Computational Systems for Integrative Genomics

Carl Icahn Laboratory, New Jersey

June 8, 2017

Slides at slides.com/dhimmel/csig

http://www.greenelab.com/

How I became intestested in graphs

http://blog.dhimmel.com/friendship-network/

My Facebook friendship network in 2014

What can we do with this graph?

• Course statistics:
  How many students are in CIS 550?
   
• Course recommendations:
  What courses do other students in CIS 550 take?
   
• Course scheduling:
  What room should a course be in so it's nearby other courses that its students are enrolled in?

What's the best software for storing and querying hetnets?

Visualizing Hetionet v1.0

• Hetnet of biology for drug repurposing
   
• ~50 thousand nodes
  11 types (labels)
   
• ~2.25 million relationships
  24 types
   
• integrates 29 public resources
  knowledge from millions of studies

Hetionet v1.0

• Nodes
  • standardized vocabularies
  • stable, unambiguous identifiers
     
• Relationships:
  • Omics scale required
  • Literature mining
  • High throughput experimental technologies
  • Avoid manual mapping
     
• Versioned data dependencies with GitHub commit hash URLs
  pandas-dev/pandas#14576

Constructing Hetionet v1.0

neo4j.het.io

• Customized Docker image
• Digital Ocean droplet
• SSL from Let's Encrypt
• readonly mode with a query execution timeout
• Custom GRASS style
• Custom guides

Public Hetionet Neo4j Instance

MATCH path =
  // Specify the type of path to match
  (n0:Disease)-[e1:ASSOCIATES_DaG]-(n1:Gene)-[:INTERACTS_GiG]-
  (n2:Gene)-[:PARTICIPATES_GpBP]-(n3:BiologicalProcess)
WHERE
  // Specify the source and target nodes
  n0.name = 'multiple sclerosis' AND
  n3.name = 'retina layer formation'
  // Require GWAS support for the
  // Disease-associates-Gene relationship
  AND 'GWAS Catalog' in e1.sources
  // Require the interacting gene to be
  // upregulated in a relevant tissue
  AND exists(
    (n0)-[:LOCALIZES_DlA]-(:Anatomy)-[:UPREGULATES_AuG]-(n2))
RETURN path

How could multiple sclerosis could affect retina layer formation?

More queries at thinklab.com/d/220

Project Rephetio contributions on Thinklab

(see thinklab.com/p/rephetio/leaderboard)

Project Rephetio: drug repurposing predictions

• Hetionet v1.0 contains:
  • 1,538 connected compounds
  • 136 connected diseases
  • 209,168 compound–disease pairs
  • 755 treatments
• Systematic drug repurposing:
  • Compare the therapeutic utility of data types
  • Identify the mechanisms of drug efficacy
  • Predict the probability of treatment for all 209,168 compound–disease pairs (het.io/repurpose)
• Project online at thinklab.com/p/rephetio

Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Daniel S Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E Baranzini
bioRxiv. 2016. DOI: 10.1101/087619

Machine learning methodology

Predictions succeed at prioritizing known treatments

1,206 compound–disease metapaths (length ≤ 4)

DWPC Δ AUROC

1. Upper tier:
   traditional pharmacology
2. Upper-middle tier:
   traditionally biomedicine, but newer in drug efficacy
3. Lower-middle tier:
   genome-wide / high-throughput data sources
4. Lower tier:
   cellular components

Browse at het.io/repurpose/metapaths.html

Project Rephetio: Does bupropion treat nicotine dependence?

