EECS 730Introduction to Bioinformatics

Function

Luke HuanElectrical Engineering and Computer Science

http://people.eecs.ku.edu/~jhuan/

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Overview

Gene ontology

Challenges

What is gene ontology

construct gene ontology

Text mining, natural language processing and

information extraction: An Introduction

Summary

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Ontology <philosophy> A systematic account of Existence. <artificial intelligence> (From philosophy) An explicit formal specification

of how to represent the objects, concepts and other entities that are assumed to exist in some area of interest and the relationships that hold among them.

<information science> The hierarchical structuring of knowledge about things by subcategorising them according to their essential (or at least relevant and/or cognitive) qualities.

This is an extension of the previous senses of "ontology" (above) which has become common in discussions about the difficulty of maintaining subject indices.

The philosophy of indexing everything in existence?

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Aristotele’s (384-322 BC) Ontology Substance

plants, animals, ... Quality Quantity Relation Where When Position Having Action Passion

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Ontology and -informatics

In information sciences, ontology is better defined as: “a domain of knowledge, represented by facts and their logical connections, that can be understood by a computer”.

(J. Bard, BioEssays, 2003)

“Ontologies provide controlled, consistent vocabularies to describe concepts and relationships, thereby enabling knowledge sharing”

(Gruber, 1993)

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Information Exchange in Bio-sciences

Basic challenges: Definition, definition, definition

What is a name? What is a function?

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Cell

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Cell

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Cell

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Cell

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Cell

Image from http://microscopy.fsu.edu

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What’s in a name?

The same name can be used to describe different concepts

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What’s in a name?

Glucose synthesis Glucose biosynthesis Glucose formation Glucose anabolism Gluconeogenesis

All refer to the process of making glucose from simpler components

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What’s in a name?

The same name can be used to describe different concepts

A concept can be described using different names

Comparison is difficult – in particular across species or across databases

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Function (what) Process (why)

Drive nail (into wood) Carpentry

Drive stake (into soil) Gardening

Smash roach Pest Control

Clown’s juggling object Entertainment

What is Function? The Hammer Example

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Information Explosion

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Entering the Genome Sequencing Era

Eukaryotic Genome Sequences Year Genome # GenesSize (Mb)

Yeast (S. cerevisiae) 1996 12 6,000

Worm (C. elegans) 1998 97 19,100

Fly (D. melanogaster) 2000 120 13,600

Plant (A. thaliana) 2001 125 25,500

Human (H. sapiens, 1st Draft) 2001 ~3000 ~35,000

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A Common Language for Annotation of Genes from

Yeast, Flies and Mice

What is the Gene Ontology?

…and Plants and Worms

…and Humans

…and anything else!

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http://www.geneontology.org/

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What is the Gene Ontology?

Gene annotation system

Controlled vocabulary that can be applied to all organisms Organism independent

Used to describe gene products proteins and RNA - in any organism

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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

The 3 Gene Ontologies

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Cellular Component where a gene product acts

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Cellular Component

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Cellular Component

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Cellular Component

Enzyme complexes in the component ontology refer to places, not activities.

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Molecular Function

insulin binding

insulin receptor activity

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Molecular Function activities or “jobs” of a gene product

glucose-6-phosphate isomerase activity

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Molecular Function

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

Sets of functions make up a biological process.

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Biological Processa commonly recognized series of events

cell division

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Biological Process

transcription

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Biological Process

Metabolism: degradation or synthesis of biomelecules

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Biological Process

Development: how a group of cell become a tissue

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Biological Process

social behavior

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Ontology applications

Can be used to: Formalise the representation of biological knowledge Standardise database submissions Provide unified access to information through

ontology-based querying of databases, both human and computational

Improve management and integration of data within databases.

Facilitate data mining

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Gene Ontology Structure

Ontologies can be represented as directed acyclic graphs (DAG), where the nodes are connected by edges Nodes = terms in biology Edges = relationships between the terms

is-a part-of

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Parent-Child Relationships

Chromosome

Cytoplasmic chromosome

Mitochondrialchromosome

Plastid chromosome

Nuclear chromosome

A child is a subset or instances of

a parent’s elements

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Parent-Child Relationshipscell

membrane chloroplast

mitochondrial chloroplastmembrane membrane

is-apart-of

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Annotation in GO

A gene product is usually a protein but can be a functional RNA

An annotation is a piece of information associated with a gene product

A GO annotation is a Gene Ontology term associated with a gene product

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Terms, Definitions, IDs Term: MAPKKK cascade (mating sensu Saccharomyces)

Goid: GO:0007244

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

Evidence code: how annotation is done

Definition_reference: PMID:9561267

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Annotation Example

GO Term

Gene Product

nek2

centrosomeGO:0005813

Reference

PMID: 11956323

Evidence Code

IDAInferred fromDirect Assay

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GO Annotation

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GO Annotation

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GO Annotation

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Evidence Code

Indicate the type of evidence in the cited source that supports the association between the gene product and the GO term

