Integrative Genomics

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Integrative Genomics

• Anil Jegga
• Biomedical Informatics
• CCHMC
• Anil.Jegga_at_cchmc.org
• http//anil.cchmc.org

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Two Separate Worlds..
Medical Informatics
Bioinformatics the omes
PubMed
Proteome
Disease Database
Patient Records
OMIM Clinical Synopsis
Clinical Trials
382 omes so far and there is UNKNOME too -
genes with no function known http//omics.org/inde
x.php/Alphabetically_ordered_list_of_omics
With Some Data Exchange
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Motivation
To correlate diseases with anatomical parts
affected, the genes/proteins involved, and the
underlying physiological processes (interactions,
pathways, processes). In other words, bringing
the disciplines of Medical Informatics (MI) and
BioInformatics (BI) together (Biomedical
Informatics - BMI) to support personalized or
tailor-made medicine.
How to integrate multiple types of genome-scale
data across experiments and phenotypes in order
to find genes associated with diseases
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Model Organism Databases Common Issues

• Heterogeneous Data Sets - Data Integration
• From Genotype to Phenotype
• Experimental and Consensus Views
• Incorporation of Large Datasets
• Whole genome annotation pipelines
• Large scale mutagenesis/variation projects
  (dbSNP)
• Computational vs. Literature-based Data
  Collection and Evaluation (MedLine)
• Data Mining
• extraction of new knowledge
• testable hypotheses (Hypothesis Generation)

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Support Complex Queries

• Show me all genes involved in brain development
  that are expressed in the Central Nervous System.
• Show me all genes involved in brain development
  in human and mouse that also show iron ion
  binding activity.
• For this set of genes, what aspects of function
  and/or cellular localization do they share?
• For this set of genes, what mutations are
  reported to cause pathological conditions?

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Bioinformatic Data-1978 to present

• DNA sequence
• Gene expression
• Protein expression
• Protein Structure
• Genome mapping
• SNPs Mutations

• Metabolic networks
• Regulatory networks
• Trait mapping
• Gene function analysis
• Scientific literature
• and others..

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Human Genome Project Data Deluge
No. of Human Gene Records currently in NCBI
29413 (excluding pseudogenes, mitochondrial genes
and obsolete records). Includes 460 microRNAs
NCBI Human Genome Statistics as on February12,
2008
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The Gene Expression Data Deluge
Till 2000 413 papers on microarray!
Problems Deluge! Allison DB, Cui X, Page GP,
Sabripour M. 2006. Microarray data analysis from
disarray to consolidation and consensus. Nat Rev
Genet. 7(1) 55-65.
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Information Deluge..

• 3 scientific journals in 1750
• Now - gt120,000 scientific journals!
• gt500,000 medical articles/year
• gt4,000,000 scientific articles/year
• gt16 million abstracts in PubMed derived from
  gt32,500 journals

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Data-driven Problems..

• Generally, the names refer to some feature of the
  mutant phenotype
• Dickies small eye (Thieler et al., 1978, Anat
  Embryol (Berl), 155 81-86) is now Pax6
• Gleeful "This gene encodes a C2H2 zinc finger
  transcription factor with high sequence
  similarity to vertebrate Gli proteins, so we have
  named the gene gleeful (Gfl)." (Furlong et al.,
  2001, Science 293 1632)

Whats in a name!
Rose is a rose is a rose is a rose!
Gene Nomenclature

• Disease names
• Mobius Syndrome with Polands Anomaly
• Werners syndrome
• Downs syndrome
• Angelmans syndrome
• Creutzfeld-Jacob disease

• Accelerin
• Antiquitin
• Bang Senseless
• Bride of Sevenless
• Christmas Factor
• Cockeye
• Crack
• Draculin
• Dickies small eye

• Draculin
• Fidgetin
• Gleeful
• Knobhead
• Lunatic Fringe
• Mortalin
• Orphanin
• Profilactin
• Sonic Hedgehog

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Rose is a rose is a rose is a rose.. Not Really!
What is a cell?

