Bioinformatics 2.0/3.0

Bioinformatics 2.0/3.0

Kei Cheung

Yale Center for Medical Informatics

Outline

• Introduction

• Web 2.0

• Web 3.0 – Semantic Web– Topic Map

• Merging Web 2.0 and Web 3.0

Introduction

• The Human Genome Project (HGP) has transformed genome sciences from being experimental to being increasingly computational

• HGP has intensified the growth of bioinformatics• The Web has become a popular medium for accessing

information over the Internet• Numerous bioinformatics databases and tools are Web

accessible• These databases and tools as well as the Web have

become indispensable for modern-day genomic research• Web 1.0 -> Web 2.0 -> Web 3.0

Web 1.0

• It is read-only

• It is about a single person, organization, …

• It is document centric

• It is based on HTML

• It is for human to read

Web 2.0

Web 2.0

• Social networking (wiki, blog, tagging, bookmarking, rating, etc)

• Multimedia content (photo, audio, video, etc)

• Interactive, responsive, and dynamic web interface (Facebook, Flickr, YouTube, etc)

• Mashup (assembly tools and visualization tools)

Folksonomy (Social Tagging)

• Folksonomy is the practice and method of collaboratively creating and managing tags to annotate and categorize content

• In contrast to traditional subject indexing, metadata is not only generated by experts but also by creators and consumers of the content

• Freely chosen keywords are used instead of a controlled vocabulary

http://en.wikipedia.org/wiki/Collaborative

http://en.wikipedia.org/wiki/Tag_%28metadata%29

http://en.wikipedia.org/wiki/Categorization

http://en.wikipedia.org/wiki/Content_%28media_and_publishing%29

http://en.wikipedia.org/wiki/Subject_indexing

http://en.wikipedia.org/wiki/Keyword

http://en.wikipedia.org/wiki/Controlled_vocabulary

Tag Cloud

• A tag cloud (or weighted list in visual design) is a visual depiction of user-generated tags used typically to describe the content of web sites.

http://en.wikipedia.org/wiki/Tag_%28metadata%29

Web 2.0 (cont’d)

• It is decentralized

• It is a community/collaborator model instead of authority/consumer model

• It is fun

• It can be seriously used to share and integrate scientific datasets and algorithms

Bioinformatics Applications of Web 2.0

Wiki Proteins

Nature Precedings (pre-publication research and preliminary findings)

Scientific Podcasts

Multimedia (cont’d)

Journal of Visualized Experiments

myExperiment

Mashup (1): Assembly Tools

• Dapper (scrape web content and convert it into machine readable format)

• Yahoo! Pipes (fetch, filter, and integrate data)

Yahoo! Pipes Demo

Yahoo! Pipes Use Case

GeoCommons: Mashup of Maps

Mashup (2): Visualization Tools

• E.g., Google Earth

Geo-Mashup: Google Earth (tracking H5N1 virus over time)

Bioinformatics Mashup’s

• Mashup of biological entities of the same type– Protein network mashup– Sequence annotation mashup

• Mashup of biological entities of different types

Mashup of pathway data and gene expression data

Calvin cycle pathway associated with gene expressions

Challenges to Data Mashup

• Lack of annotation

• Lack of links

• Lack of link semantics

• Lack of data semantics

• Lack of standards or use of standards

Lack of Semantic Annotation

Kei Tsi Daniel Cheng(this is not me!!)

Kei Cheung (16 years ago)

Kei Cheung(6 months ago)

Lack of Links

colllaborators

Lack of Link Semantics

(?)prototyped

Lack of Data Semantics

<html”<body> …<table><tr><td>Alcohol Dehydrogenase 1B (class I), beta polypeptide</td><td>ADH1B</td></tr> …</table> …</body></html>

Lack of Standards (Use of Standards)

• Different naming rules (based on phenotype, sequence, function, organisms, etc)– Armadillo (fruitflies) vs. i-catenin (mice)– PSM1 (human) = PSM2 (yeast); PSM1 (yeast) = PSM2 (human)– Sonic Hedgehog

• ID proliferation – Different ID schemes: 1OF1 (PDB ID) and P06478 (SwissProt

ID) correspond to Herpes Thymidine Kinase– Lexcial variation: GO1234, GO:1234, GO-1234

• Synonyms vs. homonyms– Dopamine receptor D2: DRD2, DRD-2, D2– PSA: prostate specific antigen, puromycin-sensitive

aminopeptidase, psoriatric arthritis, pig serum albumin

Web 3.0

Web 3.0

• It refers to a third generation of Internet-based services that emphasize machine-facilitated understanding of information in order to provide a more productive and intuitive user experience. – Semantic Web– Topic Map

