Presentation on theme: "“Biomedical computing is entering an age where creative exploration of huge amounts of data will lay the foundation of hypotheses. Much work must still."— Presentation transcript:
“Biomedical computing is entering an age where creative exploration of huge amounts of data will lay the foundation of hypotheses. Much work must still be done to collect data and create the tools to analyse it. Bioinformatics, which provides the tools to extract and combine knowledge from isolated data, gives us ways to think about the vast amounts of information now available. It is changing the way biologists do science.” A report to Harold Varmus, June 3 1999.
The Human Proteome ~ 30,000 protein coding genes Expansion of the number of different protein molecules due to: –(a) alternative splicing (30 to 50% increase); –(b) post-translational modifications (5 to 10 fold increase) There could well be about 1 million different protein molecules in the human body
Embryonic expression of wild-type eve (rust) and a transgene containing the stripe 3 + 7 tertiary element (blue) Alignment of eve 5’ regulatory region D. melanogaster vs (A) D.erecta (B) D.pseudoobscura (C) D. willistoni and (D) D.littoralis stripe 3 + 7 eve
Gene_Ontology FlyBase - Drosophila - Cambridge & EBI, Harvard Berkeley & Bloomington. Saccharomyces Genome Data Base - Stanford. Mouse Genome Informatics - Jackson Labs. The Arabidopsis Information Resource - Stanford WormBase - Caltech & CSHL DictyBase - Chicago SwissProt - Hinxton & Geneva The Institute for Genome Research - MD With support from NIH (NHGRI) &AstraZeneca.
The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
What is an Ontology? An ontology is a specification of a conceptualization that is designed for reuse across multiple applications and implementations. …a specification of a conceptualization is a written, formal description of a set of concepts and relationships in a domain of interest. Peter Karp (2000) Bioinformatics 16:269
The Gene Ontology Consortium subscribes to the Manifesto of Liberation Bioinformatics : Open source Open standards Open annotation Open data merci tim hubbard - liberationise extraordinaire de ‘inxton
Introduction to GO GO: A Gene Ontology GO Objectives: Provide a controlled vocabulary for the description of the molecular function and cellular location of gene products, as well as the role of the gene products in basic biological processes Use these terms as attributes of gene products in the collaborating databases Allow queries across databases using GO terms, providing the linking of biological information across species
GO = Three Ontologies Biological Process = goal or objective within cell Molecular Function = elemental activity or task Cellular Component = location or complex
Parent-Child Relationships Hierarchy One-to-many parental relationship Directed acyclic graph - dag Many-to-many parental relationship Each child has only one parent Each child may have one or more parents
Classes of parent-child relationship: ISA (hyponomy) - as in: an elephant is a mammal. PARTOF (meronomy) - as in: a trunk is part of an elephant.
Thank yous Genome annotation: Colleagues in the European and Berkeley Drosophila Genome Projects. FlyBase: Colleagues in Harvard, Berkeley, Bloomington & Cambridge. Gene Ontology: Colleagues in Berkeley, Jackson Labs, Stanford and EBI.