• any small compartment
• (biology) the basic structural and functional
  unit of all organisms they may exist as
  independent units of life (as in monads) or may
  form colonies or tissues as in higher plants and
  animals
• a device that delivers an electric current as the
  result of a chemical reaction
• a small unit serving as part of or as the nucleus
  of a larger political movement
• cellular telephone a hand-held mobile
  radiotelephone for use in an area divided into
  small sections, each with its own short-range
  transmitter/receiver
• small room is which a monk or nun lives
• a room where a prisoner is kept

Image Sources Somewhere from the internet
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Foundation Model Explorer
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• COLORECTAL CANCER 3-BP DEL, SER45DEL
• COLORECTAL CANCER SER33TYR
• PILOMATRICOMA, SOMATIC SER33TYR
• HEPATOBLASTOMA, SOMATIC THR41ALA
• DESMOID TUMOR, SOMATIC THR41ALA
• PILOMATRICOMA, SOMATIC ASP32GLY
• OVARIAN CARCINOMA, ENDOMETRIOID TYPE, SOMATIC
  SER37CYS
• HEPATOCELLULAR CARCINOMA SOMATIC SER45PHE
• HEPATOCELLULAR CARCINOMA SOMATIC SER45PRO
• MEDULLOBLASTOMA, SOMATIC SER33PHE

The REAL Problems
Many disease states are complex, because of many
genes (alleles ethnicity, gene families, etc.),
environmental effects (life style, exposure,
etc.) and the interactions.
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The REAL Problems
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Integrative Genomics - what is it?Another
buzzword or a meaningful concept useful for
biomedical research?
Acquisition, Integration, Curation, and Analysis
of biological data
Integrative Genomics the study of complex
interactions between genes, organism and
environment, the triple helix of biology. Gene
ltgt Organism lt-gt Environment It is definitely
beyond the buzzword stage - Universities now have
programs named 'Integrated Genomics.'
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Methods for Integration

• Link driven federations
• Explicit links between databanks.
• Warehousing
• Data is downloaded, filtered, integrated and
  stored in a warehouse. Answers to queries are
  taken from the warehouse.
• Others.. Semantic Web, etc

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Link-driven Federations

• Creates explicit links between databanks
• query get interesting results and use web links
  to reach related data in other databanks
• Examples NCBI-Entrez, SRS

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http//www.ncbi.nlm.nih.gov/Database/datamodel/
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http//www.ncbi.nlm.nih.gov/Database/datamodel/
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http//www.ncbi.nlm.nih.gov/Database/datamodel/
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http//www.ncbi.nlm.nih.gov/Database/datamodel/
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http//www.ncbi.nlm.nih.gov/Database/datamodel/
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Querying Entrez-Gene
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Link-driven Federations

• Advantages
• complex queries
• Fast
• Disadvantages
• require good knowledge
• syntax based
• terminology problem not solved

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Data Warehousing
Data is downloaded, filtered, integrated and
stored in a warehouse. Answers to queries are
taken from the warehouse.

• Advantages
• Good for very-specific, task-based queries and
  studies.
• Since it is custom-built and usually
  expert-curated, relatively less error-prone

• Disadvantages
• Can become quickly outdated needs constant
  updates.
• Limited functionality For e.g., one
  disease-based or one system-based.

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http//concise-scanner.cchmc.org
Sequence Context
To identify putative gene targets of
transcription factors
List of Transcription Factor Binding Sites
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GenomeTrafac Tracks
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http//polydoms.cchmc.org
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No Integrative Genomics is Complete without
Ontologies

• Gene Ontology (GO)

• Unified Medical Language System (UMLS)

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The 3 Gene Ontologies

• Molecular Function elemental activity/task
• the tasks performed by individual gene products
  examples are carbohydrate binding and ATPase
  activity
• What a product does, precise activity
• Biological Process biological goal or objective
• broad biological goals, such as dna repair or
  purine metabolism, that are accomplished by
  ordered assemblies of molecular functions
• Biological objective, accomplished via one or
  more ordered assemblies of functions
• Cellular Component location or complex
• subcellular structures, locations, and
  macromolecular complexes examples include
  nucleus, telomere, and RNA polymerase II
  holoenzyme
• is located in (is a subcomponent of )

http//www.geneontology.org
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Example Gene Product hammer
Function (what) Process (why) Drive a nail -
into wood Carpentry Drive stake - into soil
Gardening Smash a bug Pest Control A
performers juggling object Entertainment
http//www.geneontology.org
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GO term associations Evidence Codes

• ISS Inferred from sequence or structural
  similarity
• IDA Inferred from direct assay
• IPI Inferred from physical interaction
• TAS Traceable author statement
• IMP Inferred from mutant phenotype
• IGI Inferred from genetic interaction
• IEP Inferred from expression pattern
• ND no data available

http//www.geneontology.org
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What can researchers do with GO?