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

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Types of evidence codes

Types of evidence code Experimental codes - IDA, IMP, IGI, IPI, IEP Computational codes - ISS, IEA, RCA, IGC Author statement - TAS, NAS Other codes - IC, ND

Two types of annotation Manual Annotation Electronic Annotation

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Beyond GO – Open Biomedical Ontologies

Orthogonal to existing ontologies to facilitate combinatorial approaches Share unique identifier space Include definitions

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Gene Ontology and Text Mining

Derive ontology from text data More general goal: understand text data

automatically

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Finding GO terms

In this study, we report the isolation and molecular characterization of the B. napus PERK1 cDNA, that is predicted to encode a novel receptor-like kinase. We have shown that like other plant RLKs, the kinase domain of PERK1 has serine/threonine kinase activity, In addition, the location of a PERK1-GTP fusion protein to the plasma membrane supports the prediction that PERK1 is an integral membrane protein…these kinases have been implicated in early stages of wound response…

Process: response to wounding GO:0009611

Function: protein serine/threonine kinase activity GO:0004674

Component: integral to plasma membrane GO:0005887

…for B. napus PERK1 protein (Q9ARH1)

PubMed ID: 12374299

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Mining Text DataData Mining / Knowledge Discovery

Structured Data Multimedia Free Text Hypertext

HomeLoan ( Loanee: Frank Rizzo Lender: MWF Agency: Lake View Amount: $200,000 Term: 15 years)

Frank Rizzo boughthis home from LakeView Real Estate in1992. He paid $200,000under a15-year loanfrom MW Financial.

<a href>Frank Rizzo</a> Bought<a hef>this home</a>from <a href>LakeView Real Estate</a>In <b>1992</b>.<p>...Loans($200K,[map],...)

(Taken from ChengXiang Zhai, CS 397cxz, UIUC, CS – Fall 2003)

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Bag-of-Tokens Approaches

Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal. Now we are engaged in a great civil war, testing whether that nation, or …

nation – 5civil - 1war – 2men – 2died – 4people – 5Liberty – 1God – 1…

FeatureExtraction

Loses all order-specific information!Severely limits context!

Documents Token Sets

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Natural Language Processing

A dog is chasing a boy on the playgroundDet Noun Aux Verb Det Noun Prep Det Noun

Noun Phrase Complex Verb Noun PhraseNoun Phrase

Prep PhraseVerb Phrase

Verb Phrase

Sentence

Dog(d1).Boy(b1).Playground(p1).Chasing(d1,b1,p1).

Semantic analysis

Lexicalanalysis

(part-of-speechtagging)

Syntactic analysis(Parsing)

A person saying this maybe reminding another person to

get the dog back…

Pragmatic analysis(speech act)

Scared(x) if Chasing(_,x,_).+

Scared(b1)

Inference

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General NLP—Too Difficult! Word-level ambiguity

“design” can be a noun or a verb (Ambiguous POS) “root” has multiple meanings (Ambiguous sense)

Syntactic ambiguity “natural language processing” (Modification) “A man saw a boy with a telescope.” (PP Attachment)

Anaphora resolution “John persuaded Bill to buy a TV for himself.”

(himself = John or Bill?) Presupposition

“He has quit smoking.” implies that he smoked before.

Humans rely on context to interpret (when possible).This context may extend beyond a given document!

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Reference for GO

Gene ontology teaching resources: http://www.geneontology.org/

GO.teaching.resources.shtml

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References for Text Ming

1. C. D. Manning and H. Schutze, “Foundations of Natural Language Processing”, MIT Press, 1999.

2. S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach”, Prentice Hall, 1995.

3. S. Chakrabarti, “Mining the Web: Statistical Analysis of Hypertext and Semi-Structured Data”, Morgan Kaufmann, 2002.

4. G. Miller, R. Beckwith, C. FellBaum, D. Gross, K. Miller, and R. Tengi. Five papers on WordNet. Princeton University, August 1993.

5. C. Zhai, Introduction to NLP, Lecture Notes for CS 397cxz, UIUC, Fall 2003.

6. M. Hearst, Untangling Text Data Mining, ACL’99, invited paper. http://www.sims.berkeley.edu/~hearst/papers/acl99/acl99-tdm.html

7. R. Sproat, Introduction to Computational Linguistics, LING 306, UIUC, Fall 2003.

8. A Road Map to Text Mining and Web Mining, University of Texas resource page. http://www.cs.utexas.edu/users/pebronia/text-mining/

9. Computational Linguistics and Text Mining Group, IBM Research, http://www.research.ibm.com/dssgrp/

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Acknowledge

Some slides are taken from http://www.tulane.edu/~wiser/cells/.