From EpoDB to EPConDB: Adventures in Gene Expression Databases Chris Stoeckert, Ph.D. Computational Biology and Informatics Laboratory

EpoDB: A Prototype Database for the Analysis of Genes Expressed During Vertebrate Erythropoiesis Retrieve all adult, mammalian b-globin gene transcription units. Retrieve the proximal promoter regions from all genes with a significant change in expression between the BFU stage and erythroblast stage. Display the predicted transcription factor binding sites in the proximal promoter region of the mouse b-globin gene. Stoeckert, Salas, Brunk, Overton (1999) Nucl. Acids Res. 26:288

EpoDB: A Data Warehouse Formed by Information Integration GENE STRUCTURE: GenBank GENE FUNCTION: Swiss-Prot TRANSCRIPTION UNIT (EpoDB) GENE REGULATION: Transfac ,TRRD GENE EXPRESSION: GERD

EpoDB Transcription Unit

Populating EpoDB Database: GenBank Swiss-Prot TRRD GERD Total: 7819 2381 171 65 Yes: 3715 1241 80 65

EpoDB Highlights Reference sequences Controlled vocabularies “gene ontology” Specified sequence retrieval All vertebrates Controlled vocabularies Gene names and family names Experiment descriptions Gene warehouse GenBank, SWISS-PROT, Transfac, literature But no ESTs!

http://www.cbil.upenn.edu/EpoDB

EpoDB Gene Landmark Query

EpoDB limitations Not scalable Manual triage of keyword-selected entries Manual selection of reference genes Did not handle data from high throughput technologies No ESTs Could not represent microarrays, SAGE Could not make use of unannotated genomic sequence Difficult to administer Prolog-based

Chronology of CBIL Systems Gene Expression Sequence Annotation 1995 2001 EpoDB GAIA ParaDB DoTS Relational DB (Oracle) with Perl object layer GUS RAD EPConDB PlasmoDB AllGenes

CBIL Project Architecture Sequence & annotation Gene index (ESTs and mRNAs) Microarray expression data experimental annotation Relational DB (Oracle) with Perl object layer GUS RAD

GUS: Genomics Unified Schema free text Controlled vocabs. GO Species Tissue Dev. Stage Genes, gene models STSs, repeats, etc Cross-species analysis Genomic Sequence RAD RNA Abundance DB Characterize transcripts RH mapping Library analysis Cross-species analysis DOTS Transcribed Sequence Special Features Transcript Expression Arrays SAGE Conditions Ownership Protection Algorithm Evidence Similarity Versioning under development Domains Function Structure Cross-species analysis Protein Sequence Pathways Networks Representation Reconstruction

GUS Object View Gene Genomic Sequence Gene Instance Gene Feature NA RNA RNA Sequence RNA Instance RNA Feature Protein Protein Sequence Protein Instance Protein Feature AA Sequence AA Feature

Clusters vs. Contig Assemblies UniGene Transcribed Sequences (DOTS) CAP4 (Paracel): Consensus Sequences -Alternative splicing -Paralogs BLAST: Clusters of ESTs & mRNAs

“Unassembled” clusters (consensus sequences and new) Incremental Updates of DoTS Sequences Incoming Sequences (EST/mRNA) Make Quality (remove vector, polyA, NNNs) “Quality” sequences AssemblySequence Block with RepeatMasker Blocked sequences Assign to DOTS consensus sequences (blastn at 40 bp length, 92% identity) Cluster incoming sequences that are not covered by consensus sequence. DOTS Consensus Sequences “Unassembled” clusters Assemble DOTS consensus sequences and incoming sequences with CAP4 - initially reassemble CAP4 assemblies (consensus sequences and new) Calculate new DOTS consensus sequence using weighted consensus sequence(s) and new CAP4 assembly. New Consensus sequences Update GUS database

Assembled Transcripts About 3 million human EST and mRNA sequences used Combined into 797,028 assemblies Cluster into 150,006 “genes” Can identify a protein for 76,771 genes And predict a function for 24,127 genes About 2 million mouse EST and mRNA sequences used Combined into 355,770 assemblies Cluster into 74,024 “genes” Can identify a protein for 34,008 genes And predict a function for 15,403 genes

Assembly Validation Alignment to Genomic Sequence via Blast/sim4. preliminary data look good Assembly consistency (Assemblies provide potential SNPs) Add BLAST sim4 figure

Crabtree et al. Genome Research 2001 Bridging Fingerprint Contigs and RH Maps on Mouse Chromosome 5 Crabtree et al. Genome Research 2001 Fingerprint Map Chr. 5 RH Map

Predicting Gene Ontology Functions

RAD Multiple labs Multiple biological systems Multiple platforms Expressed genes? Differentially-expressed genes? Co-regulated genes? Gene pathways?