• Access gene product functional information
• Find how much of a proteome is involved in a
  process/ function/ component in the cell
• Map GO terms and incorporate manual annotations
  into own databases
• Provide a link between biological knowledge and
• gene expression profiles
• proteomics data

• Getting the GO and GO_Association Files
• Data Mining
• My Favorite Gene
• By GO
• By Sequence
• Analysis of Data
• Clustering by function/process
• Other Tools

And how?
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http//www.geneontology.org/
Gene list enrichment analysis tools (DAVID,
FatiGO, ToppGene
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Open biomedical ontologies
http//obo.sourceforge.net/
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Unified Medical Language System Knowledge Server
UMLSKShttp//umlsks.nlm.nih.gov/kss/

• The UMLS Metathesaurus contains information about
  biomedical concepts and terms from many
  controlled vocabularies and classifications used
  in patient records, administrative health data,
  bibliographic and full-text databases, and expert
  systems.
• The Semantic Network, through its semantic types,
  provides a consistent categorization of all
  concepts represented in the UMLS Metathesaurus.
  The links between the semantic types provide the
  structure for the Network and represent important
  relationships in the biomedical domain.
• The SPECIALIST Lexicon is an English language
  lexicon with many biomedical terms, containing
  syntactic, morphological, and orthographic
  information for each term or word.

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Unified Medical Language SystemMetathesaurus

• about over 1 million biomedical concepts
• About 5 million concept names from more than 100
  controlled vocabularies and classifications (some
  in multiple languages) used in patient records,
  administrative health data, bibliographic and
  full-text databases and expert systems.
• The Metathesaurus is organized by concept or
  meaning. Alternate names for the same concept
  (synonyms, lexical variants, and translations)
  are linked together.
• Each Metathesaurus concept has attributes that
  help to define its meaning, e.g., the semantic
  type(s) or categories to which it belongs, its
  position in the hierarchical contexts from
  various source vocabularies, and, for many
  concepts, a definition.
• Customizable Users can exclude vocabularies that
  are not relevant for specific purposes or not
  licensed for use in their institutions.
  MetamorphoSys, the multi-platform Java install
  and customization program distributed with the
  UMLS resources, helps users to generate
  pre-defined or custom subsets of the
  Metathesaurus.
• Uses
• linking between different clinical or biomedical
  vocabularies
• information retrieval from databases with human
  assigned subject index terms and from free-text
  information sources
• linking patient records to related information in
  bibliographic, full-text, or factual databases
• natural language processing and automated
  indexing research

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UMLSKS Semantic Network

• Complexity reduced by grouping concepts according
  to the semantic types that have been assigned to
  them.
• There are currently 15 semantic groups that
  provide a partition of the UMLS Metathesaurus for
  99.5 of the concepts.

ACTIActivities BehaviorsT053Behavior ANATAna
tomyT024Tissue CHEMChemicals
DrugsT195Antibiotic CONCConcepts
IdeasT170Intellectual Product DEVIDevicesT074
Medical Device DISODisordersT047Disease or
Syndrome GENEGenes Molecular
SequencesT085Molecular Sequence GEOGGeographic
AreasT083Geographic Area LIVBLiving
BeingsT005Virus OBJCObjectsT073Manufactured
Object OCCUOccupationsT091Biomedical
Occupation or Discipline ORGAOrganizationsT093H
ealth Care Related Organization PHENPhenomenaT03
8Biologic Function PHYSPhysiologyT040Organism
Function PROCProceduresT061Therapeutic or
Preventive Procedure
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UMLSKS Semantic Navigator
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Disease Gene Identification and Prioritization
Hypothesis Majority of genes that impact or
cause disease share membership in any of several
functional relationships OR Functionally similar
or related genes cause similar phenotype.

• Functional Similarity Common/shared
• Gene Ontology term
• Pathway
• Phenotype
• Chromosomal location
• Expression
• Cis regulatory elements (Transcription factor
  binding sites)
• miRNA regulators
• Interactions
• Other features..

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Background, Problems Issues

• Most of the common diseases are multi-factorial
  and modified by genetically and mechanistically
  complex polygenic interactions and environmental
  factors.
• High-throughput genome-wide studies like linkage
  analysis and gene expression profiling, tend to
  be most useful for classification and
  characterization but do not provide sufficient
  information to identify or prioritize specific
  disease causal genes.

