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Analysis Environments For Functional Genomics

Analysis Environments For Functional Genomics. Bruce R. Schatz CANIS Laboratory Institute for Genomic Biology University of Illinois at Urbana-Champaign schatz@uiuc.edu , www.canis.uiuc.edu. Bioinformatics Seminar Department of Computer Science, UIUC February 25, 2005.

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Analysis Environments For Functional Genomics

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  1. Analysis EnvironmentsFor Functional Genomics Bruce R. Schatz CANIS LaboratoryInstitute for Genomic BiologyUniversity of Illinois at Urbana-Champaign schatz@uiuc.edu , www.canis.uiuc.edu Bioinformatics Seminar Department of Computer Science, UIUC February 25, 2005

  2. What are Analysis Environments • Functional Analysis • Find the underlying Mechanisms • Of Genes, Behaviors, Diseases • Comparative Analysis • Top-down data mining (vs Bottom-up) • Multiple Sources especially literature

  3. Building Analysis Environments • Manual by Humans • Interaction user navigation • Classification collection indexing • Automatic by Computers • Federation search bridges • Integration results links

  4. Trends in Analysis Environments Central versus Distributed Viewpoints • The 90s Pre-Genome • Entrez (NIH NCBI) versus • WCS (NSF Arizona) • The 00s Post-Genome • GO (NIH curators) versus • BeeSpace (NSF Illinois)

  5. Pre-Genome Environments Focused on Syntax pre-Web • WCS (Worm Community System) • Search words across sources • Follow links across sources • Words automatic, Links manual Towards Uniform Searching

  6. Post-Genome Environments Focused on Semantics post-Web • BeeSpace (Honey Bee Inter Space) • Navigate concepts across sources • Integrate data across sources • Concepts automatic, Links automatic Towards Question Answering

  7. Paradigm Shift Towards Dry-Lab Biology, Walter Gilbert (Jan 1991) • “The new paradigm, now emerging, is that all the 'genes' will be known (in the sense of being resident in databases available electronically), and that the starting point of a biological investigation will be theoretical. An individual scientist will begin with a theoretical conjecture, only then turning to experiment to follow or test that hypothesis. ... • To use this flood of knowledge [the total sequence of the human and model organisms], which will pour across the computer networks of the world, biologists not only must become computer-literate, but also change their approach to the problem of understanding life. ... • The Coming of Informational Science Correlation of Information across Sources

  8. NCBI Entrez

  9. Community Systems results data (database management) (electronic mail) knowledge (hypertext annotations) literature news (information retrieval) (bulletin boards) Formal Informal browse and share all the knowledge of a community

  10. Worm Community System • WCS Information: Literature BIOSIS, MEDLINE, newsletters, meetings Data Genes, Maps, Sequences, strains, cells • WCS Functionality Browsing search, navigation Filtering selection, analysis Sharing linking, publishing • WCS: 250 users at 50 labs across Internet (1991)

  11. WCS Molecular

  12. WCS Cellular

  13. WCS Publishing

  14. WCS Linking

  15. WCS invokes gm

  16. WCS vis-à-vis acedb

  17. Towards the Interspace • from Objects to Concepts • from Syntax to Semantics • Infrastructure is Interaction with Abstraction Internet is packet transmission across computers Interspace is concept navigation across repositories

  18. THE THIRD WAVE OF NET EVOLUTION CONCEPTS OBJECTS PACKETS

  19. Technology Engineering FORMAL (manual) Electrical IEEE communities INFORMAL groups (automatic) individuals LEVELS OF INDEXES

  20. COMPUTING CONCEPTS ‘92: 4,000 (molecular biology) ‘93: 40,000 (molecular biology) ‘95: 400,000 (electrical engineering) ‘96: 4,000,000 (engineering) ‘98: 40,000,000 (medicine)

