Semantic empowerment of Life Science Applications October 2006

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Semantic empowermentof Life Science
Applications October 2006

• Amit Sheth
• LSDIS Lab, Department of Computer Science,
• University of Georgia

Acknowledgement NCRR funded Bioinformatics of
Glycan Expression, collaborators, partners at
CCRC (Dr. William S. York) and Satya S. Sahoo,
Cartic Ramakrishnan, Christopher Thomas, Cory
Henson.
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Computation, data and semantics In life sciences

• The development of a predictive biology will
  likely be one of the major creative enterprises
  of the 21st century. Roger Brent, 1999
• The future will be the study of the genes and
  proteins of organisms in the context of their
  informational pathways or networks. L. Hood,
  2000
• "Biological research is going to move from being
  hypothesis-driven to being data-driven." Robert
  Robbins
• Well see over the next decade complete
  transformation (of life science industry) to very
  database-intensive as opposed to wet-lab
  intensive. Debra Goldfarb
• We will show how semantics is a key enabler for
  achieving the above predictions and visions in
  which information and process play critical role.

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Semantic Web and Life Science

• Data captured per year 1 exabyte (1018)(Eric
  Neumann, Science, 2005)
• How much is that?
• Compare it to the estimate of the total words
  ever spoken by humans 12 exabyte
• Death by data
• The need for
• Search
• Integration
• Analysis, decision support
• Discovery

Not data, but analysis and insight, leading to
decisions and discovery
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Semantic empowermentof Life Science Applications

• Life Science research today deals with highly
  heterogeneous as well as massive amounts of data
  distributed across the world.
• We need more automated ways for integration and
  analysis leading to insight and discovery
• - to understand cellular components, molecular
  functions and biological processes, and more
  importantly complex interactions and
  interdependencies between them.

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Benefits of Semantics

• Development of large domain-specific knowledge
• for reference, common nomenclature, tagging
• Integration of heterogeneous multi-source data
  biomedical documents (text), scientific/experiment
  al data and structured databases
• Semantic search, browsing, integration analysis,
  and discovery
• Faster and more reliable discovery leading to
  quality of life improvements

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What is semantics Semantic Web

• Meaning and use of data
• From syntax and structure to semantics (beyond
  formatting, organization, query interfaces,.)
• XML -gt RDF -gt OWL -gt Rules -gt Trust
• Ontologies at the heart of Semantic Web,
  capturing agreement and domain knowledge
• (Automatic) Semantic annotation, reasoning,
• Also, increasing use of Services oriented
  Architecture -gt semantic Web services
• W3C SW for Health Care and Life Sciences

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Semantic empowermentof Life Science Applications

• This talk will demonstrate some of the efforts
  in
• Building large (populated) life science
  ontologies (GlycO, ProPreO)
• Gathering/extracting knowledge and metadata
  entity and relationship extraction from
  unstructured data, automatic semantic annotation
  of scientific/experimental data (e.g., mass
  spectrometry)
• Semantic web services and registries, leading to
  better discovery/reuse of scientific tools and
  their composition
• Ontology-driven applications developed

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Semantic Applications

• Active Semantic Medical Records Demo an
  operational health care application using
  multiple ontologies, semantic annotations and
  rule based decsion support
• Semantic Browser Demo contextual browsing of
  PubMed aided by ontology and schema (in future
  instance) level relationships
• N-glycosylation process an example of scientific
  workflow
• Integrated Semantic Information Knowledge
  System (ISIS) integrated access and analysis of
  structured databases, sc. literature and
  experimental data
• Others we will not discuss SemBowser, SemDrug,
  .