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Background, Problems Issues

• Since multiple genes are associated with same or
  similar disease phenotypes, it is reasonable to
  expect the underlying genes to be functionally
  related.
• Such functional relatedness (common pathway,
  interaction, biological process, etc.) can be
  exploited to aid in the finding of novel disease
  genes. For e.g., genetically heterogeneous
  hereditary diseases such as Hermansky-Pudlak
  syndrome and Fanconi anaemia have been shown to
  be caused by mutations in different interacting
  proteins.

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PPI - Predicting Disease Genes

• Direct proteinprotein interactions (PPI) are one
  of the strongest manifestations of a functional
  relation between genes.
• Hypothesis Interacting proteins lead to same or
  similar disease phenotypes when mutated.
• Several genetically heterogeneous hereditary
  diseases are shown to be caused by mutations in
  different interacting proteins. For e.g.
  Hermansky-Pudlak syndrome and Fanconi anaemia.
  Hence, proteinprotein interactions might in
  principle be used to identify potentially
  interesting disease gene candidates.

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• Prioritize candidate genes in the interacting
  partners of the disease-related genes
• Training sets disease related genes
• Test sets interacting partners of the training
  genes

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• Example Breast cancer

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ToppGene General Schema
http//toppgene.cchmc.org
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TOPPGene - Data Sources

• Gene Ontology GO and NCBI Entrez Gene
• Mouse Phenotype MGI (used for the first time for
  human disease gene prioritization)
• Pathways KEGG, BioCarta, BioCyc, Reactome,
  GenMAPP, MSigDB
• Domains UniProt (Pfam, Interpro,etc.)
• Interactions NCBI Entrez Gene (Biogrid,
  Reactome, BIND, HPRD, etc.)
• Pubmed IDs NCBI Entrez Gene
• Expression GEO
• Cytoband MSigDB
• Cis-Elements MSigDB
• miRNA Targets MSigDB

New features added
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• ToppGene web server (http//toppgene.cchmc.org)
• For functional enrichment analysis

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• ToppGene web server (http//toppgene.cchmc.org)
• For functional enrichment analysis

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• ToppGene web server (http//toppgene.cchmc.org)
• For functional enrichment analysis

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• ToppGene web server (http//toppgene.cchmc.org)
• For functional enrichment analysis

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• ToppGene web server (http//toppgene.cchmc.org)
• For candidate gene prioritization

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• ToppGene web server (http//toppgene.cchmc.org)
• For candidate gene prioritization

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• ToppGene web server (http//toppgene.cchmc.org)
• For candidate gene prioritization

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Limitations

• General limitations of any training-test
  strategy
• Prior knowledge of disease-gene associations.
• Assumption that the disease genes yet to discover
  will be consistent with what is already known
  about a disease.
• Depend on the accuracy and completeness of the
  functional annotations.
• Only one-fifth of the known human genes have
  pathway or phenotype annotations and there are
  still more than 40 genes whose functions are not
  defined!

Chen et al., 2007 BMC Bioinformatics
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And the Benefits of Integrative Genomics.

• To unravel the connection between genotype and
  phenotype - Systematically identify novel
  phenotypegenotype relationships.
• Encourage standard terminology usage.
• Saves time for researchers.
• Hypotheses generator.
• Paves way for prognosis, diagnosis, and
  personalized medicine (adverse drug reactions,
  etc.).
• Deeper understanding of disease and an enhanced
  integration of medicine with biology.
• Increasing knowledge of the genes associated with
  diseases will allow researchers to address more
  complicated issues, including the relative
  contributions to disease of genes in the core
  biological set shared by all species and those
  encoding proteins specific to humans how
  sequence features (such as conservation and
  polymorphism) relate to disease characteristics
  and how protein function relates to the outcome
  of clinical treatment
• And MANY MORE..

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Take-home messages

• Networks and integration of databases are keys to
  success in Bioinformatics.
• Integration of data computation and data
  integration into a single cohesive whole will
  increase the efficiency of research effort
• by reducing the serendipity hit and miss nature
  of empirical research and
• will provide valuable clues to the biomedical
  researchers on their choice of experiments -
  limitations of funds, manpower and time.
• Users have to know what is available and how to
  access (what are the limitations) and use the
  resources they are offered.

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PubMed
OMIM
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Acknowledgement

• Jing Chen
• Siva Gowrisankar
• Vivek Kaimal
• Mrunal Deshmukh
• Huan Xu

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Thank You!
http//sbw.kgi.edu/
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