Knowledge based analysis of genome scale data
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Knowledge-based Analysis of Genome-scale Data. How to Understand Gene Sets ?. Gene products function together in dynamic groups A key task is to understand why a set of gene products are grouped together in a condition, exploiting all existing knowledge about: The genes (all of them)

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Knowledge-based Analysis of Genome-scale Data

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Knowledge based analysis of genome scale data

Knowledge-based Analysis ofGenome-scale Data


How to understand gene sets

How to Understand Gene Sets?

  • Gene products function together in dynamic groups

  • A key task is to understand why a set of gene products are grouped together in a condition, exploiting all existing knowledge about:

    • The genes (all of them)

    • Their relationships (|genes|2)

    • The condition(s) under study.


Knowledge based analysis of genome scale data

1,170 peer-reviewed gene-related databases in 2009 Nucleic Acids Research database issue


Exponential growth in the biomedical literature

Exponential growth in the biomedical literature

1,000 genomes project will create 1,400GB next year http://1000genomes.org


How to stay ahead

How to stay ahead?

  • Have to take advantage of information gained in different disciplines

    • Relaxin 1 & βblockers

    • Originally characterized in 1926 as pregnancy related


Gene by gene

Gene-by-gene

  • Lots of gene centric information sources:

    • PubMed / GeneRIFs

    • Entrez Gene / UniProt

    • GeneCards

    • OMIM (with associated human phenotypes)

  • But these can be overwhelming even for a single gene, let alone for a list of hundreds.

    • Try scanning these for information about human PPARD, a moderately well-studied gene.


Mapping to pathways

Mapping to Pathways

  • Searching a pathway database (KEGG, Reactome, WikiPathways) with multiple genes

  • “Painting” expression dataonto staticpathways, e.g.GenMAPP


Mapping to ppi networks

Mapping to PPI networks

  • Greater coverage than pathways, but harder to interpret (e.g. GenePro Cytoscape plugin)


Tools to find commonalities

Tools to find commonalities

  • GO term enrichment

    • Identifies annotations of all genes in a cluster that appear more often than expected for a random set of genes of the same size, e.g. Onto-Express

  • DAVID gene functional classification enrichment (GO, PIR, KEGG, Interpro, etc.)


Gene set enrichment analysis

Gene Set Enrichment Analysis

  • Start with predefined sets of related genes, then test expression data for over-representation of each group

  • Not alwayseasy to definegood sets;chromosomalregions workwell in cancer


3r approach

3R Approach

  • Integrated approach to creating knowledge-based resources and using them for analysis

  • Reading: Extracting information from the literature and curated databases

  • Reasoning: Integrating, extending, evaluating and aligning knowledge with data

  • Reporting: Interactive visualizations and queries that facilitate explanation and hypothesis generation


Information integration

Information integration

  • Peer-reviewed gene-centric databases contain:

    • Annotations to function, location, process, disease, etc. ontologies

    • Linkages to many sorts of experimental and derived data(GWAS, expression, structure, pathways, population frequencies)

    • Linkages to publications that report evidence relevant to them

  • Many can be integrated into a single, unified network using gene and/or publication identifiers.

    • Identifier cross-reference lists increasingly reliable

    • Increasing coordination and standardization among providers

  • Some challenges remain, e.g. what is a “gene”?

    • PRO might help, but not there yet.


Reading

Reading

  • The best source of knowledge is the literature

  • OpenDMAP is significant progress in concept recognition in biomedical text

  • Even simple-minded approaches are powerful

    • Gene co-occurrence widely used

    • Thresholded co-occurrence fraction is better


Opendmap extracts typed relations from the literature

OpenDMAP extracts typed relations from the literature

  • Concept recognition tool

    • Connect ontological terms to literature instances

    • Built on Protégé knowledge representation system

  • Language patterns associated with concepts and slots

    • Patterns can contain text literals, other concepts, constraints (conceptual or syntactic), ordering information, or outputs of other processing.

    • Linked to many text analysis engines via UIMA

  • Best performance in BioCreative II IPS task

  • >500,000 instances of three predicates (with arguments) extracted from Medline Abstracts

  • [Hunter, et al., 2008] http://bionlp.sourceforge.net


Reasoning in knowledge networks

Reasoning in knowledge networks

Ddc; MGI:94876

[Bada & Hunter, 2006]

catechols(CHEBI:33566)

catecholamines(CHEBI:33567)

adrenaline (CHEBI:33568)

noradrenaline(CHEBI:33569)

Cadps; MGI:1350922

GO:0042423

GO:0050432

CHEBI:33567

MGI:94876

MGI:1350922

Reliability = 0.009740


Inferred interactions

Inferred interactions

  • Dramatically increase coverage…

  • But at the cost of lower reliability

  • We apply new method toassess reliabilitywithout an explicit goldstandard

  • [Leach, et al., 2007;Gabow, et al., 2008]

Top 1,000 Craniofacial genes(1,000,000 possible edges)


