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Gene Ontology and Semantic Similarity

Measures

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Copyright notice

• Many of the images in this power point presentation are from Bioinformatics and Functional Genomics by Jonathan Pevsner (ISBN 0-471-21004-8). Copyright © 2003 by John Wiley & Sons, Inc.

• Many slides of this power point presentation Are from slides of Dr. Jonathon Pevsner and other people. The Copyright belong to the original authors. Thanks!

What is Ontology?

• Dictionary: A branch of metaphysics concerned with the nature and relations of being.

• Barry Smith: The science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality.

16061700s

Sriniga Srinivasan, Chief Ontologist, Yahoo!

The ontology. Dividing human knowledge into a clean set of categories is a lot like trying to figure out where to find that suspenseful black comedy at your corner video store. Questions inevitably come up, like are Movies part of Art or Entertainment? (Yahoo! lists them under the latter.) -Wired Magazine, May 1996

So what does that mean?From a practical view, ontology is the representation of something we know about. “Ontologies" consist of a representation of things, that are detectable or directly observable, and the relationships between those

things.

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what kinds of things exist?

what are the relationships between these things?

eye

_part of

sclera

_is a

sense organ

developsfrom

Optic placode

A biological ontology is:

• A (machine) interpretable representation of some aspect of biological reality

http://www.macula.org/anatomy/eyeframe.html

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Gene Ontology (GO) Consortium

aa

www.geneontology.org• Formed to develop a shared language adequate for the annotation of molecular characteristics across organisms; a common language to share

knowledge.

• Seeks to achieve a mutual understanding of the definition and meaning of any word used; thus we are able to support cross-database queries.

• Members agree to contribute gene product annotations and associated sequences to GO database; thus facilitating data analysis and semantic interoperability.

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Gene Ontology widely adopted

AgBase

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• Molecular Function = elemental activity/task– the tasks performed by individual gene products; examples are

carbohydrate binding and ATPase activity

• Biological Process = biological goal or objective– broad biological goals, such as mitosis or purine metabolism, that are

accomplished by ordered assemblies of molecular functions

• Cellular Component = location or complex– subcellular structures, locations, and macromolecular complexes;

examples include nucleus, telomere, and RNA polymerase II holoenzyme

GO represents three biological domains

Function (what) Process (why)

Drive nail (into wood) Carpentry

Drive stake (into soil) Gardening

Smash roach Pest Control

Clown’s juggling object Entertainment

Example: Gene Product = hammer

Biological ExamplesMolecular FunctionMolecular FunctionBiological ProcessBiological Process Cellular ComponentCellular Component

term: MAPKKK cascade (mating sensu Saccharomyces)

goid: GO:0007244

definition: OBSOLETE. MAPKKK cascade involved in transduction of mating pheromone signal, as described in Saccharomyces.

definition_reference: PMID:9561267

comment: This term was made obsolete because it is a gene product specific term. To update annotations, use the biological process term 'signal transduction during conjugation with cellular fusion ; GO:0000750'.

Terms, Definitions, IDs

Cellular Component

• where a gene product acts

Molecular Function

• A gene product may have several functions; a function term refers to a reaction or activity, not a gene product

• Sets of functions make up a biological process

Molecular Function

• activities or “jobs” of a gene product

glucose-6-phosphate isomerase activity

Molecular Function

insulin bindinginsulin receptor activity

Biological Process

a commonly recognized series of events

cell division

Biological Process

transcription

Biological Process

regulation of gluconeogenesis

Biological Process

limb development

Ontology Structure

• Terms are linked by two relationships– is-a – part-of

Ontology Structure

cell

membrane chloroplast

mitochondrial chloroplastmembrane membrane

is-apart-of

Ontology Structure

• Ontologies are structured as a hierarchical directed acyclic graph (DAG)

• Terms can have more than one parent and zero, one or more children

Ontology Structure

cell

membrane chloroplast

mitochondrial chloroplastmembrane membrane

Directed Acyclic Graph (DAG) - multiple

parentage allowed

Ontology Structure

http://www.ebi.ac.uk/ego

Anatomy of a GO term

id: GO:0006094name: gluconeogenesisnamespace: processdef: The formation of glucose fromnoncarbohydrate precursors, such aspyruvate, amino acids and glycerol.[http://cancerweb.ncl.ac.uk/omd/index.html]exact_synonym: glucose biosynthesisxref_analog: MetaCyc:GLUCONEO-PWYis_a: GO:0006006is_a: GO:0006092

unique GO ID

term name

definition

synonym

database ref

parentage

ontology

Evidence Codes for GO Evidence Codes for GO AnnotationsAnnotations

http://www.geneontology.org/doc/GO.evidence.html

IEA Inferred from Electronic Annotation

ISS Inferred from Sequence Similarity

IEP Inferred from Expression Pattern

IMP Inferred from Mutant Phenotype

IGI Inferred from Genetic Interaction

IPI Inferred from Physical Interaction

IDA Inferred from Direct Assay

RCA Inferred from Reviewed Computational Analysis

TAS Traceable Author Statement

NAS Non-traceable Author Statement

IC Inferred by Curator

ND No biological Data available

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Semantic Similarity Measures between GO terms and proteins

Two information in GO for semantic similarity

• information content (IC) of GO terms

• structural information of GO hierarchy

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Information content• The IC of a GO term t is usually

defined as negative logarithm of the term's probability:

• And the probability of a given GO term t is defined as:

– where anno(t) is the number of genes directly annotated with the term t. Thus, the information content of DAG root is 0. As GO terms ascend the hierarchical tree of DAG from the leaf, the information content would not increase. 31

))(log()( tprobtIC

)()(

)()()()(rootdescendantctdescendantd

cannodannotannotprob

Structural information• Two structural concepts that are

frequently used in GO similarity measures are – the “lowest common ancestor”

(LCA): The LCA of two GO terms is their common ancestor term at the lowest level in the DAG structure.