RAD: RNA Abundance Database Experiment Platform Raw Data Processed Data Algorithm Metadata Compliant with the MGED standards

Microarray Gene Expression Database group (MGED) International effort on microarray data standards: Develop standards for storing and communicating microarray-based gene expression data defining the minimal information required to ensure reproducibility and verifiability of results and to facilitate data exchange (MIAME, MAGEML-MAGEDOM) collecting (and where needed creating) controlled vocabularies/ ontologies. developing standards for data comparison and normalization. The schema is compliant with the minimum annotations recommended by MGED. MIAME: Minimum Information About a Microarray Experiment (common set of concepts that need to be captured in a database to describe gene expression experiments adequately for interpretation, reproduction or critical assessment). MAML: MicroArray Mark-up Language (XML Document Type Definitions of the concepts). http://www.mged.org

Query RAD by Sample or by Experiment Access by Experiment groups Sample info ontologies Image info

Different Views of GUS/RAD Focused annotation of specific organisms and biological systems: organisms biological systems Endocrine pancreas Human Mouse CNS GUS GUS Plasmodium falciparum Hematopoiesis *not drawn to scale*

AllGenes

AllGenes “Erythroblast” Query

AllGenes Enhancements: Annotated Entries

AllGenes Enhancements: Genomic Data

http://plasmodb.org New site

Functional Genomics of the Developing Endocrine Pancreas cDNA libraries from pancreatic tissue Consortium libraries Novel genes relevant dbEST libraries Microarray studies on pancreatic tissue Genome wide-survey for genes expressed Pancreas chip Validated sequences of interest Novel sequences from libraries

www.cbil.upenn.edu/EPConDB

EPConDB: Content and Features Pancreas clone sets Panc Chip Clone sets 1.0, 1.5, 2.0 Transcripts found in consortium libraries Novel transcripts discovered from consortium libraries Microarray results Using Incyte’s GEM (genome-wide survey) Using Panc Chip Genes expressed in pancreas AllGenes queries: function, chromosomal location, name, accession Pathways

Relational DB (Oracle) with Perl object layer EPConDB Architecture Sequence & annotation Gene index (ESTs and mRNAs) Microarray expression data experimental annotation Relational DB (Oracle) with Perl object layer GUS RAD

EPConDB Pathway query

EPConDB Boolean Query

EPConDB History Query

EPConDB: Future Developments Add more microarray results Provide tools for microarray analysis Provide genomic alignments Provide tools for analysis of (putative) promoters

Microarray Analysis: Xcluster Xcluster provided by Gavin Sherlock

Microarray Analysis: R statistics SMA R package from Terry Speed’s group

Microarray Analysis: PaGE

Future EPConDB Query Result

Microarray Analysis: Data download

RAD GUS EST clustering and assembly Identify shared TF binding sites TESS (Transcription Element Search Software) PROM-REC (Promoter recognition) Genomic alignment and comparative Sequence analysis Identify shared TF binding sites

Summary EpoDB provides high quality genes for sequence analysis But is limited in scope AllGenes provides the entire transcriptome for a wide variety of human and mouse tissues Needs to provide high quality genes PlasmoDB provides the entire Plasmodium genome. Integrating EST, SAGE, and microarray data EPConDB provides integration of EST and microarray gene expression data for a specific system Will provide microarray analysis

Acknowledgements http:www.cbil.upenn.edu CBIL: Chris Overton Chris Stoeckert Vladimir Babenko Brian Brunk Jonathan Crabtree Sharon Diskin Greg Grant Yuri Kondrakhin Georgi Kostov Phil Le Li Li Junmin Liu Elisabetta Manduchi Joan Mazzarelli Shannon McWeeney Debbie Pinney Angel Pizarro Jonathan Schug Fidel Salas Juergen Haas Annotation collaborators: Nikolay Kolchanov Alexey Katohkin EPConDB collaborators: Klaus Kaestner Marie Scearce Doug Melton, Harvard Alan Permutt, Wash. U Comparative Sequence Analysis Collaborators: Maja Bucan Shaying Zhao Whitehead/MIT Center for Genome Research PlasmoDB collaborators: David Roos Martin Fraunholz Jesse Kissinger Jules Milgram Ross Koppel, Monash U. Malarial Genome Sequencing Consortium (Sanger Centre, Stanford U., TIGR/NMRC) http:www.cbil.upenn.edu