing information displays.
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            Prediction of individualized therapeutic vulnerabilities in cancer from genomic profiles

            Motivation: Somatic homozygous deletions of chromosomal regions in cancer, while not necessarily oncogenic, may lead to therapeutic vulnerabilities specific to cancer cells compared with normal cells. A recently reported example is the loss of one of the two isoenzymes in glioblastoma cancer cells such that the use of a specific inhibitor selectively inhibited growth of the cancer cells, which had become fully dependent on the second isoenzyme. We have now made use of the unprecedented conjunction of large-scale cancer genomics profiling of tumor samples in The Cancer Genome Atlas (TCGA) and of tumor-derived cell lines in the Cancer Cell Line Encyclopedia, as well as the availability of integrated pathway information systems, such as Pathway Commons, to systematically search for a comprehensive set of such epistatic vulnerabilities. Results: Based on homozygous deletions affecting metabolic enzymes in 16 TCGA cancer studies and 972 cancer cell lines, we identified 4104 candidate metabolic vulnerabilities present in 1019 tumor samples and 482 cell lines. Up to 44% of these vulnerabilities can be targeted with at least one Food and Drug Administration-approved drug. We suggest focused experiments to test these vulnerabilities and clinical trials based on personalized genomic profiles of those that pass preclinical filters. We conclude that genomic profiling will in the future provide a promising basis for network pharmacology of epistatic vulnerabilities as a promising therapeutic strategy. Availability and implementation: A web-based tool for exploring all vulnerabilities and their details is available at http://cbio.mskcc.org/cancergenomics/statius/ along with supplemental data files. Contact: statius@cbio.mskcc.org Supplementary information: Supplementary data are available at Bioinformatics online.
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              Plot protein: visualization of mutations

              Background 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. 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. 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. 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. Conclusions 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.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2016
                4 August 2016
                : 11
                : 8
                : e0160519
                Affiliations
                [1 ]Bioinformatics Services Division, North Carolina Research Campus, Kannapolis, North Carolina, United States of America
                [2 ]Bioinformatics and Genomics Department, University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
                National Cancer Institute, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceived and designed the experiments: JJ CB.

                • Performed the experiments: JJ.

                • Analyzed the data: JJ.

                • Contributed reagents/materials/analysis tools: JJ.

                • Wrote the paper: JJ CB.

                • Designed and wrote software: JJ.

                Author information
                http://orcid.org/0000-0002-5761-7533
                Article
                PONE-D-16-18096
                10.1371/journal.pone.0160519
                4973895
                27490490
                ff9bca68-afdf-4b35-89ca-c1b9634af8ff
                © 2016 Jay, Brouwer

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 4 May 2016
                : 20 July 2016
                Page count
                Figures: 1, Tables: 1, Pages: 6
                Funding
                The authors have no support or funding to report.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Domains
                Computer and Information Sciences
                Software Engineering
                Software Tools
                Engineering and Technology
                Software Engineering
                Software Tools
                Computer and Information Sciences
                Computer Software
                Research and Analysis Methods
                Database and Informatics Methods
                Biological Databases
                Mutation Databases
                Biology and Life Sciences
                Genetics
                Mutation
                Mutation Databases
                Computer and Information Sciences
                Data Visualization
                Computer and Information Sciences
                Computer Networks
                Internet
                Computer and Information Sciences
                Software Engineering
                Source Code
                Engineering and Technology
                Software Engineering
                Source Code
                Computer and Information Sciences
                Computer Architecture
                Pipelines (Computing)
                Graphics Pipelines
                Custom metadata
                All code and binary releases may be found at http://github.com/pbnjay/lollipops.

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                Uncategorized

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