– the “most informative common ancestor” (MICA). Given two GO terms, the MICA is their common ancestor with the lowest probability in the DAG structure.

– Notice that there also exists a case where two terms have multiple ancestor terms at the same level. In this case, the term with lowest probability will be the final LCA.

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Resnik Mesasure• Resnik similarity. Resnik

introduced a semantic similarity for “is_a” ontologies based the highest IC values among IC values of all common ancestors of two terms:

•

• where S(t1, t2) is the set of common ancestors of two term t1 and t2. The Resnik similarity has a minimum zero.

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)2,1(21Re ))(max(),(

ttStsnik tICttSim

Lin similarity• Lin developed a information-

theoretic similarity applicable to any domain that can be described by a probabilistic model. Lin measure is based on the relative probability between two terms and their MICA. The Lin measure between two GO term t1 and t2 is defined as:

•

– The value of Lin similarity ranges from zero to one.

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))(log())(log(

))(log(2),(

2121 tprobtprob

MICAprobttSimLin

Jiang similarity• Jiang similarity. The Jiang and Conrath integrated the edge-

based method with the node-based approach of the information content calculation to develop a new distance measure. For its simple case in which factors related to local density, node depth and link type are ignored, the Jiang measure between two GO term t1 and t2 is defined as:

• • • Jiang distance measure can easily be transformed into a

similarity measure by adding one and inverting it [10].• • • If terms t1 and t2 are the same, DistJiang(t1,t2) should be 0.

Adding one is to avoid the division of 0. The value of Jiang similarity ranges from zero to one

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)),((2)()(),( 212121 ttMIAICtICtICttDisJiang

),(1

1),(

21

21 ttDisttSim

CJ

Jiang

Wu & Palmer

• Wu & Palmer, 1994– Based on the depths of the two concepts in

the taxonomies, and the depth of the LCS

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)()(

)),((2),(

21

2121 cdepthcdepth

cclcsdepthccsimwup

Graph information content (GIC) similarity

• Let DAGT1 and DAGT2 be two ancestor DAGs induced by two GO terms t1 and t2. Then, the graph information content (GIC) measure is defined as the ratio between sum of information content of GO terms in the intersection of DAGT1 and DAGT2 and sum of information content of GO terms in the union of DAGT1 and DAGT2:

• The values of GIC similarity ranges from zero to one

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21

21

)(

)(),( 21

TT

TT

DAGDAGt

DAGDAGtGIC tIC

tICttSim

Schlicker’s relevance similarity

• Schlicker et al. pointed out that both the specific of MICA and the relation between two GO terms with MICA are needed to be considered in a similarity measure. Then, they developed a new relevance similarity measure by combining the Lin similarity with the probability of MICA.

•

• The values of relevance similarity are also between zero and one. We also evaluate the

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))(1((max),(),(

21Re21

MICAprobSimttSim LinttSt

l

Wang Similarity• For any term t in DAGA = (A, TA, EA), its S-value related to term A, SA(t), is

defined as:••

– where we is the semantic contribution factor for edge e EA linking term t with its child term t’. In DAGA, GO term A is the most specific term and we define its contribution to its own semantics as 1. Other terms in DAGA are more general and, hence, contribute less to the semantics of GO term A. Therefore, we have 0 < we < 1.

• The semantic value of GO term A, SV(A), as: •

– The semantic contribution factors for “is-a” and “part-of” relations can be set to different values, for example, 0.8 and 0.6 respectively.

• Given DAGA = (A, TA, EA) and DAGB = (B, TB, EB) for GO terms A and B respectively, the semantic similarity between these two terms, SGO(A, B), is defined as

•

•– where SA(t) is the S-value of GO term t related to term A and SB(t) is the S-value of

GO term t related to term B. 39

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AS

AeA

A

if )}(|)(max{)(

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BAS BA TTtBA

GO

Semantic Similarity between Proteins

• Best match average similarity. The similarity between two proteins is based on average of best match GO term pairs. Given two proteins p1 and p2, let go1 and go2 represent their corresponding sets of GO terms. First, the similarity between one GO term t and a set of GO terms, go = {t1, t2, … tk) is defined as the maximum similarity between the t and any member in set go.

• • • Therefore, the similarity between two proteins can be defined as the

weighted average of the term similarity scores:• •

– where m is the number of terms in go1 and n is the number of terms in go2. In the best match average protein similarity measures, proteins with more GO terms annotated will have more influence more on the overall similarity score

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),(),(

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Semantic Similarity between Proteins

• Average similarity. The similarity between two proteins, p1 and p2, is the average of similarities among all pairs of two GO term sets, go1 and go2:

• •

– where m is the number of terms in go1 and n is the number of terms in go2.

• Maximum similarity. The similarity between two proteins, p1 and p2, is the maximum similarity among all pairs of two GO term sets, go1 and go2:

• •

– where m is the number of terms in go1 and n is the number of terms in go2.

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ttSim

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