Chado introduction

Ontology-oriented databases: Chado and OBD Chris Mungall Lawrence Berkeley Labs

Outline Chado GMOD & Model Organism Databases Genomics data in Chado using SO OBD NCBO & OBD Requirements RDF and the semantic web SPARQL endpoints

Chado: what is it? A relational database schema for biological data Part of the Generic Model Organism Database (GMOD) project http://www.gmod.org Interoperable tools for Model Organism Databases Chado was originally built for MODs

A brief introduction to MODs Some Model Organism Databases: FlyBase (D melanogaster) WormBase (C elegans) MGD (M musculus) … What does a MOD organisation do? Curate and integrate data on a specific species or taxon Provide a web portal for the community What are the database requirements for a MOD?

Must store representations of genes and genomic entities Sequence data Exon-intron structure Noncoding genes Curated and computed features Entities with unusual transcriptional properties And more…

Must store other data types pertinent to that organism Including, but not limited to: Expression Interaction Genetic and phenotypic Priorities amongst MODs differ Different MOs have different biological and experimental characteristics E.g. D melanogaster and genetics

Must house rich annotation data using ontologies GO (Gene Ontology); Anatomical Ontologies; Phenotype Ontologies

Must track provenance and evidence for data MOD data is often curated from the literature Other sources Computes High throughput data Imaging

Must be an integrated source of data Must drive Web Portal http://www.flybase.org http://www.wormbase.org http://www.yeastgenome.org Links out to external resources GO, Ensembl, UniProt, … Substantial amount of records managed locally in single integrated database

Origins of Chado Chado was originally developed for FlyBase Integration of GadFly (Berkeley) and previous FlyBase database Chado later adopted by GMOD and other some individual MODs Popular amongst ‘newer’ MODs; eg Paramecium Also used outside MOD community TIGR Jenalia Farm Research Campus

Chado key concepts Tightly Integrated foreign key relations between entities Contrast with federated model Module System New modules can be ‘slotted in’ Some modules are mandatory Generic and extensible uses ontologies and terminologies for typing Highly normalised Community & open source

Chado modules Core general (dbxrefs) cv (ontologies) pub (bibliographic) audit Domains sequence (genomics) phenotype expression RAD map genetic phylogeny organism event

Identifiers: dbxref s All public records identified using bipartite scheme Not just external cross-references DB Authority must be specified Distinct table Can be associated with URIs (db, accession, version[optional]) Records can also get secondary dbxrefs Examples: GO:0000001, FlyBase:FBgn0000001

Ontologies and terminologies are central to Chado Ontology - A formal representation of some portion of biological reality eye what kinds of things exist? what are the relationships between these things? ommatidium sense organ eye disc is_a part_of develops from

Ontologies: cv module Based on GO DB Schema and OBO format spec key concepts cvterm (a term, or class in an ontology) cvterm_relationship DAGs Subject-predicate-object Cv (an ontology or terminology)

Subset of Sequence Ontology transcript Part_of Transcript region Transcript region Is_a exon Object Type Subject

Genomics: Sequence module some key concepts (a subset): Feature A genomic entity (gene, intron, SNP, chromosome, ..) Featureloc A relative location in sequence coordinates feature_relationship A pairwise relation between two features e.g. exon to transcript Featureprop Tag-value data for a feature feature_cvterm Ontology-based annotation

Feature table Features have sequences Sequence are not independent entities Embedded in feature table All features reside in same table Genes, exons, chromosomes, SNPs, .. Typed using Sequence Ontology (SO) Optional extra: Automatically generated SQL view layer

Feature Graphs: the feature_relationship table Feature graphs (FGs) Subject-predicate-object Predicates (types) are cvterms

Example: alternately spliced gene 7 features: 1 gene 2 transcripts 4 exons Not shown: polypeptide A (transcript) Part_of 4 (exon) B (transcript) Part_of 3 (exon) A (transcript) Part_of 3 (exon) B (transcript) Part_of 2 (exon) G (gene) Part_of B (transcript) A (transcript) Part_of 1 (exon) G (gene) Part_of A (transcript) Object Predicate Subject

Feature graph configurations are constrained by SO SO determines ontological relations between features Eg: Exon part_of transcript Standard rules for is_a E.g. X is_a Y, Y part_of Z => X part_of Z See OBO Relation ontology http://www.obofoundry.org/ro Rules must be encoded outside standard relational schema

Declarative programming: SQL Functions Powerful, but optional PostgreSQL only Can be ported Separation of interface from implementation Sequence operations Transcription, translation Feature Graph operations Deduction of implicit features (eg introns) Location Graph operations Projection, mereological relations Related: Tata S, Patel JM, Friedman JS, and Swaroop A Declarative querying for biological sequence databases Proc of the 22nd International Conference on Data Engineering (ICDE), April 3-7, Atlanta, GA, 2006.

