International Symposium on Integrative Bioinformatics 2010

1. Enhancing Data Integration with Text Analysis to
Find Proteins Implicated in Plant Stress Response
Keywan Hassani‐Pak
keywan.hassani‐pak@bbsrc.ac.uk
Integrative Bioinformatics 2010

2. Outline
• Motivation
– Prior information for candidate genes
– Structured data and unstructured text
• Methods
– Text mining plugin for Ondex
– Application case
• Results
– Visualisation
– Association networks
– Filtering noise
– Validation
• Summary

3. Motivation
• High throughput ‘omics research can identify many candidate genes
• Interpretation of experimental results needs prior information
• Most important sources for prior information are
– Structured bioinformatics databases
– Unstructured scientific literature
• GOAL: Automated methods for the integration of prior information
Identify genes that
alter expression
over time
DBs
Literature
Public Data SourcesTime Course Microarray Data
Gene1
Gene2
Gene3
…
...
GeneN
Candidate Genes
Experiment1
Experiment2
…
Get prior information
for genes regarding
the experiment

4. Structured Data vs. Unstructured Text
• Data integration methods
– Syntactic and semantic heterogeneity
– Literature references
• Text mining methods
– Identify facts hidden in unstructured text
– Integrate facts with database entries
http://www.nactem.ac.uk/software/kleio
http://www.uniprot.org

5. Integrative Text Mining
• Old: Data integration and text mining systems have been largely
developed independently
• Idea: Combining structured knowledge stored in public data bases with
unstructured information in literature
• New: Text mining plugin for the data integration framework Ondex
Data
Transformation
Clients/ToolsHeterogeneous
Data Sources
UniProt
OBO
Parser
Parser
Ondex
CoreGeneralizedObjectDataModel
DatabaseLayer
Mapping
Methods
Accession
Name based
BLAST
Data Exchange
Taverna
Cytoscape
Ondex
Frontend
Lucene
KEGG Parser
OXL/RDF
WebService
Text Mining
MEDLINE Parser
Ondex Integrator
www.ondex.org

6. Advanced Knowledge Base
1. Structured information
– Bioinformatics databases, ontologies
– Curated citations in structured data sources (e.g. from UniProt)
2. Unstructured information
– MEDLINE titles and abstracts are indexed and normalised (by Lucene)
– Information Retrieval strategies: exact, fuzzy, proximity
– Named Entity Recognition: concept‐based (names and synonyms)
– Score: tf‐idf weight (term frequency * inverse document frequency)
text‐mining
x
y
BA
is_related
Publication
Concepts
published_in
weighted association network
IP=1.7; M=1.2; N=2
yx
BA

7. Association Scores
weighted association network
N=29; M=3.1; IP=22.4
BA
= N
tf‐idf = 3.1 = M
tf‐idf = 1.7
tf‐idf = 0.9
IP = 22.4
...

8. Phenotypes
Worldwide Data Resources
Time Course Microarray Data
Network Inference
‐ Literature
‐ Public databases
‐ Public experiments
Identification of key
regulatory genes
Knock out experiments
Overexpresser experiments
Identify genes that alter
expression over time
Prior information
Ondex
The PRESTA project
http://www2.warwick.ac.uk/fac/sci/whri/research/presta

9. Application Case: Knowledge Base for
Stress Response in Arabidopsis
• Publications (the corpus)
– MEDLINE: search ‘Arabidopsis thaliana’
28653 publications
• Proteins
– UniProtKB: search ‘taxid:3702 + reviewed’
8582 proteins
– 13502 curated citations
• Plant Stress Ontology
– 33 stresses/treatments related to PRESTA
experiments
– Biotic: Bacteria, Fungus, etc.
– Abiotic: Drought, Salt, Light, Hormone, etc.
Stress
Protein
Publication
Enzyme
13502 352445194
published_in
is_related

10. X. campestris
Network Visualisation

11. Protein‐Stress Association Network
• 3145 proteins linked to 32 stresses by 10777 relations
• On average
• each protein associated with 3.4 stresses
• each stress associated with 337 proteins
• Filtering associations based on three confidence scores IP, M and N
X. campestris
Ethylene
Metric Min Max
IP 0.01 347.26
M 0.01 26.86
N 1 600

12. How to find cut‐offs for filtering?
• Problem: Text mining results often error‐prone
• Aim: Improving signal‐to‐noise ratio by setting optimal cut‐offs
• Co‐citation number (N) is simplest way to potentially reduce noise in
such association networks
• Filtering by IP and M should be more selective as both consider
frequency of terms in the corpus
• However, none of the metrics is superior overall
• Considering several metrics at the same time seems to be method of
choice to reduce noise and highlight key associations

13. a. b.
TM
AHD
AHD
TM
Validation of Protein‐Ethylene Pairs
• Ethylene association network contained 533 proteins
• Ideally read all abstracts and evaluate association
• Comparison with Arabidopsis Hormone Database (AHD)
a. 31 curated associations: 71.0% recall
b. 166 total associations (inc. GO): 44.8% recall

14. Top 10 protein predictions
ACCESSION NAME PUBMED YEAR M N IP PVAL TRUE
AT3G05420 ACBP4 18836139 2008 13.51 1 13.51 1.00* yes
AT1G31812 ACBP6 18836139 2008 11.57 2 17.14 0.50 yes
AT3G03190 ATGSTF6 14617075 2003 7.36 7 15.75 0.25 yes
AT4G26080 ABI1 19705149 2009 6.66 10 12.22 0.39 yes
AT3G21510 AHP1 18384742 2008 6.60 3 6.70 0.17 yes
AT1G75040 PR‐5 15988566 2005 5.18 12 5.47 0.07 yes
AT2G45820 Remorin 9159183 1997 5.04 4 6.77 0.86 no
AT3G11410 PP2CA 19705149 2009 5.00 1 5.00 1.00 yes
AT1G09570 Phytochrome A 8703080 1996 4.79 11 8.47 0.19 no
AT1G04240 IAA3 19213814 2009 4.54 3 5.14 0.67 yes
• Evaluated top 10 proteins (sorted by M score) from our analyses that are
linked to ethylene but were not found in AHD.
• P‐value relates to the significance of the IP score.
However if N=1 P=1 (*)
• Evidence text
• PMID:18836139: the interaction of ACBP4 and AtEBP may be related to AtEBP‐mediated
defence possibly via ethylene and/or jasmonate signalling.
• PMID:19705149: protein phosphatase 2C ABI1 modulates biosynthesis ratio of ABA and
ethylene.

15. Future Work
• Integrate more advanced text mining methods
• Extensive analysis and evaluation of our association metrics
• Investigate alternative association metrics
• Finding best cut‐off for optimal signal‐to‐noise ratio
• Apply method to more application cases

16. Summary
• Prior information needs to be extracted from structured data and
unstructured text
• Developed a flexible text mining plugin for the data integration framework
Ondex (open source)
• Can be linked into various bioinformatics workflow to enhance high‐
throughput ‘omics research
• First report of systematically combining data integration with basic text
mining
• Generated prior information for Arabidopsis proteins regarding the
PRESTA experiments

17. Acknowledgements
ONDEX BBSRC SABR Project BB/F006039
PRESTA BBSRC SABR project BB/F005806
Catherine Canevet
Chris Rawlings
Roxane Legaie
Hugo van den Berg
Jay Moore
THANK YOU!
Contact:
keywan.hassani‐pak@bbsrc.ac.uk