Semantic Similarity over Gene Ontology for Multi-label Protein

Subcellular Localization

Shibiao WAN and Man-Wai MAKThe Hong Kong Polytechnic University

Sun-Yuan KUNGPrinceton University

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Outline

1. Introduction and Motivation2. Retrieval of GO Terms3. Semantic Similarity Measures4. Multi-label Multi-Class Classification5. Results6. Conclusions

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Proteins and Their Subcellular Locations

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Subcellular Localization Prediction

• The subcellular locations of proteins help biologists to elucidate the functions of proteins.

• Identifying the subcellular locations by entirely experimental means is time-consuming and costly.

• Computational methods are necessary for subcellular localization prediction.

• Previous research has found that gene ontology (GO) based methods outperform methods based on other protein features (e.g. AA composition).

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Multi-label Problem• Some proteins can simultaneously reside at, or move

between, two or more subcellular locations.• Multi-label (Multi-location) proteins play important

roles in some metabolic processes taking place in multiple subcellular locations.

• State-of-the-art multi-label predictors, such as Plant-mPLoc, iLoc-Plant, and mGOASVM use frequency counts of GO terms as features.

• In this work, we propose using semantic similarity of GO terms as features for multi-label subcellular localization prediction.

GO Extraction by searching GOA

databaseSVM Subcellular

Location(s)

Method’s Flowchart

Semantic Similarity Measure

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GOA Database

BLAST Swiss-ProtDatabase

homolog AC

S

AC

SVM

SVM

M

Multi-label SVM

.

.

.

.

.

.

SS: Semantic Similarity

GO of trainingproteins

Semantic Similarity

Vector

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Gene Ontology Gene ontology is a set of standardized

vocabularies annotating the functions of genes and gene products

GO terms, e.g., GO:0000187 A protein sequence may correspond to 0, 1 or

many GO terms.

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Gene Ontology: Example

Search----GO:0000187 in http://www.geneontology.org/

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GOA Database

• Gene Ontology Annotation database.

– Provide structured annotations to proteins in UniProt Knowledgebase (UniProtKB) and other protein databases using standardized GO vocabularies.

– Include a series of cross-references to other databases.

• Given an Accession Number, the GOA database allows us to find a set of GO terms associated with that accession number.

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GOA DatabaseAccession Number

(AC) GO term(s)

Search A0M8T9 in http://www.ebi.ac.uk/GOA/

1 AC maps to many GO terms !

GO Extraction by searching GOA

database

Finding GO Terms without an Accession Number

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GOA Database

BLAST Swiss-ProtDatabase

homolog AC

S

AC

GO Terms of Qi

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Semantic Similarity Measure

Find Common Ancestors

GO

Database

GO term x

GO term y

A(x,y) ComputingSemantic Similarity

sim(x,y)

SQL QueryAncestors

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Finding Common Ancestors, A(x,y)

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GO:0000187

is_a part_of

Finding Common Ancestors, A(x,y)

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Semantic Similarity MeasureWe use Lin’s measure to estimate the semantic similarity between two GO terms (x and y):

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Semantic Similarity between 2 ProteinsSemantic similarity between 2 proteins (Gi, Gj):

Semantic Similarity Vector:

No. of training proteins

where

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Multi-label SVM Scoring

GO of trainingproteins

GO of Qt

=

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Benchmark Datasets The Plant dataset

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Performance MetricsOverall locative accuracy:

Overall actual accuracy:

Actual accuracy is more objective and stricter!

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Performance Comparison The Plant dataset

Conclusions

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• Our Proposed predictor performs significantly better than

Plant-mPLoc and iLoc-Plant, and also better than mGOASVM, in terms of locative and actual accuracies.

• As for individual locative accuracies, our proposed predictor are significantly higher than the three predictors for all of the 12 locations.

• In terms of GO information extraction, Plant-mPLoc, iLoc-Plant and mGOASVM use the occurrences of GO terms as features, whereas the proposed predictor discovers the semantic relationship between GO terms, from which the semantic similarity between proteins can be obtained.

Web Servers

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Thank you!

Multi-label SVM Classifier

Transformed labels for M-class problem:

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YAC known ?

Retrieve homologs by BLAST; maxk 1k

?maxkk

Retrieve a set of GO termsiki ,G

Multi-label SVM classification

N

Y

Y

N

N

0k

1 kk

Using back-up methods

Using the homolog th-k

?0|, iki|G

Retrieving GO Terms with/without AC

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• The relationships between GO terms in the GO hierarchy can be obtained from the SQL database through the link: http://archive.geneontology.org/latest-termdb/go_daily-termdb-tables.tar.gz.

• We only considered the ‘is-a’ relationship.

Finding Common Ancestors