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“Biomedical computing is entering an age where creative exploration

“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

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“Biomedical computing is entering an age where creative exploration

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  1. “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.

  2. 3 Kilobytes 6 Megabytes 9 Terabytes 12 Petabytes 15 Exabytes 18 Zettabytes 21 Yottabytes

  3. GAATTCCCGGTTCAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTGGGCATACTTCTTTTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATATTGACCTGATCCTGTTTGCCATCCTCGAAGACGGCCAACAGACGGAATACCTGCCCGCCCCTTGCCGTCGTTTTCACGTACTGTGGTCGTCCCTTGTTTATGGGCAGGCATCCCTCGTGCGTTGGACTGCTCGTACTGTTGGGCGAGGATTCCGTAAACGCCGGCATGTTGTCCACTGAGACAAACTTGTAAACCCGTTCCCGAACCAGCTGTATCAGAGATCCGTATTGTGTGGCCGTGGGGAGACCCTTCTCGCTTAGCATCGAAAAGTAACCTGCGGGAATTCCACGGAAATGTCAGGAGATAGGAGAAGAAAACAGAACAACAGCAAATACTGAGCCCAAATGAGCGATAGATAGATAGATCGTGCGGCGATCTCGTACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGGTTCTGGCTTACGATCGGGTTTTGGGCTTTGGTTGTGGCCTCCAGTTCTCTGGCTCGTTGCCTGTGCCAATTCAAGTGCGCATCCGGCCGTGTGTGTGGGCGCAATTATGTTTATTTACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGTCCCGGTTCAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTGGGCATACTTCTTTTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATATTGACCTGATCCTGTTTGCCATCCTCGAAGACGGCCAACAGACGGAATACCTGCCCGCCCCTTGCCGTCGTTTTCACGTACTGTGGTCGTCCCTTGTTAAAGTAACCTGCGGGAATTCCACGGAAATGTCAGGAGATAGGAGAAGAAAACAGAACAACAGCAAATACTGAGCCCAAATGAGCGATAGATAGATAGATCGTGCGGCGATCTCGTACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGGTTCTGGCTTACGATCGGGTTTTGGGCTTTGGTTGTGGCCTCCAGTTCTCTGGCTCGTTGCCTGTGCCAATTCAAGTGCGCATCCGGCCGTGTGTGTGGGCGCAATTATGTTTATTTACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGTCCCGGTTCAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTGGGCATACTTCTTTTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATATTGACCTGATCCTGTTTGCCATCCTCGAAGACGGCCAACAGACGGAATACCTGCCCGCCCCTTGCCGTCGTTTTCACGTACTGTGGTCGTCCCTTGTTTATGGGCAGGCATCCCTCGTGCGTTGGACTGCTCGTACTGTTGGGCGAGGATTCCGTAAACGCCGGCATGTTGTCCACTGAGACAAACTTGTAAACCCGTTCCCGAACCAGCTGTATCAGAGATCCGTATTGTGTGGCCGTGGGGAGACCCTTCTCGCTTAGCATCGAAAAGCTTACGATCGGGTTTTGGGCTTTGGTTGTGGCCTCCAGTTCTCTGGCTCGTTGCCTGTGCCAATTCAAGTGCGCATCCGGCCGTGTGTGTGGGCGCAATTATGTTTATTTACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGTCCCGGTTCAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTGGGCATACTTCTTTTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATATTGACCTGATCCTGTTTGACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGTCCCGGTTCAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTGGGCATACTTCTTTTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATATTGACCTGATCCTGTTTGCCATCCTCGAAGACGGCCAACAGACGGAATACCTGCCCGCCCCTTGCCGTCGTTTTCACGTACTGTGGTCGTCCCTTGTTTATGGGCAGGCATCCCTCGTGCGTTGGACTGCTCGTACTGTTGGGCGAGGATTCCGTAAACGCCGGCATGTTGTCCACTGAGACAAACTTGTAAACCCGTTCCCGAACCAGCTGTATCAGAGATCCGTATTGTGTGGCCGTGGGGAGACCCTTCTCGCTTAGCATCGAAAAGTAACCTGCGGGAATTCCACGGAAATGTCAGGAGATAGGAGAAGAAAACAGAACAACAGCAAATACTGTGCGGCGATCTCGTACTGGACGGAAATGTCAGGAGATAGGAGAAGAAAA GAATTCCCGGTTCAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTGGGCATACTTCTTTTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATATTGACCTGATCCTGTTTGCCATCCTCGAAGACGGCCAACAGACGGAATACCTGCCCGCCCCTTGCCGTCGTTTTCACGTACTGTGGTCGTCCCTTGTTTATGGGCAGGCATCCCTCGTGCGTTGGACTGCTCGTACTGTTGGGCGAGGATTCCGTAAACGCCGGCATGTTGTCCACTGAGACAAACTTGTAAACCCGTTCCCGAACCAGCTGTATCAGAGATCCGTATTGTGTGGCCGTGGGGAGACCCTTCTCGCTTAGCATCGAAAAGTAACCTGCGGGAATTCCACGGAAATGTCAGGAGATAGGAGAAGAAAACAGAACAACAGCAAATACTGAGCCCAAATGAGCGATAGATAGATAGATCGTGCGGCGATCTCGTACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGGTTCTGGCTTACGATCGGGTTTTGGGCTTTGGTTGTGGCCTCCAGTTCTCTGGCTCGTTGCCTGTGCCAATTCAAGTGCGCATCCGGCCGTGTGTGTGGGCGCAATTATGTTTATTTACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGTCCCGGTTCAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTGGGCATACTTCTTTTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATATTGACCTGATCCTGTTTGCCATCCTCGAAGACGGCCAACAGACGGAATACCTGCCCGCCCCTTGCCGTCGTTTTCACGTACTGTGGTCGTCCCTTGTTAAAGTAACCTGCGGGAATTCCACGGAAATGTCAGGAGATAGGAGAAGAAAACAGAACAACAGCAAATACTGAGCCCAAATGAGCGATAGATAGATAGATCGTGCGGCGATCTCGTACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGGTTCTGGCTTACGATCGGGTTTTGGGCTTTGGTTGTGGCCTCCAGTTCTCTGGCTCGTTGCCTGTGCCAATTCAAGTGCGCATCCGGCCGTGTGTGTGGGCGCAATTATGTTTATTTACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGTCCCGGTTCAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTGGGCATACTTCTTTTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATATTGACCTGATCCTGTTTGCCATCCTCGAAGACGGCCAACAGACGGAATACCTGCCCGCCCCTTGCCGTCGTTTTCACGTACTGTGGTCGTCCCTTGTTTATGGGCAGGCATCCCTCGTGCGTTGGACTGCTCGTACTGTTGGGCGAGGATTCCGTAAACGCCGGCATGTTGTCCACTGAGACAAACTTGTAAACCCGTTCCCGAACCAGCTGTATCAGAGATCCGTATTGTGTGGCCGTGGGGAGACCCTTCTCGCTTAGCATCGAAAAGCTTACGATCGGGTTTTGGGCTTTGGTTGTGGCCTCCAGTTCTCTGGCTCGTTGCCTGTGCCAATTCAAGTGCGCATCCGGCCGTGTGTGTGGGCGCAATTATGTTTATTTACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGTCCCGGTTCAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTGGGCATACTTCTTTTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATATTGACCTGATCCTGTTTGACTGGTAACTGGTAATTTGATCGATTCAAACGATTCTGGGTCTCCCCGGTTTTCTGTCCCGGTTCAATCTCGTAGAACTTGCCCTTGGTGGACAGTGGGACGTACAACACCTGCCGGTTTTCATTAAGCAGCTGGGCATACTTCTTTTCCTTCTCCCTTCCCATGTACCCACTGCCATGGGACCTGGTCGCATTGCCGTTGCCATGTTGCGACATATTGACCTGATCCTGTTTGCCATCCTCGAAGACGGCCAACAGACGGAATACCTGCCCGCCCCTTGCCGTCGTTTTCACGTACTGTGGTCGTCCCTTGTTTATGGGCAGGCATCCCTCGTGCGTTGGACTGCTCGTACTGTTGGGCGAGGATTCCGTAAACGCCGGCATGTTGTCCACTGAGACAAACTTGTAAACCCGTTCCCGAACCAGCTGTATCAGAGATCCGTATTGTGTGGCCGTGGGGAGACCCTTCTCGCTTAGCATCGAAAAGTAACCTGCGGGAATTCCACGGAAATGTCAGGAGATAGGAGAAGAAAACAGAACAACAGCAAATACTGTGCGGCGATCTCGTACTGGACGGAAATGTCAGGAGATAGGAGAAGAAAA

