1. Lecture 7.11 The Ensembl Database Erin Pleasance Steven Jones Canada’s Michael Smith Genome Sciences Centre, Vancouver

2. Lecture 7.12 www.ensembl.org

3. Lecture 7.13 What is Ensembl? Public annotation of mammalian and other genomes Open source software Relational database system The future of genomic bioinformatics?

4. Lecture 7.14 The Ensembl Project “Ensembl is a joint project between EMBL European Bioinformatics Institute and the Sanger Institute to develop a software system which produces and maintains automatic annotation on eukaryotic genomes. Ensembl is primarily funded by the Wellcome Trust”

5. Lecture 7.15 The Ensembl Project “The main aim of this campaign is to encourage scientists across the world - in academia, pharmaceutical companies, and the biotechnology and computer industries - to use this free information.” - Dr. Mike Dexter, Director of the Wellcome Trust

6. Lecture 7.16 Diagram of contigview as “what we want in the end” Goal: An Accessible, Annotated Genome

7. Lecture 7.17 Ensembl Software System Uses extensively BioPerl (www.bioperl.org) The free mySQL database Entire Ensembl code base is freely available under Apache open source license. Mainly written in Perl, extensions in C. Some viewers have been written in Java (e.g. Apollo).

8. Lecture 7.18 Ensembl Genome Annotation Utilizes raw DNA sequence data from public sources Creates a tracking database (The “Ensembl database”) Joins the sequences - based on a sequence scaffold or “Golden Path” Automatically finds genes and other features of the sequence Associates sequence and features with data from other sources Provides a publicly accessible web based interface to the database

9. Lecture 7.19 The Genome Problem The problem with the genome (particularly human) is that it is “large, complicated, and opaque to analysis” (Ewan Birney, Ensembl) Genome features to identify include: –Genes: protein coding, RNA, pseudogenes –Regulatory elements –SNPs, repeats, etc….

10. Lecture 7.110 DNA sequence in Ensembl Sequences are determined in fragments (contigs) Features cross boundaries between fragments Entire sequence too large and changes too much (constantly updated and reassembled) to be stored as one long database entry

11. Lecture 7.111 DNA sequence in Ensembl Core design feature is the “virtual contig” object Allows genome sequence to be accessed as a single large contiguous sequence even though it is stored as a collection of fragments VC object handles reading and writing features to the DNA sequence

12. Lecture 7.112 Ensembl Gene Build System Three-part gene build system –“Best in genome” matches for known genes –Alignment of homologous genes –Ab initio gene finding Genes predicted on repeat-masked DNA All genes predicted based on experimental (available sequence) evidence

13. Lecture 7.113 “Best in genome” predictions Find known proteins from SPTREMBL on genome using pmatch Incorporate cDNAs using exonerate and EST_genome –Align with gaps placed preferentially at splice consensus sites –Allows prediction of 5’ and 3’ UTRs Refine predictions using genewise

14. Lecture 7.114 “Best in genome” predictions ContigView of best in genome gene with associated evidence Known gene (p53) Proteins aligned cDNAs aligned UTRs predicted Unigene clusters aligned Alignments shown in ContigView

15. Lecture 7.115 Homology predictions Align homologous proteins using BLAST, genewise –Paralogs (from same organism) –Orthologs (from closely related organisms) Assemble novel genes

16. Lecture 7.116 Ab initio gene predictions Use Genscan to identify novel exons Confirm exons by BLAST to known proteins, mRNAs, UniGene clusters Based on ab initio predictions but require homology evidence ContigView of homology gene with associated evidence Novel gene GenScan predictions Proteins aligned Unigene clusters aligned

17. Lecture 7.117 Pseudogenes Many pseudogenes also predicted

18. Lecture 7.118 Ensembl Gene Build System Resulting “Ensembl genes” are highly accurate with low false positive rates Ensembl human gene identifiers are 95% stable between builds Snapshot or stats on genes

19. Lecture 7.119 Ensembl EST genes ESTs not accurate enough to produce Ensembl genes, but important especially for identifying alternative transcripts Create an independent set of “EST genes” Known gene Unigene clusters aligned EST genes

20. Lecture 7.120 Ensembl EST genes Map ESTs to genome using Exonerate, BLAST, and EST2Genome Define transcripts by merging redundant ends, setting splice sites to common ends –Finds splice sites and defines UTRs –Alternative transcript predicted if at least one alternatively spliced EST exists Process transcripts with Genomewise to find longest ORF for each

21. Lecture 7.121 Ensembl EST genes Evidence for genes shown (ExonView)

22. Lecture 7.122 Manual gene annotation: Otter Manual annotation done with applications eg. Apollo Otter database/server allows manual annotations to be integrated with automated annotations

23. Lecture 7.123 Manually curated genes: VEGA Chromosomes 6,7,13,14, 20 and 22 contain manually curated genes from VEGA database

