KnetMiner provides an easy to use web interface to visualisation and data mining tools for the discovery and evaluation of candidate genes from large scale integrations of public and private data sets. It addresses the needs of scientists who generally lack the time and technical expertise to review all relevant information available in the literature, from key model species and from a potentially wide range of related biological databases. We have previously developed genome-scale knowledge networks (GSKNs) for multiple crop and animal species (Hassani-Pak et al. 2016). The KnetMiner web server searches and evaluates millions of relations and concepts within the GSKNs in real-time to determine if direct or indirect links between genes and trait-based keywords can be established. KnetMiner accepts as user inputs: search terms in combination with a gene list and/or genomic regions. It produces a table of ranked candidate genes and allows users to explore the output in interactive genome and network map visualisation tools that have been optimised for web use on desktop and mobile devices. The KnetMiner web server and the GSKNs provide a step-forward towards systematic and evidence-based gene discovery.

1. Mining biological knowledge networks for
gene-phenotype discovery
Keywan Hassani-Pak
http://knetminer.rothamsted.ac.uk/
Plant and Animal Genomes Conference 2017
@KnetMiner

2. The Genotype to Phenotype Challenge
Genotype
SNPs and Indels
Omics
Includes any ‘omics
Phenotype
Flowering
Defence
Development
Stress tolerance
Biological Knowledge Network
1. Methods to assemble and visualise an integrated
knowledge network of the cell
2. Methods to use the knowledge network to
translate genotype to phenotype

3. • Free and open source
• Data warehousing using a graph-
database
• Platform to integrate public and private
datasets in various formats
• Provides a GUI, CLI and APIs for
reproducible data integration workflows
Ondex – Data Integration Platform
Ondex
www.ondex.org

4. The approach is generic and works similarly for other species

5. Let’s get a GWAS dataset…
http://plants.ensembl.org/biomart
#SNP=66,816 | #Gene=27,502 | #Phenotype=107

6. … transform into a network
(SNP)
(Phenotype)
associated

7. Biological interaction datasets
http://thebiogrid.org

8. (SNP)
(Phenotype)
associated
… add biological interactions

9. • Gene-GO
• Gene-Phenotype
Gene knock-out or overexpression
Text mining publications
• Gene-Publication
• Gene-Pathway
• Homology to yeast
• Homology to crops
Wheat
… finally add other open linked data
>500,000 nodes
>1,500,000 links
Genome-scale knowledge network

10. Relationships in Crop Knowledge Networks
GO
TO
encodes
text-mining
GWAS
P-Value 10-8
41% identity
EnsemblCompara
Genes Homology Annotations
encodes
Inferred from
Mutant Phenotype
PMID: 15598800
Genetics
QTL
GWAS
Marker
Interactions Phenotype
Mutations in TTG2
cause phenotypic
defects seed color
pigmentation.
PMID: 17766401

11. • Methods needed to evaluate millions of
relationships in knowledge network, prioritize
genes and extract relevant subnetworks
• Interactive and exploratory tools needed to
enable knowledge discovery and decision
making
• Interpretation should be the task of domain
experts i.e. biologists!
How to search and interpret too much information?

12. KnetMiner – Systematic and evidence-based gene discovery
http://knetminer.rothamsted.ac.uk

13. Web Browser
KnetMiner
Client
KnetMiner
Server
Servlets and JSP Page
Java Socket
Knowledge
Graph DBOndex API
DHTML
JavaScript
Apache Tomcat
Multithreaded
Java Server
HTML, JSON, XML and images
over HTTP via Ajax
Views
Java Socket
Java Applet
Flash
KnetMiner Software Architecture
Major improvements
to the user-interface.
Re-implemented Java
Applet and Flash
components in
JavaScript.
Now compatible with
most OS and touch
devices.