Chado: ongoing work Chado for phenotype (EQ) data With FlyBase, ZFIN, DictyBase Chado for evolutionary science In collaboration with NESCENT Documentation! Helpdesk (NESCENT) More GMOD integration Unified Architecture for GMOD? Latest Obo format features Allow for post-composition of complex terms

NCBO: OBO and OBD OBO: Open Bio Ontologies Http://obo.sourceforge.net http://www.obofoundry.org NCBO BioPortal; access to: OBO ontologies OBD annotations Current DBPs Fly & fish mutant phenotype annotation Linking to disease HIV Clinical trial analysis

OBD: Storing biomedical annotations Requirements different from Chado Domain scope All of biology and biomedicine Ontologies used for annotation Not just OBO Data integration Index minimum amount of data Link to external data where appropriate Provide and use data services Requirements partially met by semantic web technology

The Semantic Web Datamodel Based on RDF triples Subject-predicate-object Each element is a URI Various serialisations: RDF/XML N3, N-Triples Multiple APIs, QLs and storage options RDF Graphs constrained by ontologies Expressed in RDF Schema, OWL

OBD ‘Schema’: formal ontology of annotation Within OBO Foundry Framework - uses OBO upper ontology

Implementing OBD using SemWeb technology OBD-Sesame 3rd party triplestore Relational or in-memory Lacks native OWL support Performance issues OBD-SQL Developed at Berkeley Reuse Chado methodology, code ‘ Triplestore’ with extras Reduces triple overhead with common patterns

Wrapping databases as SPARQL endpoints A lot of data in existing relational databases like Chado Goal: make available as distributed resource in OBD compliant way Solution: d2rq declarative mappings and SPARQL Progress: GO Database SPARQL endpoint: http://yuri.lbl.gov:9000/ Chado and OBD mappings coming soon Application: Integration of annotations through genome dashboard

GO annotations OBD Disease/pheno annotations Genome server MOD D2rq D2rq DAS Sesame Usage scenario: AJAX Gbrowse (http://genome.biowiki.org) Annotation info sparql DAS/2 sparql sparql

Conclusions Flexible hypernormalized schemas Performance penalties Too much freedom expression? Ontologies + reasoners provide some constraints; eg SO Open world assumption Federation vs tight integration Tight integration is required for MODs As more data types become available dynamic integration will be key RDF and SPARQL is one solution

Thanks LBL Shengqiang Shu Mark Gibson Nicole Washington Seth Carbon John Day Richter Chris Smith Karen Eilbeck Sima Misra Suzanna Lewis FlyBase Dave Emmert Pinglei Zhou Peili Zhang Aubrey de Grey Paul Leyland William Gelbart HHMI Gerry Rubin GMOD, Nescent Scott Cain Sohel Merchant Eric Just Sierra Moxon Andrew Uzilov Brian Osborne Ian Holmes Lincoln Stein

Feature localisation Interbase Simplifies code All localisations relative Location Graph (LG) Recursive/nested locations allowed

Recursive location graphs Locations can be nested Finished genomes typically flat; depth(LG)=1 Unfinished genomes, heterochromatin may require 2 (rarely more) levels features located relative to contigs Contigs related relative to chrmosomes May be a requirement to change coordinates at each level independently

Nested LGs Redundant localisations can be used to ‘flatten’ LG Group>0 indicates denormalised/flattened LG - must be recalculated if group=0 coordinates change 1 0 0 group chrom1 12000..13000[+] contig1 chrom1 12100..13100[+] exon1 contig1 100..200[+] exon1 Srcfeature Loc Feature

Relational featurelocs A relation between two or more locations Matches, sequence variants Indicated using rank column Use case: SNPs Simple way to query for variants introducing premature termination of translation Combine relational featurelocs and redundant featurelocs 3+ featureloc pairs: Sequence of SNP on reference and variant genome (+ location on reference) Same on transcripts Same on polypeptides

OWL entailment genomics use case SO defines ‘TE gene’ as: A SO:gene which is part_of a SO:TE In OWL: Class(TE_Gene complete Gene part_of(TE)) Result: Queries for ‘SO:TE_gene’ return features not explicitly annotated as such Compare: Chado Equivalent rules to be added PostgreSQL functions? Oboedit reasoner adapter?

Chado introduction

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Chado introduction