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BeeBase - The Honey Bee Model Organism Database Chris Elsik [email protected] Outline • BeeBase - what it is now • How it works • Future Plans BeeBase http://racerx00.tamu.edu/PHP/bee_search.php • • • • 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_genome2 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. Gbrowse Internals • BioPerl Library - allows browser to run on top of a variety of database management systems and schemata • Bio::Graphics module - used to graphically render any type of nucleotide or protein feature • Bio::DB::GFF Database - uses a flat coordinate system to represent genomic features. Optimized for queries that retrieve features by ID, type or region of genome 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/ 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 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. 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. 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 More BeeBase Plans • Curate literature for orthologs to provide an entry into the BeeSpace conceptual navigation system. • Incorporate QTL viewer using Dave Adelson’s QTL viewer software, which was developed for cattle. • Incorporate OpenGeneX gene expression database and expression data from the BeeSpace project. Gene Ontology For Honey Bee Gene Ontology Consortium http://www.geneontology.org/ • “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 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, Coimmunoprecipitation, 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 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 Background: Evolution-based functional inference and orthology 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. 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. Example Gene Tree (with plant genes) Rice-2b paralogs Rice-2a Maize-2 paralogs Wheat-2 Sorghum-2 Barley-1 Wheat-1 Maize-1 Sorghum-1 Arabidopsis orthologs The outgroup, Arabidopsis is a dicot. The cereals are monocots. Monocots and dicots diverged ~230 million years ago. Monocots diverged from each other ~60 mya. Why shouldn’t 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 Back to Gene Ontology for Honey Bee: Proposed 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 don’t 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 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. Searching by Gene Ontology Borrowing More From Cattle • Bovine QTL Database - David Adelson, TAMU The Bovine QTL viewer Interface Image showing all chromosomes Image showing one chromosome QTL Details 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) Acknowledgements • Elsik Lab – – – – – – Justin Reese Kyounghwa Bae Anand Venkatraman Shreyas Murthi Michael Dickens Juan Anzola • Collaborators – Bruce Schatz, Gene Robinson and the BeeSpace group, UIUC – William Gelbart - FlyBase (Harvard University) – Spencer Johnston (TAMU) – Danny Weaver, Bee Power LP