Semantic Web• "The Semantic Web is an extension of the current web in which

information is given well-defined meaning, better enabling computers and people to work in cooperation." -- Tim Berners-Lee, James Hendler, Ora Lassila, The Semantic Web, Scientific American, May 2001

• It provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries

• It is based on the Resource Description Framework (RDF)– URI for naming/identify web objects– Graph structure (directed acyclic graph or DAG) for connecting web

objects

Resource Description Framework (RDF)

• It is a standard data model (directed acyclic graph) for representing information (metadata) about resources in the World Wide Web

• In general, it can be used to represent information about “things” or “resources” that can be identified (using URI’s) on the Web

• It is intended to provide a simple way to make statements (descriptions) about Web resources

RDF Statement

A RDF statement consists of:• Subject: resource identified by a URI• Predicate: property (as defined in a name space identified by a

URI) • Object: property value (literal) or a resource

A resource can be described by multiple statements.

<?xml version="1.0"?> <rdf:RDF xmlns:rdf=“http://www.w3.org/1999/02/22-rdf-syntax-ns#” xmlns:en=“http://en.wikipedia.org/wiki/” ><rdf:Description about=“http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&list_uids=125”>

<en:name>Alcohol Dehydrogenase 1B (class I), beta polypeptide”></en:name><en:synonym>ADH1B</en:synonym>

</rdf:Description></rdf:RDF>

Graphical & XML Representationhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&list_uids=125

“Alcohol Dehydrogenase 1B (class I), beta polypeptide”

http://en.wikipedia.org/wiki/Namehttp://en.wikipedia.org/wiki/Snynonym

“ADH1B”

RDF Schema (RDFS)

• RDF Schema terms:– Class– Property– type– subClassOf– range– Domain

• Example:<DNASequence, type, Class><Promoter,subClassOf,DNASequence><Protein,type,Class><TranscriptionFactor,subClassOf,Protein><Bind,type,Property><Bind,domain, TranscriptionFactor><Bind,range, Promoter>

Ontologies

• In both computer science and information science, an ontology is a representation of a set of concepts within a domain and the relationships between those concepts.

• It is a shared conceptualization of a domain

• Ontologies are commonly encoded using ontology languages.

http://en.wikipedia.org/wiki/Computer_science

http://en.wikipedia.org/wiki/Information_science

http://en.wikipedia.org/wiki/Domain_of_discourse

http://en.wikipedia.org/wiki/Ontology_language

Web Ontology Language (OWL)

• Latest standard in ontology languages from the W3C

• Built on top of RDF

• OWL semantically extends RDF while it is syntactically the same as RDF

• Three species of OWL– OWL-Lite– OWL-DL– OWL-Full

OWL > RDF/RDFS

• Cardinality restrictions: (e.g., a gene may have more than one transcription factor binding sites)

• Disjointedness of classes: (e.g., mRNA may be classified either as introns or exons)

• Other OWL constructs – uniqueness: (e.g.,a GO term can have only one GO identifier)– unionOf: (e.g., gene may be the unionOf intron and exons– sameAs: specifying synonymous relationship between classes

(e.g., “Cerebellar Purkinje Cell” sameAs “Purkinje Neuron”).

Topic Map• A topic map (an ISO standard) is used represent

information using topics (concepts), associations, and occurrences

• It is used to organize information in a way that can be optimized for navigation.

association

occurrence

Neuroscience Topic Map

Topic Map Encoding/Querying

• XML Topic Map (XTM)

• Top Map Query Language (TMQL)

Visual Topic Maps

• A Visual Topic Map can be defined as a topic map including visual topics. A visual topic is defined by a topic name which refers to a visual content.

NCBI Site Map

Mosaic of Chinese Characters in Stories about the Meaning of Ideograms

Visualization of the del.icio.us Tags in an Interactive Graph

Combining Semantic Web and Topic Map

Topic MapSemantic Web

Visualization

Machinereasoning

Knowledge organization & representation (mapping between XTM and RDF/OWL)

Web 2.0 Meets Web 3.0

• Folksonomy meets ontology– Tags can evolve into standard heavy-weight

ontologies, while light-weight ontologies can be applied to tagging

• Human readability meets machine readability– Visual network vs. semantic network

• Social network meets semantic network– FOAF, semantic wiki

• Syntactic mashup meets semantic mashup– Dapper and yahoo pipes may become ontologically

aware

Conclusions

• Web 2.0 and 3.0 provides a platform for data/tool sharing and integration (mashup) and scientific collaboration

• More use cases are needed• Question?

– While Web 1.0 has played an important role in organizing/disseminating information produced by HGP, can Web 2.0/3.0 offer more to present “big science” projects like ENCODE?

The End

Documents

Bioinformatics 2.0/3.0