Let us start with a couple of simple applications
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Life Science Ontologies

• Glyco
• An ontology for structure and function of
  Glycopeptides
• 573 classes, 113 relationships
• Published through the National Center for
  Biomedical Ontology (NCBO)

• ProPreO
• An ontology for capturing process and lifecycle
  information related to proteomic experiments
• 398 classes, 32 relationships
• 3.1 million instances
• Published through the National Center for
  Biomedical Ontology (NCBO) and Open Biomedical
  Ontologies (OBO)

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N-Glycosylation metabolic pathway
GNT-Iattaches GlcNAc at position 2
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GlycO ontology

• Challenge model hundreds of thousands of
  complex carbohydrate entities
• But, the differences between the entities are
  small (E.g. just one component)
• How to model all the concepts but preclude
  redundancy ? ensure maintainability, scalability

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GlycoTree
N. Takahashi and K. Kato, Trends in Glycosciences
and Glycotechnology, 15 235-251
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EnzyO

• The enzyme ontology EnzyO is highly intertwined
  with GlycO. While its structure is mostly that
  of a taxonomy, it is highly restricted at the
  class level and hence allows for comfortable
  classification of enzyme instances from multiple
  organisms
• GlycO together with EnzyO contain all the
  information that is needed for the description of
  Metabolic pathways
• e.g. N-Glycan Biosynthesis

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Pathway representation in GlycO
Pathways do not need to be explicitly defined in
GlycO. The residue-, glycan-, enzyme- and
reaction descriptions contain all the knowledge
necessary to infer pathways.
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Zooming in a little
The N-Glycan with KEGG ID 00015 is the substrate
to the reaction R05987, which is catalyzed by an
enzyme of the class EC 2.4.1.145.
The product of this reaction is the Glycan with
KEGG ID 00020.
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GlycO population

• Multiple data sources used in populating the
  ontology
• KEGG - Kyoto Encyclopedia of Genes and Genomes
• SWEETDB
• CARBANK Database
• Each data source has different schema for storing
  data
• There is significant overlap of instances in the
  data sources
• Hence, entity disambiguation and a common
  representational format are needed

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Ontology population workflow
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Ontology population workflow
Asn(41)b-D-GlcpNAc (41)b-D-GlcpNAc
(41)b-D-Manp (31)a-D-Manp (21)b-
D-GlcpNAc (41)b-D-GlcpNAc(61)a-D-M
anp (21)b-D-GlcpNAc
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Ontology population workflow
ltGlycangt ltaglycon name"Asn"/gt ltresidue
link"4" anomeric_carbon"1" anomer"b"
chirality"D" monosaccharide"GlcNAc"gt
ltresidue link"4" anomeric_carbon"1" anomer"b"
chirality"D" monosaccharide"GlcNAc"gt
ltresidue link"4" anomeric_carbon"1" anomer"b"
chirality"D" monosaccharide"Man" gt
ltresidue link"3" anomeric_carbon"1" anomer"a"
chirality"D" monosaccharide"Man" gt
ltresidue link"2" anomeric_carbon"1" anomer"b"
chirality"D" monosaccharide"GlcNAc" gt
lt/residuegt ltresidue link"4"
anomeric_carbon"1" anomer"b" chirality"D"
monosaccharide"GlcNAc" gt lt/residuegt
lt/residuegt ltresidue link"6"
anomeric_carbon"1" anomer"a" chirality"D"
monosaccharide"Man" gt ltresidue
link"2" anomeric_carbon"1" anomer"b"
chirality"D" monosaccharide"GlcNAc"gt
lt/residuegt lt/residuegt lt/residuegt
lt/residuegt lt/residuegt lt/Glycangt
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Ontology population workflow
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ProPreO ontology

• Two aspects of glycoproteomics
• What is it? ? identification
• How much of it is there? ? quantification
• Heterogeneity in data generation process,
  instrumental parameters, formats
• Need data and process provenance ?
  ontology-mediated provenance
• Hence, ProPreO models both the glycoproteomics
  experimental process and attendant data

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ProPreO population transformation to rdf
Scientific Data
Computational Methods
Ontology instances
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ProPreO population transformation to rdf
Scientific Data
Computational Methods
Key
amino-acid sequence
Protein Data
Protein Path
amino-acid sequence
Extract Peptide Amino-acid Sequence from Protein
Amino-acid Sequence
Peptide Path
Calculate Chemical Mass
Calculate Monoisotopic Mass
Determine N-glycosylation Concensus
RDF
Chemical Mass RDF
Monoisotopic Mass RDF
Amino-acid Sequence RDF
chemical mass
monoisotopic mass
amino-acid sequence
n-glycosylation concensus
chemical mass
monoisotopic mass
amino-acid sequence
n-glycosylation concensus
parent protein
Protein RDF
Peptide RDF
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Semantic empowermentof Life Science Applications

