BeeBase The Honey Bee Model Organism Database Chris Elsik celsiktamu'edu

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BeeBase - The Honey Bee Model Organism
DatabaseChris Elsikc-elsik_at_tamu.edu
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Outline

• BeeBase - what it is now
• How it works
• Future Plans

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BeeBasehttp//racerx00.tamu.edu/PHP/bee_search.ph
p

• Predicted Gene and Homolog Search Page
• Genome Browser
• Comparative Map Viewer
• Protein Families Database with Bee, Fly and
  Mosquito proteins
• The newest assembly ( release 2.0)
  http//racerx00.tamu.edu/cgi-bin/gbrowse/bee_genom
  e2

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Gbrowse

• A module of the Generic Model Organism Database
  Project (GMOD), www.gmod.org
• A graphical viewer of features along a reference
  sequence
• Based on MySQL and Perl
• The configuration file allows us to
• Change fonts, colors, text.
• Change overview sequence scaffold, contig,
  genetic map, karyotype.
• Define tracks.
• Modify track appearance.

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Gbrowse Internals

• BioPerl Library - allows browser to run on top of
  a variety of database management systems and
  schemata
• BioGraphics module - used to graphically render
  any type of nucleotide or protein feature
• BioDBGFF Database - uses a flat coordinate
  system to represent genomic features. Optimized
  for queries that retrieve features by ID, type or
  region of genome

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Our task is to generate GFF data

• GFF generic feature format
• A standard format that aids data exchange
• Allows you to specify a substring of a biological
  sequence
• The current version (2) uses terms from the
  Sequence Ontology project
• - A set of terms used to describe features on a
  nucleotide or protein sequence. It encompasses
  both "raw" features, such as nucleotide
  similarity hits, and interpretations, such as
  gene models.
• For information on the specifications
• http//www.sanger.ac.uk/Software/formats/GFF/

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Computing Data for Tracks

• Markers
• Compare marker sequences to genome scaffolds
  using BLASTN
• Use ePCR (primersearch) for markers with primers,
  but no sequence
• ESTs
• Compare ESTs to genome scaffolds using fasta or
  BLAT
• Use exonerate (http//www.ebi.ac.uk/guy/exonerate
  /) to predict exon/intron boundaries for each
  match
• Protein Homologs
• Compare protein sequences to genome scaffolds
  using tfastx to identify matches
• Use exonerate to predict exon/intron boundaries
  for each match

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Annotating Tracks

• The most time consuming task in computing tracks
  is providing annotations for protein homologs.
• Annotations come from different sources and are
  in different formats depending on protein
  dataset.
• We use UniProt for all homolog tracks in assembly
  1.1 and 1.2 browsers.
• Assembly 2 uses proteome sets for Drosophila
  (FlyBase), C. elegans (WormBase), Yeast (SGD),
  Mosquito (Ensembl) and Human (Ensembl) to avoid
  redundancy within proteomes.
• The fasta formatted sequences are not annotated
  (except yeast).
• The other insect track will come from UniProt.
• To identify which sequences are insect, we use
  taxon-id and a locally installed NCBI taxonomy
  database.

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CMAP

• CMap is a web-based tool that allows users to
  view comparisons of genetic and physical maps.
• The package also includes tools for curating map
  data.
• MySQL and Perl
• Consists of modules for data, logic (howmaps are
  layed out), and presentation.
• Our work is to modify the configuration file and
  format data.

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Future BeeBase Plans

• Redo protein families analysis after final gene
  prediction set is released add proteins from
  additional model organisms (worm, yeast, mouse,
  human)
• Phylogenetic analysis to identify orthologs
• Gene Ontology assignment
• Create gene pages for each gene, similar to
  FlyBase, using the new Turnkey gmod-web module

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More BeeBase Plans

• Curate literature for orthologs to provide an
  entry into the BeeSpace conceptual navigation
  system.
• Incorporate QTL viewer using Dave Adelsons QTL
  viewer software, which was developed for cattle.
• Incorporate OpenGeneX gene expression database
  and expression data from the BeeSpace project.

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Gene Ontology For Honey Bee
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Gene Ontology Consortiumhttp//www.geneontology.o
rg/

• The goal of the Gene OntologyTM (GO) Consortium
  is to produce a controlled vocabulary that can be
  applied to all organisms even as knowledge of
  gene and protein roles in cells is accumulating
  and changing.
• GO provides three structured networks of defined
  terms to describe gene product attributes.
• Molecular Function Ontology the tasks performed
  by individual gene products examples are
  carbohydrate binding and ATPase activity
• Biological Process Ontology broad biological
  goals, such as mitosis or purine metabolism, that
  are accomplished by ordered assemblies of
  molecular functions
• Cellular Component Ontology subcellular
  structures, locations, and macromolecular
  complexes examples include nucleus, telomere,
  and origin recognition complex