• Bupropion was first approved for depression in 1985
   
• In 1997, bupropion was approved for smoking cessation
   
• Can we predict this repurposing from Hetionet? The prediction was:
  • 99.5th percentile for nicotine dependence
  • probability 2.50-fold greater than null

Compound–causes–SideEffect–causes–Compound–treats–Disease

Compound–binds–Gene–binds–Compound–treats–Disease

Compound–binds–Gene–associates–Disease

Compound–binds–Gene–participates–Pathway–participates–Disease

MATCH path = (n0:Compound)-[:BINDS_CbG]-(n1)-[:PARTICIPATES_GpPW]-
  (n2)-[:PARTICIPATES_GpPW]-(n3)-[:ASSOCIATES_DaG]-(n4:Disease)
USING JOIN ON n2
WHERE n0.name = 'Bupropion'
  AND n4.name = 'nicotine dependence'
  AND n1 <> n3
WITH
[
  size((n0)-[:BINDS_CbG]-()),
  size(()-[:BINDS_CbG]-(n1)),
  size((n1)-[:PARTICIPATES_GpPW]-()),
  size(()-[:PARTICIPATES_GpPW]-(n2)),
  size((n2)-[:PARTICIPATES_GpPW]-()),
  size(()-[:PARTICIPATES_GpPW]-(n3)),
  size((n3)-[:ASSOCIATES_DaG]-()),
  size(()-[:ASSOCIATES_DaG]-(n4))
] AS degrees, path
RETURN
  path,
  reduce(pdp = 1.0, d in degrees| pdp * d ^ -0.4) AS path_weight
ORDER BY path_weight DESC
LIMIT 10

Cypher query to find the top CbGbPWaD paths

Browse all predictions at het.io/repurpose. Discuss at thinklab.com/d/224

Top 100 epilepsy predictions & their chemical structure

Discuss at thinklab.com/d/224#5

Top 100 epilepsy predictions & their drug targets

Discuss at thinklab.com/d/230#14

• Hetionet (≤ v1.0) integrated data from 31 resources:
  • 5 United States Government works
  • 12 openly licensed
  • 4 non-commercial use only
  • 9 were all rights reserved
  • 1 explicitly & contractually forbid reuse
• Requested permission for 11 resources:
  • median time to first response was 16 days
  • 2 affirmative responses
• Other considerations:
  • who owns data
  • incompatibilities: share alike vs non-commercial
  • copyright status of data & fair use
• Solution: license attribute per node/relationship

Legal barriers to data reuse

Recommendations:

1. release data under an open license
2. University researchers: commit to open in your resource sharing plan

Future: all biomedical knowledge in a single network

https://github.com/greenelab/snorkeling

• Teach computers how to read the literature and extract knowledge.
   
• Continuously and automatically refine and grow the hetnet.
   
• Free from any legal restrictions on reuse. 

hetmech

https://github.com/greenelab/hetmech

Find the tissue-supported contribution of each pathway to treating CAD with enalapril (https://neo4j.het.io)

MATCH path = (n0:Compound)-[:BINDS_CbG]-(n1)-[:PARTICIPATES_GpPW]-
  (n2)-[:PARTICIPATES_GpPW]-(n3)-[:ASSOCIATES_DaG]-(n4:Disease)
MATCH (n4)-[:LOCALIZES_DlA]-(anatomy)
MATCH (n1)-[:EXPRESSES_AeG]-(anatomy)-[:EXPRESSES_AeG]-(n3)
WHERE n0.name = 'Enalapril'
  AND n4.name = 'coronary artery disease'
  AND n1 <> n3
WITH
  DISTINCT path,
  n2 AS pathway,
[
  size((n0)-[:BINDS_CbG]-()),
  size(()-[:BINDS_CbG]-(n1)),
  size((n1)-[:PARTICIPATES_GpPW]-()),
  size(()-[:PARTICIPATES_GpPW]-(n2)),
  size((n2)-[:PARTICIPATES_GpPW]-()),
  size(()-[:PARTICIPATES_GpPW]-(n3)),
  size((n3)-[:ASSOCIATES_DaG]-()),
  size(()-[:ASSOCIATES_DaG]-(n4))
] AS degrees
RETURN
  pathway.identifier AS pathway_id,
  pathway.name AS pathway_name,
  count(*) AS PC,
  sum(reduce(pdp = 1.0, d in degrees| pdp * d ^ -0.4)) AS DWPC
ORDER BY DWPC DESC, pathway_name