24. Lecture 7.124 Gene information in Ensembl: GeneView

25. Lecture 7.125 Transcript information in Ensembl: TransView

26. Lecture 7.126 Protein information in Ensembl: ProteinView

27. Lecture 7.127 Comparative genomics in Ensembl Gene orthologue pairs: Human Mouse Rat Fugu Zebrafish C. elegans C. briggsae Fly Mosquito DNA homology: Human Mouse Rat

28. Lecture 7.128 Comparative genomics in Ensembl: Gene orthologs Gene ortholog pairs shown in GeneView Calculated by BLAST (reciprocal best BLAST hits, or BLAST + synteny) dN/dS = nonsynonymous/synonymous change (measure of selection)

29. Lecture 7.129 Comparative genomics in Ensembl: DNA homology DNA homology shown in ContigView Mouse and rat homology

30. Lecture 7.130 Comparative genomics in Ensembl: Synteny Large-scale homology shown in SyntenyView –Synteny = homologous sequence blocks, in same order and orientation

31. Lecture 7.131 Other features in Ensembl Menus provide other feature options Features eg. SNPs and markers have special views

32. Lecture 7.132 Other data sources in Ensembl Ensembl incorporates gene and feature info from many other datasources OMIM SwissProt

33. Lecture 7.133 Other data sources in Ensembl: Link out

34. Lecture 7.134 The Distributed Annotation System Allows viewing third-party annotation of the genomic scaffold Users can choose the annotation they are interested in Features are viewed in consistent user interface/display Allows specialized feature annotation and the comparison of different methodologies

35. Lecture 7.135 DAS: Selecting data

36. Lecture 7.136 GeneDAS GeneDAS allows exchange of annotations on gene level –eg. access to SwissProt annotations from GeneView

37. Lecture 7.137 DAS: Add your own annotations Anyone can add data and upload it to DAS server for others to view

38. Lecture 7.138 Sequence similarity searching Two search methods –SSAHA: very fast, good for identifying near-exact DNA-DNA matches –BLAST: slower but more accurate, can do DNA or protein searches Can search against any species Can search against genomic sequence, cDNAs (Ensembl or Genscan), or protein sequences

39. Lecture 7.139

40. Lecture 7.140 Show alignment [A], sequence [S], or ContigView [C] Hits relative to genome

41. Lecture 7.141 BLAST results

42. Lecture 7.142 Data Mining with EnsMart EnsMart - organizes data from Ensembl into a query-optimized database Allows very fast, cross-data source querying Accessible from: –Ensembl website (MartView) –Stand-alone application (MartExplorer) –Command-line interface (MartShell) Extremely powerful for data mining

43. Lecture 7.143 Dataming with Ensmart Mouse homologues for human disease genes. Coding SNPs for all novel kinases. Genes on chromosome 1 expressed in liver. Ensembl genes mapped to RefSeq identifiers. Upstream sequence for all Ensembl genes mapped to U95A chip. Disease related genes between markers (eg D10S255 and D10S259). Transmembrane proteins with an Ig-MHC domain (IPR003006) on chromosome 2. Genes with associated coding SNPs on chromosomal band 5q35.3

44. Lecture 7.144 Choose focus: gene set or SNPS Choose organism (any species in Ensembl)

45. Lecture 7.145 Filter genes based on info about: Region Genes Diseases Expression patterns Multi-species comparisons Protein domains and families SNPs

46. Lecture 7.146 Choose output type: –Features (genes with associated info) –SNPs –Structures (of genes – eg. exons) –Sequences Choose what information to output

47. Lecture 7.147 Multiple Programming Interfaces now exist for Ensembl

48. Lecture 7.148 Another example of how to utilize the Ensembl database – Sockeye www.bcgsc.ca/bioinfo/software

49. Lecture 7.149 Apollo – java viewer www.ensembl.org/apollo

50. Lecture 7.150 Ensembl updates Monthly Include: –Changes in genome builds (with new annotations) –Changes in code or database schema –Additional views and tools on website

51. Lecture 7.151 Pre-Ensembl Full annotation can take weeks Pre-Ensembl site provides in-progress annotation –Placement of known proteins –Ab initio gene predictions –Repeat masking –BLAST and SSAHA searching

52. Lecture 7.152 Ensembl Software System Software can be accessed by FTP Can also be accessed through CVS (concurrent versions system) Possible to set up a mirror of the entire Ensembl system.

53. Lecture 7.153 Further Information The Ensembl Project: www.ensembl.org VEGA: vega.sanger.ac.uk EnsMart: www.ensembl.org/EnsMart/ Distribributed Annotation System: www.biodas.org Human Genome Central Resources: www.ensembl.org/genome/central References: –Ensembl: Hubbard et al, 2002. NAR 30 (1), 38-41. Clamp et al, 2003. NAR 31 (1), 38-42. Birney et al, 2004. NAR 32, D468-D470. –EnsMart: Birney et al, 2004. Genome Res. 14, 160-169.