14. Which associations (genes) are worth following up?
Often a highly subjective decision
How is genotype translated to phenotype?
Often involves multi-omics interactions

15. KnetMiner search interface

16. KnetMiner Outputs

17. Use Case 1 – Mining GWAS and QTL data

18. • 96 or 192 Arabidopsis inbred lines
• Genotyped: 250,000 SNPs
• 107 phenotypes were measured
https://arapheno.1001genomes.org/study/1/
o Flowering
o Defence
o Ionomics
o Developmental
• Wilcoxon and EMMA (control population structure) statistical tests
GWAS of 107 Phenotypes in Arabidopsis
Atwell et al., Nature 2010

19. Examples where GWAS results are simple to interpret
Sodium concentration (Na)
Lesioning (LES)
AvrRpm1
Single, sharp peak of
association centred on
causal polymorphism
LD decays within 10 kb on average
in Arabidopsis

20. Examples where GWAS results are complex to interpret
FLC gene expression (FLC)
Leaf Number (LN22)
Days to flowering (FT Field)
Peaks are diffuse
covering several hundred
kb without a clear centre
Causal polymorphisms have not
always strongest association

21. Using KnetMiner to interpret GWAS results
Wilcoxon
results
EMMA
results
Atwell et al., Nature 2010
Flowering Locus C (FLC) gene expression

22. Demo: Exploring genes and networks controlling FLC expression

23. • Petal size QTL in Arabidopsis (in collaboration with John Doonan)
Using KnetMiner to prioritise genes in QTL

24. Use Case 2 – Mining differentially expressed
genes

25. #25
White grained wheat is more prone to pre-harvest sprouting (PHS)
• PHS is the result of premature germination of grain in
the ear and results in loss of bread-making quality
• Red grain colour is associated with increased dormancy
and resistance to PHS
• Grain colour is due to proanthocyanidins (condensed
tannins) in the testa
Sprouting
Grain colour
+ = white
o = red
Groos et al. (2002)TAG 104, 39-47
Red grain 20dpa
Andy Phillips

26. 67 down-regulated genes
37 up-regulated genes
Over hundred statistically significant
genes.
How are these linked to grain colour
and PHS?
Differential Gene Expression Analysis

27. Google-like search interface
• Search knowledge graph using trait-
based keywords
• Real-time user feedback and query
suggestions
Trait related
keywords
Query term
suggestions

28. Genes linked to grain colour and/or PHS

29. Genes with direct or indirect links to grain colour and PHS
#29

30. KnetMiner methodology

31. Ondex Text-Mining Plugin
Input data
• 27,416 Arabidopsis gene names from Phytozome
• 52,561 Abstracts from PubMed that contain Arabidopsis
• 22,201 curated citations from TAIR
• 1,349 Trait Ontology terms from Planteome
Hassani-Pak et al., 2010
text-mining
x
y
BA
occurrs_in
Publication
Concepts
published_in
weighted association network
IP=1.7; M=1.2; N=2
yx
BAGeneTO
TO

32. Text-mining output
These steps connect 5553 Arabidopsis genes to 409 TO terms
based on 18,341 co-citations

33. • Uses TF*IDF to rank documents by their relevance to a search term
• Additionally, considers the properties of gene-evidence networks such as
 the specificity of documents to a gene
 the frequency of evidence concepts
• Smart pre-indexing of the knowledge network makes the computation of
the score very fast
Gene Ranking

34. • Web application for very fast search of
large genome-scale knowledge graphs
• Ranking of candidate genes based on
knowledge mining
• Interactive visualisation of genome
and knowledge maps
• Facilitates hypothesis validation and
generation
KnetMiner – Making Gene Discovery Efficient & Fun
http://knetminer.rothamsted.ac.uk/

35. Acknowledgements
John Doonan
Sergio Feingold
Martin Castellote
Uwe Scholz
Matthias Lange
Andy Law
Keywan Hassani-Pak
Ajit Singh
Marco Brandizi
Monika Mistry
Lisa Lill
Chris Rawlings
Dave Edwards
Philipp Bayer
Misha Kapushesky
Kevin Dialdestoro
@KnetMiner