  21. Simulating a New World • Obtain discipline-scale collection • MEDLINE from NLM, 10M bibliographic abstracts • human classification: Medical Subject Headings • Partition discipline into Community Repositories • 4 core terms per abstract for MeSH classification • 32K nodes with core terms (classification tree) • Community is all abstracts classified by core term • 40M abstracts containing 280M concepts • concept spaces took 2 days on NCSA Origin 2000 • Simulating World of Medical Communities • 10K repositories with > 1K abstracts (1K w/ > 10K)

  22. Interspace Remote Access Client

  23. Navigation in MEDSPACE For a patient with Rheumatoid Arthritis • Find a drug that reduces the pain (analgesic) • but does not cause stomach (gastrointestinal) bleeding Choose Domain

  24. Concept Search

  25. Concept Navigation

  26. Retrieve Document

  27. Navigate Document

  28. Retrieve Document

  29. Informational Science Computational Science is widely accepted as The Third Branch of Science (beyond Experimental and Theoretical) Genes are Computed, Proteins are Computed, Sequence “equivalences” are Computed. Informational Science is coming to be accepted as The Fourth Branch of Science Based on Information Science technologies for Functional Analysis across Information Sources

  30. Post-Genome Informatics I Comparative Analysis within the Dry Lab of Biological Knowledge • Classical Organisms have Genetic Descriptions. There will be NO more classical organisms beyond Mice and Men, Worms and Flies, Yeasts and Weeds. Must use comparative genomics on classical organisms Via sequence homologies and literature analysis.

  31. Post-Genome Informatics II Functional Analysis within the Dry Lab of Biological Knowledge • Automatic annotation of genes to standard classifications, e.g. Gene Ontology via homology on computed protein sequences. • Automatic analysis of functions to scientific literature, e.g. concept spaces via text extractions. Thus must use functions in literature descriptions.

  32. Informatics: From Bases to Spaces data Bases support genome data e.g. FlyBase has sequences and maps Genes annotated by GO and linked to literature e.g. BeeBase has computed annotations Protein homologies for similar Genes via GO information Spaces support biomedical literature e.g. BeeSpace uses automatically generated conceptual relationships to navigate functions

  33. Gene Ontology

  34. Gene Ontology Gene Symbol Data Source Full Name … Calca MGI calcitonin-related polypeptide Cat-1 Wormbase None Cat-2 Wormbase None CCKR-Human UniProt Cholecystokinin receptor CRF2-Rat UniProt Corticotropin releasing factor Crhr2 RGD corticotrophin relse hormone Egl-10 Wormbase None Egl-30 Wormbase None Feh-1 Wormbase None For FlyBase None

  35. Behavioral Molecular Biologist Biologist Molecular Biology Literature Brain Gene Bee Bee Expression Literature Genome Profiles Flybase, Brain Region WormBase Localization Neuroscience Literature Neuro- scientist Conceptual Navigation in BeeSpace

  36. BeeSpace Analysis Environment • Build Concept Space of Biomedical Literature for Functional Analysis of Bee Genes -Partition Literature into Community Collections -Extract and Index Concepts within Collections -Navigate Concepts within Documents -Follow Links from Documents into Databases Locate Candidate Genes in Related Literatures then follow links into Genome Databases

  37. Question Answering

  38. Functional Phrases <gene> encodes <chemical> • Sokolowski and colleagues demonstrated in Drosophila melanogaster that the foraging gene (for) encodes a cGMP dependent protein kinase (PKG). • The dg2 gene encodes a cyclic guanosine monophosphate (cGMP)- dependent protein kinase (PKG). <chemical> affects/causes <behavior> • Thus, PKG levels affected food-search behavior. • cGMP treatment elevated PKG activity and caused foraging behavior. <gene> regulates <behavior> • Amfor, an ortholog of the Drosophila for gene, is involved in the regulation of age at onset of foraging in honey bees. • This idea is supported by results for malvolio (mvl), which encodes a manganese transporter and is involved in regulating Drosophila feeding and age at onset of foraging in honey bees.