3r knowledge networks

3R Knowledge Networks

  • Combine diverse sources…

    • Databases of interactions

    • Information extracted from the literature (CF or DMAP)

    • Inference of interactions

  • … Into a unified knowledge summary network:

    • Every link gets a reliability value

    • Combine multiple links for one pair into a single summary

      • More sources  more reliable

      • Better sources  more reliable

      • “Noisy Or” versus “Linear Opinion Pool”

  • Summaries allow for effective use of noisy inferences

    • [Leach PhD thesis 2007; Leach et al., 2007]


Knowledge based analysis of experimental data

Knowledge-based analysis of experimental data

  • High-throughput studies generate their own interaction networks tied to fiducials

    • E.g. Gene correlation coefficients in expression data

  • Combine with background knowledge by:

    • Averaging (highlights already known linkages)

    • Hanisch (ISMB 2002) method (emphasizes data linkages not yet well supported by the literature)

  • Report highest scoring data + knowledge linkages, color coding for scores of average, Hanisch or both.


The hanalyzer 3r proof of concept

The Hanalyzer: 3R proof of concept

  • [Leach, Tipney, et al., PLoS Comp Bio 2009] http://hanalyzer.sourceforge.org See video demo by searching YouTube for “Hanalyzer”

  • Knowledge network built for mouse

    • NLP only CF and DMAP for three relationships from PubMed abstracts

  • Simple reasoning (co-annotation, including ontology cross-products)

  • Visualization of combined knowledge / data network via Cytoscape + new plugins


First application craniofacial development

First application: Craniofacial Development

  • NICHD-funded study (Rich Spritz; Trevor Williams) focused on cleft lip & palate

  • Well designed gene expression array experiment:

    • Craniofacial development in normal mice (control)

    • Three tissues (Maxillary prominence, Fronto-nasal prominence, Mandible)

    • Five time points (every 12 hours from E10.5)

    • Seven biological replicates per condition (well powered)

  • >1,000 genes differentially expressed among at least 2 of the 15 conditions (FDR<0.01)


The whole network

The Whole Network

Craniofacial dataset, covering all genes on the Affy mouse chip.

Graph of top 1000 edges using AVE or HANISCH (1734 in total).

Edges identified by both.

Focus on mid-size subnetwork


Knowledge based analysis of genome scale data

Link calculations for MyoD1 MyoG

Shared interprodomains:

IPR:11598…

Premod_M interaction:

Mod074699

R = 0.0438

R = 0.1005

Inferred link through shared GO/ChEBI:

ChEBI:16991

Shared GObiological processes:

GO:6139…

Co-occurrencein abstracts:PMID:16407395…

DMAP transportrelation

R = 0.01

R = 0.0172

Shared GOcell component: GO:5667…

R = 0.1034

R = 0.0105

R = 0.0190

Shared GOmolecular functions:

GO:3705…

R = 0.0284

Shared knockoutphenotypes:MP:5374 …

R = 0.018

Correlation inexpression data:Pdata = 0.4808


Strong data and background knowledge facilitate explanations

Strong data and background knowledge facilitate explanations

  • Goal is abductive inference: why are these genes doing this?

    • Specifically, why the increase in mandible before the increase in maxilla, and not at all in the frontonasal prominence?

AVE edges

Both edges

Skeletal muscle structural components

Skeletal muscle contractile components

Proteins of no common family


Exploring the knowledge network

Exploring the knowledge network

See the YouTubeHanalyzer demo fora better sense of the process


Scientist aide literature explanation tongue development

Scientist + aide + literature  explanation: tongue development

AVE edges

Both edges

Skeletal muscle structural components

Skeletal muscle contractile components

Proteins of no common family

The delayed onset, at E12.5, of the same group of proteins during mastication muscle development.

Myoblast differentiation and proliferation continues until E15 at which point the tongue muscle is completely formed.

Myogenic cells invade the tongue primodia ~E11


On to discovery

HANISCH edges

AVE edges

Both edges

inferred synapse signaling proteins

Inferred myogenic proteins

Proteins of no common family

Proteins in the previous AVE based sub-network

On to Discovery

  • Add the strong data, weak background knowledge (Hanisch) edges to the previous network, bringing in new genes.

  • Four of these genes not previously implicated in facial muscle development (1 almost completely unannotated)


Biological validation

Biological validation

Transverse, E12.5

Sagittal, E11.5

More rostral

More caudal

Apobec2

E430002G05Rik

Hoxa2

Zim1


Using ontologies for explanation what is the role of cav3 in muscle

Using ontologies for explanation:What is the role of CAV3 in muscle?


Genes bad guys pirolli card int l conf on intelligence analysis 2005

Genes ~ Bad Guys?Pirolli & Card, Int’l Conf. on Intelligence Analysis, 2005


Bio jigsaw

Bio-Jigsaw

Based on Stasko, et al.’s [2007] Jigsaw visual analytics system


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