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Discovery over a NGLY1 deficiency
Knowledge Graph
Núria Queralt Rosinach
The Su Lab @ Scripps, CA
BioHackathon 2017 Symposium, Tokyo-Morioka
Knowledge Discovery

From DisGeNET-RDF to knowledge discovery over
knowledge graphs

Drug repurposing

Rare diseases as a challenge

Clinical

Economical

Social
NGLY1 Deficiency

Autosomal recessive genetic disease (Congenital Disorders of DeGlycosilation)

First report in 2012

NGLY1 gene: the cells of the body cannot synthesize the enzyme N-Glycanase 1

Nonsense mutations

Extremely rare: clinical and molecular data for <100 patients

Clinical phenotypes: heterogeneity and not fully characterized
NO treatment, NO understanding, LACK of data
Alacrima
Movement disorders
Developmental delay
Liver disease
NGLY1
NGLY1 gene

Encodes N-glycanase 1

Well conserved in eukaryotes

Known function: enzymatic activity

Cleaves N-Glyco from protein parts on misfolded N-Glycoproteins

Endoplasmic Reticulum Associated Degradation (ERAD) pathway

Wide organ/tissue expression

Associated to neurological and mithocondrial diseases
Goals
1) Generate hypotheses on clinical therapies for
NGLY1 deficiency
3) Treatments to cure or improve the quality of life
2) Drug repurposed candidates
Approach

NGLY1-oriented knowledge graph

Apply graph mining tools for discovery
NGLY1
deficiency
Drugs
EVIDENCE-
BASED
HYPOTHESES
Rephetio System

Hetionet v1.0: hetnet for drug repurposing

1552 small molecules

137 complex diseases

755 treatments (supervised)

47,031 nodes, 11 types

2,250,197 edges, 24 types

29 public resources

Studies over the last half century
Heterogeneous Network
Credit: D. Himmelstein http://slides.com/dhimmel/thesis-
Hetionet Data
DISEASEDISEASEVARIANTVARIANT DISEASEDISEASEGENEGENE
Animal models Text mining
LHGDN
BEFREE
GAD
CTD
RGD
MGD
UniPro
t
CTD
Curated
Orpha
net
HPO
ClinVar
GWAS
Catalo
g
PsyGe
NET
• 13 sources• 13 sources • Linked Open Data• Linked Open Data
http://www.disgenet.org/
support@disgenet.org
twitter: @DisGeNET
V5.0
Edge Prediction
doi: 10.1109/ASONAM.2011.112

PathPredict

To predict if two objects will build a relationship

Based on heterogeneous networks

Metapaths as topological features

Interpretability of the mechanism behind the relationship predicted
Heterogeneous Network
Edge Prediction (HNEP)

Hetnet → Metapaths → DWPC → Supervised model
Heterogeneous Network
Edge Prediction (HNEP)

Extraction of topological features from the network

Metapath: encodes a relationship with a biological interpretation
Logistic Regression
Rephetio Predictions

Interpretability
Himmelstein, Systematic integration of biomedical knowledge
prioritizes drugs for repurposing DOI: https://doi.org/10.1101/087619
RD-Rephetio

Add NGLY1 content – Biocuration

Add Rare Disease (RD) content – Orphanet, DrugCentral
Biocuration
Biocuration

To capture the pathophysiology
NGLY1 Deficiency edges to:

SYMPTOMS

NGLY1

GO annotation

ERAD pathway

PPI
ERAD
pathway
NGLY1
Deficiency
NGLY1

Disease/Phenotypes

Gene/Proteins

GO_CellComponent

GO_BiologicalProcess

GO_MolecularFunction

Pathway

Taxon
Biocuration

To capture the phenotypic spectrum
Biocuration

NGLY1 phenotype annotation

~100 phenotypes

~50 new HPO terms
Biocuration
Enns et al. (2014) Lam et al. (2017)
Drug repurposing for NGLY1
Drugs
Deep Phenotyping

Anchor NGLY1 deficiency: DOID:0060728

Added Rare Diseases (Orphanet)

Added Rare Disease to Gold Standard (DrugCentral)
Phenome
Genome
Rephetio pros/cons

Pros

Interpretability

Cons

Scalability

Computational cost

Based on connectivity similarity
BioHackathon 2016
Availability: https://github.com/bio-ontology-research-group/walking-rdf-and-owl
Proposal for BH17
Add edges
Text mining
Ontologies
Latest edge
prediction algorithm
Biocuration
Testing
Framework
RD-Rephetio
Join me!
Questions?
Proposal for BH17
Add edges
Text mining
Ontologies
Latest edge
prediction algorithm
Biocuration
Testing
Framework
RD-Rephetio
Contact: nuria#slack

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