Disease Ontology and disease concept data. The latest version of DO has close to 2015 Disease Ontology update: DO's expanded curation activities to connect disease-related data Elvira Mitraka 0 Lynn M. Schriml lschriml@som.umaryland.edu 0 Institute for Genome Science, University of Maryland School of Medicine , Baltimore, MD , USA 2015 9 000

The  Human  Disease  Ontology  is  a  widely  used  biomedical  resource,   which   standardizes   and   classifies   common   and   rare   human   diseases.   Its   latest   iteration   makes   use   of   the   OWL   language   to   facilitate   easier   curation  between  a  variety  of  working  groups  and  to  take  advantage  of   the  analyses  available  using  OWL.  The  DO  integrates  disease  concepts   from  ICD-­‐9,  ICD-­‐10,  the  National  Cancer  Institute  Thesaurus,  SNOMED-­CT,  MeSH,  OMIM,  EFO  and  Orphanet.  The  DO  Team  is  focused  on  ena-­bling   mapping   and   curation   of   large   disease   datasets   for   major   Bio-­medical   Resource   Centers   and   integration   of   their   disease   terms   into   DO.  Constant  updates  and  additions  to  the  ontology  allow  for  coverage   of  the  vast  field  of  human  diseases.  By  having  close  collaborations  with   a  variety  of  research  groups,  such  as  MGD,  EBI,  NCI,  the  Disease  Ontol-­ogy  has  established  itself  as  the  go-­‐to  tool  for  human  disease  curation.   Implementing   a   combination   of   informatic   tools   and   manual   curation   DO  ensures  that  it  maintains  the  highest  standard  possible.  

 INTRODUCTION

The Disease Ontology (DO) (Schriml et al., 2012) is the core disease data resource for the biomedical community. Human disease data is a cornerstone of biomedical research for identifying drug targets, connecting genetic variations to phenotypes, understanding molecular pathways relevant to novel treatments and coupling clinical care and biomedical research. Consequently, across the multitude of biomedical resources there is a significant need for a standardized representation of human disease to map disease concepts across resources, to connect gene variation to phenotypes and drug targets and to support development of computational tools that will enable robust data analysis and integration.

CURRENT STATUS

DO has proven to be an invaluable genomics and genetic disease data resource used for evaluating and connecting diverse sets of data, used by diverse curation groups to connect human disease to animal models and genomic resources and used to informatically identify representative phenotype sets ( Köhler et al., 2014 ; Schofield et al., 2010 ), functionally similar genes (Fang and Gough, 2013; Singleton et al., 2014) , human gene and genome annotations (Peng et al., 2013; Osborne et al., 2009) , pathways, cancer variants (Wu et al., 2014) and immune epitopes (Vita et al., 2014) . The DO website (http://www.disease-ontology.org), is a web-based application that allows users to query, browse,

CURRENT WORK

Due to the huge amount of data generated at an increasingly rapid pace, the genomics community is trying to streamline its data processing efforts. Ontologies are an avenue that lead to this, but even they can become too big and unwieldy in their effort to capture all available data. There are instances where the multitude of information captured is not needed.

The Gene Ontology is one the most widely used ontology and one of the most comprehensive. It covers the molecular functions, biological processes and location in cellular components of gene products, containing more than 40,000 terms. In order to make it more accessible and less resource intensive the Gene Ontology Consortium has created slim version of the ontology. These “GO slims” are smaller versions of GO that contain only a subset of the terms, representing a general knowledge of a specific field, without going too deep into the hierarchy.

Due to the breadth of its user base the DO team decided to create its own slim files, the DO Cancer Slim (Wu et al., 2015) being the most prominent, containing terms needed by the pan-cancer community. In the same vein a DO MGI slim is being created. It contains all the terms that were modified or created during an intensive curatorial effort to map concepts between DO and OMIM. It will give insight into the overlap between DO and OMIM, as well as which disease types are more heavily featured in the MGD. Meaning it can give even more information about which diseases do not have a mouse model to represent them.

4 UPCOMING WORK

Future plans include the definition of all disease terms in DO and the creation of DO slims for every major curation project of all the MODs. These slims will enable DO users to review the representation and classification of MOD associated diseases, to compare the diseases represented between MODs and to compare the different animal models associated with a particular disease or types of diseases across species.

 ACKNOWLEDGEMENTS

This work was supported in part by the National Institute of Health – National Center for Research Resources (R01RR025341) and NIH/NIGMS (R01 GM 089820-06).

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