  39. BeeSpace Software Implementation • Natural Language Processing Identify noun and verb phrases Recognize biological entities Compute biological relations • Statistical Information Retrieval Compute statistical contexts Support conceptual navigation

  40. Data Integration (FlyBase Gene) D. melanogaster gene foraging , abbreviated as for , is reported here . It has also been known in FlyBase as BcDNA:GM08338, CG10033 and l(2)06860. It encodes a product with cGMP-dependent protein kinase activity(EC:2.7.1.-) involved in protein amino acid phosphorylation which is a component of the cellular_component unknown . It has been sequenced and its amino acid sequence contains an eukaryotic protein kinase , a protein kinase C-terminal domain , a tyrosine kinase catalytic domain , a serine/Threonine protein kinase family active site , a cAMP-dependent protein kinase and a cGMP-dependent protein kinase . It has been mapped by recombination to 2-10 and cytologically to 24A2--4 . It interacts genetically with Csr . There are 27 recorded alleles : 1 in vitro construct (not available from the public stock centers), 25 classical mutants ( 3 available from the public stock centers) and 1 wild-type. Mutations have been isolated which affect the larval nerve terminal and are behavioral, pupal recessive lethal, hyperactive, larval neurophysiology defective and larval neuroanatomy defective. for is discussed in 80 references (excluding sequence accessions), dated between 1988 and 2003. These include at least 6 studies of mutant phenotypes , 2 studies of wild-type function , 3 studies of natural polymorphisms and 7 molecular studies . Among findings on for function, for activity levels influence adult olfactory trap response to a food medium attractant. Among findings on for polymorphisms, the frequency of for R and for s strains in three natural populations are studied to determine the contribution of the local parasitoid community to the differences in for R and for s frequencies.

  41. BeeSpace Information Sources • Biomedical Literature • Medline (medicine) • Biosis (biology) • Agricola, CAB Abstracts, Agris (agriculture) • Model Organisms (heredity) -Gene Descriptions (FlyBase, WormBase) • Natural Histories (environment) -BeeKeeping Books (Cornell Library, Harvard Press)

  42. Medical Concept Spaces (1998) • Medical Literature (Medline, 10M abstracts) • Partition with Medical Subject Headings (MeSH) • Community is all abstracts classified by core term • 40M abstracts containing 280M concepts • computation is 2 days on NCSA Origin 2000 • Simulating World of Medical Communities • 10K repositories with > 1K abstracts • (1K with > 10K)

  43. Biological Concept Spaces (2005) Compute concept spaces for All of Biology BioSpace across entire biomedical literature 50M abstracts across 50K repositories Use Gene Ontology to partition literature into biological communities for functional analysis GO same scale as MeSH but adequate coverage? GO light on social behavior (biological process)

  44. Paradigm Shift Dissecting Human Disease, Victor McKusick (Feb 2001) • Structural genomics Functional genomics • Genomics Proteomics • Map-based gene discovery Sequence-based gene discovery • Monogenic disorders Multifactorial disorders • Specific DNA diagnosis Monitoring susceptibility • Analysis of one gene Analysis of multi-gene pathways • Gene action Gene regulation • Etiology (mutation) Pathogenesis (mechanism) • One species Several species

  45. Needles and Haystacks Genes • Honey Bees have 13K genes • Perhaps 100 have known functions Paths • Perhaps 30K protein families exist • KEGG has 200 known pathways Statistical Clustering for Interactive Discovery Across Two Orders of Magnitude!

  46. Concept Switching In the Interspace… • each Community maintains its own repository • Switching is navigating Across repositories • use your specialty vocabulary to search another specialty

  47. Semantic region term Concept Space Concept Space CONCEPT SWITCHING • “Concept” versus “Term” • set of “semantically” equivalent terms • Concept switching • region to region (set to set) match

  48. Biomedical Session

  49. Categories and Concepts

  50. Concept Switching

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