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“Biomedical computing is entering an age where creative exploration of huge amounts of data will lay the foundation of hypotheses. Much work must still.

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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:

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3 “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

4 3 Kilobytes 6 Megabytes 9 Terabytes 12 Petabytes 15 Exabytes 18 Zettabytes 21 Yottabytes

5 GAATTCCCGGTTCAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTG GGCATACTTCTTTTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATA TTGACCTGATCCTGTTTGCCATCCTCGAAGACGGCCAACAGACGGAATACCTGCCCGCCCCTTGCCGTCGTTTTCACGTACT GTGGTCGTCCCTTGTTTATGGGCAGGCATCCCTCGTGCGTTGGACTGCTCGTACTGTTGGGCGAGGATTCCGTAAACGCCGG CATGTTGTCCACTGAGACAAACTTGTAAACCCGTTCCCGAACCAGCTGTATCAGAGATCCGTATTGTGTGGCCGTGGGGAGA CCCTTCTCGCTTAGCATCGAAAAGTAACCTGCGGGAATTCCACGGAAATGTCAGGAGATAGGAGAAGAAAACAGAACAACAG CAAATACTGAGCCCAAATGAGCGATAGATAGATAGATCGTGCGGCGATCTCGTACTGGTAACTGGTAATTTGATCGATTCAA ACGATTCTGGGTCTCCCCGGTTTTCTGGTTCTGGCTTACGATCGGGTTTTGGGCTTTGGTTGTGGCCTCCAGTTCTCTGGCT CGTTGCCTGTGCCAATTCAAGTGCGCATCCGGCCGTGTGTGTGGGCGCAATTATGTTTATTTACTGGTAACTGGTAATTTGA TCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGTCCCGGTTCAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACG TACAACACCTGCCGGTTTTCATTAAGCAGCTGGGCATACTTCTTTTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACC TGGTCGCATTGCCGTTGCCATGTTGCGACATATTGACCTGATCCTGTTTGCCATCCTCGAAGACGGCCAACAGACGGAATAC CTGCCCGCCCCTTGCCGTCGTTTTCACGTACTGTGGTCGTCCCTTGTTAAAGTAACCTGCGGGAATTCCACGGAAATGTCAG GAGATAGGAGAAGAAAACAGAACAACAGCAAATACTGAGCCCAAATGAGCGATAGATAGATAGATCGTGCGGCGATCTCGTA CTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGGTTCTGGCTTACGATCGGGTTTTGGGC TTTGGTTGTGGCCTCCAGTTCTCTGGCTCGTTGCCTGTGCCAATTCAAGTGCGCATCCGGCCGTGTGTGTGGGCGCAATTAT GTTTATTTACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGTCCCGGTTCAATCTCGTA GAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTGGGCATACTTCTTTTCCTTCTCC CTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATATTGACCTGATCCTGTTTGCCAT CCTCGAAGACGGCCAACAGACGGAATACCTGCCCGCCCCTTGCCGTCGTTTTCACGTACTGTGGTCGTCCCTTGTTTATGGG CAGGCATCCCTCGTGCGTTGGACTGCTCGTACTGTTGGGCGAGGATTCCGTAAACGCCGGCATGTTGTCCACTGAGACAAAC TTGTAAACCCGTTCCCGAACCAGCTGTATCAGAGATCCGTATTGTGTGGCCGTGGGGAGACCCTTCTCGCTTAGCATCGAAA AGCTTACGATCGGGTTTTGGGCTTTGGTTGTGGCCTCCAGTTCTCTGGCTCGTTGCCTGTGCCAATTCAAGTGCGCATCCGG CCGTGTGTGTGGGCGCAATTATGTTTATTTACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTT TCTGTCCCGGTTCAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTG GGCATACTTCTTTTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATA TTGACCTGATCCTGTTTGACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGTCCCGGTT CAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTGGGCATACTTCTT TTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATATTGACCTGATCC TGTTTGCCATCCTCGAAGACGGCCAACAGACGGAATACCTGCCCGCCCCTTGCCGTCGTTTTCACGTACTGTGGTCGTCCCT TGTTTATGGGCAGGCATCCCTCGTGCGTTGGACTGCTCGTACTGTTGGGCGAGGATTCCGTAAACGCCGGCATGTTGTCCAC TGAGACAAACTTGTAAACCCGTTCCCGAACCAGCTGTATCAGAGATCCGTATTGTGTGGCCGTGGGGAGACCCTTCTCGCTT AGCATCGAAAAGTAACCTGCGGGAATTCCACGGAAATGTCAGGAGATAGGAGAAGAAAACAGAACAACAGCAAATACTGTGC GGCGATCTCGTACTGGACGGAAATGTCAGGAGATAGGAGAAGAAAA

6 Nucleotide sequence database

7 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

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10 Annotated genome Annotation Depth of knowledge Breadth of knowledge Detailed analysis (typically biological) of single genes Large-scale analysis (typically computational) of entire genome

11 The two major methods of gene prediction sequence comparison ab initio

12 Approaches to gene finding: Generalized hidden Markov models

13 Limitations of Gene Prediction Programs Good at predicting ORF-containing sequence Prediction of exact exon-intron boundaries difficult Fuse & split genes Cannot predict UTRs Cannot predict nested genes

14 Computational Analysis Fly Alignments Known genes/cDNAs ESTs Transposons Cross-species Sequence Similarities Proteins & ESTs Fly Primate Rodent Worm Yeast Plant Other Insects Other Vertebrates Other Invertebrates Gene Predictions Genie Genscan tRNAscan-SE

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16 Drosophila Gene Collection 1 Pavel Tomancak

17 Embryonic expression of wild-type eve (rust) and a transgene containing the stripe 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 eve

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21 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.

22 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.

23 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

24 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

25 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

26 GO = Three Ontologies Biological Process = goal or objective within cell Molecular Function = elemental activity or task Cellular Component = location or complex

27 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

28 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.

29 cellular_component %membrane %vacuolar membrane %nuclear membrane %intracellular %cell

30 molecular function 5232 terms biological process 6416 terms cellular component 1111 terms all 12,759 terms definitions7735 (61%) September Content of GO

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35 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.

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