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GO Evidence Codes

• IDA inferred from direct assay - Enzyme assays,
  In vitro reconstitution (e.g. transcription),
  Immunofluorescence (for cellular component), Cell
  fractionation (for cellular component), Physical
  interaction/binding assay
• IEP inferred from expression pattern - useful for
  biological process ontology
• IGI inferred from genetic interaction -
  "Traditional" genetic interactions such as
  suppressors, synthetic lethals, etc., Functional
  complementation, Rescue experiments, Inference
  about one gene drawn from the phenotype of a
  mutation in a different gene
• IMP inferred from mutant phenotype - Any gene
  mutation/knockout, Overexpression/ectopic
  expression of wild-type or mutant genes,
  Anti-sense experiments, RNAi experiments,
  Specific protein inhibitors
• IPI inferred from physical interaction -
  2-hybrid interactions, Co-purification,
  Co-immunoprecipitation, Ion/protein binding
  experiments
• IEA inferred from electronic annotation
• ISS inferred from sequence or structural
  similarity
• IC inferred by curator, TAS traceable author
  statement, NAS non-traceable author statement ,
  ND no biological data available, NR not recorded

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Applying GO to Honey Bee

• We must rely heavily on IEA (inferred from
  electronic annotation - no curator) or ISS
  (inferred from sequence similarity - inspected by
  curator)
• We must make the most reliable inferences
  possible - based on orthology instead of homology

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BackgroundEvolution-based functional inference
and orthology
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Evolution Allows us to Infer Function

• The most powerful method for inferring function
  of a gene or protein is by similarity searching a
  sequence database.
• Our ability to characterize biological properties
  of a protein using sequence data alone stems from
  properties conserved through evolutionary time.
• Homologous (evolutionarily related) proteins
  always share a common 3-dimensional folding
  structure.
• They often contain common active sites or binding
  domains.
• They frequently share common functions.
• Predictions made using similar, but
  non-homologous proteins are much less reliable.

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Orthologs

• Homologs genes that are evolutionarily related
• There are two kinds of homologs
• Orthologs genes in different species that have
  diverged from a common gene in an ancestral
  species.
• Paralogs genes that have diverged due to gene
  duplication.
• Orthologs are more likely than paralogs to have
  conserved function.
• Orthologs cannot be identified using BLAST or
  FASTA sequence comparison alone.
• Reliable ortholog identification requires
  phylogenetic methods.

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Example Gene Tree (with plant genes)
Rice-2b
paralogs
Rice-2a
Maize-2
paralogs
Wheat-2
Sorghum-2
Barley-1
Wheat-1
orthologs
Maize-1
Sorghum-1
Arabidopsis
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Why shouldnt we depend on inferences based on
paralogs?

• Paralogs emerge after a gene duplication.
• Possible fates of duplicated genes
• Loss of function for one of the duplicates - lack
  of selective pressure allows gene to mutate
  beyond recognition
• Emergence of new functional paralogs - one
  duplicate aquires a new function, so selection
  favors its maintenance in the genome
• Sub-functionalization - both duplicates are
  required to maintain the function of the original

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Back to Gene Ontology for Honey BeeProposed
Evidence Codes within ISS

• ISS inferred from sequence similarity
  (inspected by a curator)
• We can break this down into
• Inferred from homology (lowest)
• Inferred from a ortholog in one species
• Inferred orthologs in more than one species, all
  of which have the same GO classification
  (highest).
• What if they dont all have the same GO
  classification? Move up in the diacylic graph to
  a point where GO classifications converge.
• This can be tricky since the graph is diacyclic
  and each node can have more than one parant

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Some Ongoing Gene Ontology Work in the Elsik Lab
- Cattle

• Cattle EST Gene Family Database
• Cattle gene families were created using
  assembled, translated ESTs grouped with
  homologous human protein families.
• Database is searchable using GO for the human
  proteins.
• The next step is phylogenetic analysis to
  identify human/cattle orthologs.

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Searching by Gene Ontology
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Borrowing More From Cattle

• Bovine QTL Database - David Adelson, TAMU

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The Bovine QTL viewer Interface
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Image showing all chromosomes
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Image showing one chromosome
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QTL Details
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OpenGeneX

• Web-based access to database
• PostgreSQL
• Includes as a curation tool a client side Java
  application that formats data in MAGE-ML
• Includes several statistical routines and data
  analysis tools
• Uses R statistical analysis package (open source)

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Acknowledgements

• Collaborators
• Bruce Schatz, Gene Robinson and the BeeSpace
  group, UIUC
• William Gelbart - FlyBase (Harvard University)
• Spencer Johnston (TAMU)
• Danny Weaver, Bee Power LP

• Elsik Lab
• Justin Reese
• Kyounghwa Bae
• Anand Venkatraman
• Shreyas Murthi
• Michael Dickens
• Juan Anzola