  4. Nucleotide sequence database

  5. 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

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

  7. The two major methods of gene prediction • sequence comparison • ab initio

  8. Approaches to gene finding: Generalized hidden Markov models

  9. 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

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

  11. Drosophila Gene Collection 1 Pavel Tomancak

  12. Alignment of eve 5’ regulatory region • D. melanogaster vs (A) D.erecta (B) D.pseudoobscura (C) D. willistoni and (D) D.littoralis • Embryonic expression of wild-type eve (rust) and a transgene containing the stripe 3 + 7 tertiary element (blue) eve stripe 3 + 7

  13. Gene_Ontology FlyBase - Drosophila - Cambridge & EBI, Harvard Berkeley & Bloomington. SaccharomycesGenome 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.

  14. 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.

  15. 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

  16. 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

  17. Introduction to GO GO: A Gene Ontology GO Objectives: Provide a controlled vocabulary for the description of the molecularfunction and cellular location of gene products, as well as the role of the gene products in basic biologicalprocesses 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

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

  19. 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

  20. 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.

  21. cellular_component membrane intracellular cell nucleus cytoplasm vacuole vacuolar lumen vacuolar membrane nuclear membrane Structure of the Ontologies cellular_component %membrane %vacuolar membrane %nuclear membrane %intracellular %cell <cytoplasm <vacuole <vacuolar membrane <vacuolar lumen <nucleus <nuclear membrane instance of (%), part of (<).

  22. Content of GO • molecular function 5232 terms • biological process 6416 terms • cellular component 1111 terms • all 12,759 terms • definitions 7735 (61%) September 13 2002

  23. 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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