COMICS: Cartoon Visualization of Omics Data in Spatial Context Using Anatomical Ontologies

COMICS is an interactive and open-access web platform for integration and visualization of molecular expression data in anatomograms of zebrafish, carp, and mouse model systems. Anatomical ontologies are used to map omics data across experiments and between an experiment and a particular visualization in a data-dependent manner. COMICS is built on top of several existing resources. Zebrafish and mouse anatomical ontologies with their controlled vocabulary (CV) and defined hierarchy are used with the ontoCAT R package to aggregate data for comparison and visualization. Libraries from the QGIS geographical information system are used with the R packages “maps” and “maptools” to visualize and interact with molecular expression data in anatomical drawings of the model systems. COMICS allows users to upload their own data from omics experiments, using any gene or protein nomenclature they wish, as long as CV terms are used to define anatomical regions or developmental stages. Common nomenclatures such as the ZFIN gene names and UniProt accessions are provided additional support. COMICS can be used to generate publication-quality visualizations of gene and protein expression across experiments. Unlike previous tools that have used anatomical ontologies to interpret imaging data in several animal models, including zebrafish, COMICS is designed to take spatially resolved data generated by dissection or fractionation and display this data in visually clear anatomical representations rather than large data tables. COMICS is optimized for ease-of-use, with a minimalistic web interface and automatic selection of the appropriate visual representation depending on the input data.

[1]  Fons J. Verbeek,et al.  Data Integration for Spatio-Temporal Patterns of Gene Expression of Zebrafish development: the GEMS database , 2008, J. Integr. Bioinform..

[2]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[3]  Lennart Martens,et al.  PRIDE: a public repository of protein and peptide identifications for the proteomics community , 2005, Nucleic Acids Res..

[4]  Andreas Vesalius De humani corporis fabrica libri septem , 1967 .

[5]  Raymond Y. N. Lee,et al.  Building a Cell and Anatomy Ontology of Caenorhabditis Elegans , 2003, Comparative and functional genomics.

[6]  S. Lewis,et al.  Uberon, an integrative multi-species anatomy ontology , 2012, Genome Biology.

[7]  Monte Westerfield,et al.  The zebrafish anatomy and stage ontologies: representing the anatomy and development of Danio rerio , 2014, Journal of Biomedical Semantics.

[8]  David Osumi-Sutherland,et al.  The Drosophila anatomy ontology , 2013, Journal of Biomedical Semantics.

[9]  Heiner Stuckenschmidt,et al.  Ontology-Based Integration of Information - A Survey of Existing Approaches , 2001, OIS@IJCAI.

[10]  Olivier Bodenreider,et al.  Aligning Representations of Anatomy using Lexical and Structural Methods , 2003, AMIA.

[11]  Yassene Mohammed,et al.  Identifying proteins in zebrafish embryos using spectral libraries generated from dissected adult organs and tissues. , 2014, Journal of proteome research.

[12]  Kerstin Howe,et al.  Comparison of the exomes of common carp (Cyprinus carpio) and zebrafish (Danio rerio). , 2012, Zebrafish.

[13]  Jonathan B. L. Bard,et al.  The AEO, an Ontology of Anatomical Entities for Classifying Animal Tissues and Organs , 2012, Front. Gene..

[14]  Dr. M. F. Wullimann,et al.  Neuroanatomy of the Zebrafish Brain , 1996, Birkhäuser Basel.

[15]  Olivier Bodenreider,et al.  Comparing the Representation of Anatomy in the FMA and SNOMED CT , 2006, AMIA.

[16]  S. Robboy,et al.  Progress in medical information management. Systematized nomenclature of medicine (SNOMED). , 1980, JAMA.

[17]  Terry F. Hayamizu,et al.  Mouse anatomy ontologies: enhancements and tools for exploring and integrating biomedical data , 2015, Mammalian Genome.

[18]  G. von Heijne,et al.  Tissue-based map of the human proteome , 2015, Science.

[19]  Nuno A. Fonseca,et al.  Expression Atlas update—an integrated database of gene and protein expression in humans, animals and plants , 2015, Nucleic Acids Res..

[20]  Morris A. Swertz,et al.  ontoCAT: an R package for ontology traversal and search , 2011, Bioinform..

[21]  Chris Mungall,et al.  Nose to tail, roots to shoots: spatial descriptors for phenotypic diversity in the Biological Spatial Ontology , 2014, J. Biomed. Semant..

[22]  Hwee Tong Tan,et al.  Subcellular fractionation methods and strategies for proteomics , 2010, Proteomics.

[23]  L. Zon,et al.  The ‘definitive’ (and ‘primitive’) guide to zebrafish hematopoiesis , 2004, Oncogene.

[24]  S. Anvar,et al.  A full-body transcriptome and proteome resource for the European common carp , 2016, BMC Genomics.

[25]  J. Paul Robinson,et al.  Integrating Cytomics and Proteomics* , 2006, Molecular & Cellular Proteomics.

[26]  B. Kuster,et al.  Mass-spectrometry-based draft of the human proteome , 2014, Nature.

[27]  C. Kimmel,et al.  Stages of embryonic development of the zebrafish , 1995, Developmental dynamics : an official publication of the American Association of Anatomists.

[28]  Steve Pettifer,et al.  EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats , 2013, Bioinform..

[29]  Herbert A. Simon,et al.  Why a diagram is (sometimes) worth 10, 000 word , 1987 .

[30]  Erik Segerdell,et al.  An ontology for Xenopus anatomy and development , 2008, BMC Developmental Biology.

[31]  G. Ortí,et al.  Multi-locus phylogenetic analysis reveals the pattern and tempo of bony fish evolution , 2013, PLoS currents.