• This talk will demonstrate some of the efforts
  in
• building large life science ontologies (GlycO -an
  ontology for structure and function for
  Glycopeptides and ProPreO - an ontology for
  capturing process and lifecycle information
  related to proteomic experiments) and their
  application in advanced ontology-driven semantic
  applications
• entity and relationship extraction from
  unstructured data, automatic semantic annotation
  of scientific/experimental data (e.g., mass
  spectrometry), and resulting capability in
  integrated access and analysis of structured
  databases, scientific literature and experimental
  data
• semantic web services and registries, leading to
  better discovery/reuse of scientific tools and
  composition of scientific workflows that process
  high-throughput data and can be adaptive
• semantic applications developed

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Relationship extraction from unstructured
data (other related research biological entity
extraction)
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Overview
UMLS
Biologically active substance
affects
complicates
causes
causes
Disease or Syndrome
instance_of
instance_of
???????
Fish Oils
Raynauds Disease
MeSH
9284 documents
PubMed
4733 documents
5 documents
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About the data used

• UMLS A high level schema of the biomedical
  domain
• 136 classes and 49 relationships
• Synonyms of all relationship using variant
  lookup (tools from NLM)
• MeSH
• Terms already asserted as instance of one or more
  classes in UMLS
• PubMed
• Abstracts annotated with one or more MeSH terms

T147effect T147induce T147etiology
T147cause T147effecting T147induced
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Example PubMed abstract (for the domain expert)
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Method Parse Sentences in PubMed
SS-Tagger (University of Tokyo)
SS-Parser (University of Tokyo)
(TOP (S (NP (NP (DT An) (JJ excessive) (ADJP (JJ
endogenous) (CC or) (JJ exogenous) ) (NN
stimulation) ) (PP (IN by) (NP (NN estrogen) ) )
) (VP (VBZ induces) (NP (NP (JJ adenomatous) (NN
hyperplasia) ) (PP (IN of) (NP (DT the) (NN
endometrium) ) ) ) ) ) )
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Method Identify entities and Relationships in
Parse Tree
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Method Identify entities and Relationships in
Parse Tree
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Method Fact Extraction from Parse Tree
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Semantic annotation of scientific/experimental
data
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ProPreO Ontology-mediated provenance
parent ion charge
830.9570 194.9604 2 580.2985
0.3592 688.3214 0.2526 779.4759
38.4939 784.3607 21.7736 1543.7476
1.3822 1544.7595 2.9977 1562.8113
37.4790 1660.7776 476.5043
parent ion m/z
parent ionabundance
fragment ion m/z
fragment ionabundance
ms/ms peaklist data
Mass Spectrometry (MS) Data
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ProPreO Ontology-mediated provenance
ltms-ms_peak_listgt ltparameter instrumentmicromas
s_QTOF_2_quadropole_time_of_flight_mass_spectromet
er modems-ms/gt ltparent_ion
m-z830.9570 abundance194.9604
z2/gt ltfragment_ion m-z580.2985
abundance0.3592/gt ltfragment_ion
m-z688.3214 abundance0.2526/gt ltfragment_i
on m-z779.4759 abundance38.4939/gt ltfragme
nt_ion m-z784.3607 abundance21.7736/gt ltfr
agment_ion m-z1543.7476 abundance1.3822/gt
ltfragment_ion m-z1544.7595 abundance2.9977/
gt ltfragment_ion m-z1562.8113
abundance37.4790/gt ltfragment_ion
m-z1660.7776 abundance476.5043/gt lt/ms-ms_pea
k_listgt
OntologicalConcepts
Semantically Annotated MS Data
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Semantic empowermentof Life Science Applications

