PANDA: pathway and annotation explorer for visualizing and interpreting gene-centric data

Objective. Bringing together genomics, transcriptomics, proteomics, and other -omics technologies is an important step towards developing highly personalized medicine. However, instrumentation has advances far beyond expectations and now we are able to generate data faster than it can be interpreted. Materials and Methods. We have developed PANDA (Pathway AND Annotation) Explorer, a visualization tool that integrates gene-level annotation in the context of biological pathways to help interpret complex data from disparate sources. PANDA is a web-based application that displays data in the context of well-studied pathways like KEGG, BioCarta, and PharmGKB. PANDA represents data/annotations as icons in the graph while maintaining the other data elements (i.e., other columns for the table of annotations). Custom pathways from underrepresented diseases can be imported when existing data sources are inadequate. PANDA also allows sharing annotations among collaborators. Results. In our first use case, we show how easy it is to view supplemental data from a manuscript in the context of a user’s own data. Another use-case is provided describing how PANDA was leveraged to design a treatment strategy from the somatic variants found in the tumor of a patient with metastatic sarcomatoid renal cell carcinoma. Conclusion. PANDA facilitates the interpretation of gene-centric annotations by visually integrating this information with context of biological pathways. The application can be downloaded or used directly from our website: http://bioinformaticstools.mayo.edu/research/panda-viewer/.

[1]  Miguel Pignatelli,et al.  Database: The Journal of Biological Databases and Curation , 2016 .

[2]  Steven N. Hart,et al.  The Biological Reference Repository (BioR): a rapid and flexible system for genomics annotation , 2014, Bioinform..

[3]  Damian Smedley,et al.  The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data , 2014, Nucleic Acids Res..

[4]  R. Gibbs,et al.  Genetic alterations associated with progression from pancreatic intraepithelial neoplasia to invasive pancreatic tumor. , 2013, Gastroenterology.

[5]  Joshua F. McMichael,et al.  DGIdb - Mining the druggable genome , 2013, Nature Methods.

[6]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[7]  R. Motzer,et al.  Systemic treatment options for untreated patients with metastatic clear cell renal cancer. , 2013, Seminars in oncology.

[8]  G. Linette,et al.  Phase II study of selumetinib (sel) versus temozolomide (TMZ) in gnaq/Gna11 (Gq/11) mutant (mut) uveal melanoma (UM). , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[9]  Jing Wang,et al.  WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013 , 2013, Nucleic Acids Res..

[10]  Doron Lancet,et al.  MalaCards: an integrated compendium for diseases and their annotation , 2013, Database J. Biol. Databases Curation.

[11]  R. Jiang,et al.  Prediction of Deleterious Nonsynonymous Single-Nucleotide Polymorphism for Human Diseases , 2013, TheScientificWorldJournal.

[12]  Y. Kondo,et al.  YAP induces malignant mesothelioma cell proliferation by upregulating transcription of cell cycle-promoting genes , 2012, Oncogene.

[13]  Kenny Q. Ye,et al.  An integrated map of genetic variation from 1,092 human genomes , 2012, Nature.

[14]  R. Altman,et al.  Pharmacogenomics Knowledge for Personalized Medicine , 2012, Clinical pharmacology and therapeutics.

[15]  Susumu Goto,et al.  KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..

[16]  水野 鉄也.  YAP induces malignant mesothelioma cell proliferation by upregulating transcription of cell cycle-promoting genes , 2012 .

[17]  Robert L. Sutherland,et al.  Cyclin D as a therapeutic target in cancer , 2011, Nature Reviews Cancer.

[18]  M. DePristo,et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. , 2010, Genome research.

[19]  Yoshihiro Yamanishi,et al.  KEGG for linking genomes to life and the environment , 2007, Nucleic Acids Res..

[20]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[21]  Michael L. Creech,et al.  Integration of biological networks and gene expression data using Cytoscape , 2007, Nature Protocols.

[22]  J. Cigudosa,et al.  Cyclin D1 gene (CCND1) mutations in endometrial cancer , 2003, Oncogene.

[23]  Elizabeth M. Smigielski,et al.  dbSNP: the NCBI database of genetic variation , 2001, Nucleic Acids Res..

[24]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[25]  M. Ferguson-Smith,et al.  Somatic mutations of the von Hippel-Lindau disease tumour suppressor gene in non-familial clear cell renal carcinoma. , 1994, Human molecular genetics.

[26]  Y Kubota,et al.  Frequent somatic mutations and loss of heterozygosity of the von Hippel-Lindau tumor suppressor gene in primary human renal cell carcinomas. , 1994, Cancer research.

[27]  F. Cramer.  Somatic mutations , 1978, Nature.