1. The project aims to build a knowledge framework dedicated to NGLY1 deficiency to support basic research for hypothesis generation and drug repurposing. 2. An NGLY1 deficiency knowledge graph is being created by curating literature and integrating data on genes, proteins, pathways, and phenotypes. 3. The knowledge graph is anchored to Wikidata to allow for data integration and is being used in Knowledge.Bio for hypothesis generation and to iteratively improve the graph with researcher engagement.

1. NGLY1 deficiency Knowledge Graph for
Hypothesis Generation and Drug Repurposing
Núria Queralt-Rosinach1
, Toby Li1
, Mike Mayers1
, Gregory Stupp1
, Sebastian Burgstaller-Muehlbacher1
, Benjamin M. Good1
and Andrew I. Su1
1
Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, USA
Objectives
NGLY1 deficiency is an extremely rare genetic
disease. Our aims are:
• To set up an NGLY1 deficiency-dedicated
knowledge framework
• To support basic researchers for hypothesis
generation and drug repurposing
NGLY1 deficiency
NGLY1 deficiency (ORPHA:404454) is the first con-
genital disorder of deglycosylation described, an ex-
tremely rare mono-genetic disorder with causal in-
activating mutations in the NGLY1 gene.
Figure 1: Mutations in NGLY1.
Affected individuals have N-glycanase 1 deficiency,
enzyme encoded by NGLY1 and involved in the cy-
tosolic degradation of glycoproteins.
Figure 2: A cell deficient in NGLY1.
The PubMed query ’NGLY1 deficiency’ returns 6
published papers with reported clinical and molec-
ular data for less than 100 patients. The current
challenges for NGLY1 deficiency research are:
• Lack of data
• NO understanding
• NO clinical treatment
Our aim is to build an NGLY1 deficiency knowledge
framework to support its translational research.
Curation
To answer research questions around the NGLY1 de-
ficiency, we are manually curating the literature to
build an NGLY1 deficiency knowledge base:
• Querying PubMed (around 13 papers)
• To capture the underlying pathophysiology
• Structured as a graph, subject-predicate-object
statements
• Normalized to Wikidata items (entities and
predicates) [1]
• Evaluated through competency questions
Integration
We have integrated NGLY1 knowledge on genes,
proteins, pathways, phenotypes, diseases:
Figure 3: NGLY1 deficiency curated knowledge graph.
Knowledge Graph
Our first result is the construction of an NGLY1 deficiency knowledge graph anchored to
Wikidata. Wikidata is a community-built knowledge graph and opens data integration opportunities to
external RDF knowledge graphs through the Semantic Web.
Hypothesis Generation
To generate new hypothesis the NGLY1 graph is
loaded into Knowledge.Bio, which allows:
• Expanding the graph with literature-mined
relationships
• Performing evidence-based hypothesis generation
• Iteratively improving the graph with researchers
engagement
Figure 4: NGLY1 graph visualized in Knowledge.Bio.
Drug Repurposing
Computational drug repurposing using the Rephetio
system (See Mike Mayers Poster) to generate hy-
potheses on clinical therapies for NGLY1 deficiency.
Figure 5: NGLY1 deficiency anchored to hetionet.
The following table shows first drug predictions with
a very simple NGLY1 deficiency graph anchored.
Drug (DrugBank ID) Probability
Metformin (DB00331) 0.0315
Crizotinib (DB08865) 0.0314
Ruxotinib (DB08877) 0.0308
Table 1: Preliminary drug predictions for NGLY1 deficiency.
Knowledge.Bio
Knowledge.Bio [2] is an open-source project de-
signed for hypothesis generation. It has the following
main features:
• Literature-derived relationships
• Organize concept maps linked to evidence
• Online access
• Researchers communication
Summary
The main characteristics of this ongoing project are
outlined below:
1 NGLY1 deficiency as a use case
2 Disease-oriented framework
3 Knowledge graph anchored to Wikidata and RDF
4 For hypothesis generation and drug repurposing
References
[1] S. Burgstaller-Muehlbacher et al.
Wikidata as a semantic framework for the gene wiki initiative.
Database, 2016, 2016.
[2] R. Bruskiewich et al.
Knowledge.Bio: A web application for exploring, building and sharing
webs of biomedical relationships mined from pubmed.
bioRxiv, January 2016.
Acknowledgements
This project was supported by the National Institutes of General Medical
Sciences (R01GM089820) and National Institutes for Health Common Fund
programs for Big Data to Knowledge (U54GM114833) and Extracellular
RNA Communication (U54DA036134).
Contact Information
• Web: http://sulab.org/
• Email: asu@scripps.edu