• This talk will demonstrate some of the efforts
  in
• building large life science ontologies (GlycO -an
  ontology for structure and function for
  Glycopeptides and ProPreO - an ontology for
  capturing process and lifecycle information
  related to proteomic experiments) and their
  application in advanced ontology-driven semantic
  applications
• entity and relationship extraction from
  unstructured data, automatic semantic annotation
  of scientific/experimental data (e.g., mass
  spectrometry), and resulting capability in
  integrated access and analysis of structured
  databases, scientific literature and experimental
  data
• semantic web services and registries, leading to
  better discovery/reuse of scientific tools and
  composition of scientific workflows that process
  high-throughput data and can be adaptive
• semantic applications developed

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N-Glycosylation Process (NGP)
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Semantic Web Process to incorporate provenance
Semantic Annotation Applications
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Converting biological information to the W3C
Resource Description Framework (RDF) Experience
with Entrez Gene

• Collaboration with Dr. Olivier Bodenreider
• (US National Library of Medicine, NIH, Bethesda,
  MD)

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Biomedical Knowledge Repository
.
Biomedical Knowledge Repository
Entrez
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Implementation
Entrez Gene
Entrez Gene XML
XSLT
Entrez Gene RDF graph
Entrez Gene RDF
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Web interface
ENTREZ GENE
ENTREZ GENE XML
XSLT
ENTREZ GENE RDF GRAPH
ENTREZ GENE RDF
.
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Implementation
Entrez Gene
Entrez Gene XML
XSLT
Entrez Gene RDF graph
Entrez Gene RDF
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XML
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Implementation
Entrez Gene
Entrez Gene XML
XSLT
Entrez Gene RDF graph
Entrez Gene RDF
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RDF Graph
eghas_protein_reference_name_E
APP (geneid-351)
Alzheimers Disease
subject
predicate
object
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RDF Graph
Entrez Gene RDF graph (W3C Validator Site -
http//www.w3.org/RDF/Validator/)
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Implementation
Entrez Gene
Entrez Gene XML
XSLT
Entrez Gene RDF graph
Entrez Gene RDF
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RDF
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Implementation
Entrez Gene
Entrez Gene XML
XSLT
Entrez Gene RDF graph
Entrez Gene RDF
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Connecting different genes
amyloid-beta protein
protease nexin-II
beta-amyloid peptide
APP gene Homo sapiens
A4 amyloid protein
cerebral vascular amyloid peptide
Human APP gene is implicated in Alzheimer's
disease. Which genes are functionally homologous
to this gene?
amyloid beta (A4) precursor protein (protease
nexin-II, Alzheimer disease)
amyloid beta A4 protein
amyloid beta A4 protein
amyloid beta A4 protein
APP gene Gallus gallus
APP gene Canis familiaris
amyloid protein
eghas_protein_reference_name_E
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Inference

• Rules are objects that allow inference from RDF
  data 1
• Oracle 10g allows the creation of rulebase based
  on RDFS (RDF Schema)

amyloid beta (A4) precursor protein (protease
nexin-II, Alzheimer disease)
eghas_protein_reference_name_E
egis_associated_with
egGene-track_geneid/351
egNeurodegenerative Diseases
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Integrated Semantic Information and knowledge
System (Isis)
Have I performed an error? Give me all result
files from a similar organism, cell,
preparation, mass spectrometric conditions and
compare results.
SPARQL query-based User Interface
ProPreO ontology
Is the result erroneous? Give me all result
files from a similar organism, cell,
preparation, mass spectrometric conditions and
compare results.
Experimental Data Semantic Annotation
Metadata File
Semantic Metadata Registry
PROTEOMECOMMONS
EXPERIMENTAL DATA
ProVault result
MACOT result
mzXML
Pkl
pSplit
Raw
Raw2mzXML
mzXML2Pkl
Pkl2pSplit
MASCOT Search
ProVault
PROTEOMICS WORKFLOW
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Summary, Observations, Conclusions

• We now have semantics and services enabled
  approaches that support semantic search, semantic
  integration, semantic analytics, decision support
  and validation (e.g., error prevention in
  healthcare), knowledge discovery, process/pathway
  discovery,

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• http//lsdis.cs.uga.edu
• http//knoesis.org
• http//lsdis.cs.uga.edu/projects/asdoc/
• http//lsdis.cs.uga.edu/projects/glycomics/