1. An Introduction to ENSEMBL Cédric Notredame

2. The Top 5 Surprises in the Human Genome Map 1.The blue gene exists in 3 genotypes: Straight Leg, Loose Fit and Button-Fly. 2.Tiny villages of Hobbits actually live in our DNA and produce minute quantities of wool -- which we've been ignorantly referring to as "navel lint" and throwing away for centuries. 3.It's nearly impossible to re-fold it along the original creases. 4.Beer-drinking gene conveniently located next to bathroom-locating gene. and the Number 1 Surprise In The Human Genome Map... 5-Now that there's a map, male scientists will attempt to cure diseases by randomly throwing stuff into beakers, stubbornly refusing to use the map or ask for directions -- all the while insisting the cure is right around the next corner

3. ENSEMBL: Our Scope -What is ENSEMBL ? -Searching Genes in ENSEMBL -Viewing Genes in ENSEMBL? -Doing Research With ENSEMBL? -Where do ENSEMBL Genes Come From

4. Genomes sequences are becoming available very rapidly –Large and difficult to handle computationally –Everyone expects to be able to access them immediately Bench Biologists –Has my gene been sequenced? –What are the genes in this region? –Where are all the GPCRs –Connect the genome to other resources Research Bioinformatics –Give me a dataset of human genomic DNA –Give me a protein dataset Accessing Genomes

5. Set of high quality gene predictions –From known human mRNAs aligned against genome –From similar protein and mRNAs aligned against genome –From Genscan predictions confirmed via BLAST of Protein, cDNA, ESTs databases. Initial functional annotation from Interpro Integration with external resources (SNPs, SAGE, OMIM) Comparative analysis –DNA sequence alignment –Protein orthologs What is It ?

6. Mr ENSEMBL ? Richard Durbin (ACEDB) Ewan Birney (EBI)

7. Scale and data flow –mainly engineering problems Presentation, ease of use –mainly engineering problems Algorithmic –Partly engineering –Partly research Challenges ?

8. ENSEMBL Home

12. Help! context sensitive help pages - click access other documentation via generic home page email the helpdesk HelpDesk / Suggestions

13. Finding What You Need

14. Human homepage

15. Text search

16. BLAST/SSAHA

18. BLAST/SSAHA ????

19. Changing Angle…

21. Anchor View Map View

22. Detailed View Genes, ESTs, CpG etc. 100kb Overview Genes and Markers 1Mb Chromosome Configuration Contig View

23. Contig View close-up Evidence Transcripts red & black (Ensembl predictions) Customising & short cuts Pop-up menu

24. Cyto View

25. Marker View

26. SNP View

27. Synteny View

28. Dotter View

29. Gene View

30. Gene-View

33. Trans View

34. Exon-View

35. Protein-View

38. CDK-like Family-View

39. CDK-like Family-View

40. The Right View On My Gene -Where Is My Gene ? Map View Cyto View Contig View -How Many Transcript for My Gene Gene View Exon View -What is the Function of my Gene Protein View SNP View Family View -How does My Gene compare with other Species Synteny View Dotter View

41. Getting The Stuff Back Home

42. Export-View

43. The aim of EnsMart is to integrate Ensembl data into a single, multi-species, query-optimised database –Requirement for cross-database joins removed. –Query-optimised schema improves speed of data retrieval. Examples –Coding SNPs for all novel GPCRs –The sequence in the 5kb upstream region of known proteases between D1S2806 and D1S2907 –Mouse homologues of human disease genes containing transmembrane domain located between 1p23 and 1q23 Data Mining with EnsMart

44. EnsMart I

45. EnsMart II

46. Asking Questions With ENSEMBL

47. Asking Questions 1-Selecting AND Downloading Genes using -Functional -And Evolutive Criteria 2-Comparing Two Pieces of Genome

48. All The Human Genes -Involved in Cell Death -Associated with a Disease -With a Homologue in Mouse and Chicken Asking A Question with ENSMART What Do You Want ???

49. Which Specie

50. Select the region Where? What kind of Gene ?

51. Select the kind of data Choose An Evolutionnary Trace What Kind of Function ?

52. Select the kind of data Control of Genetic Variation Control of Regulatory Region Control of Biochemical Function

53. Human Gene Cell Death Human Gene Cell Death Mouse Human Gene Cell Death Chicken Human Gene Cell Death C. Elegans 1133 genes1106 genes880 genes338 genes

54. I would like -Chromosome Information -The ID of my sequences -The corresponding OMIM Id -The corresponding Chicken id Asking A Question with ENSMART How Do You Want it Packed ???

57. Come to think of it… -I’d like to take a look at the 5’ upstream regions Asking A Question with ENSMART How Do You Want it Packed ???

60. I Want To know if the Mouse and the Human Genome are conserved around the Human Gene SNX5 Asking A Question with ENSMART What Do You Want ???

71. Where Do ENSEMBL Genes Come From Genebuild

74. Ensembl gene set Ensembl EST genes Ab initio predictions Manual curation (Vega / Sanger) Gene models from other groups Known v. novel genes Gene names & descriptions Evaluating genes and transcripts

75. The Aim…

76. Ensembl transcript predictions evidence other groups’ models manual curation Overview…

77. Automatic Gene Annotation human proteins Ensembl Genes Other proteinscDNAs Pmatch Exonerate GenewiseEst2Genome ESTs Genscan exons Add UTRs EST genes other evidence Merge

78. Place all available species-specific proteins to make transcripts Place similar proteins to make transcripts  Use mRNA data to add UTRs Build transcripts using cDNA evidence Build additional transcripts using Genscan + homology evidence Combine annotations to make genes with alternative transcripts ENSEMBL Geneset

79. blast and Miniseq Human protein sequences SwissProt/TrEMBL/RefSeq pmatch* v. assemblyGenewise *R. Durbin, unpublished Getting Genes from Known Proteins

80. Translatable gene with UTRs cDNAs - Est2Genome – UTRs, no phases proteins - Genewise – phases, no UTRs Adding the UTRs

81. DNA-DNA alignments don’t give translatable genes Protein level Alignment give: – frameshifts and splice sites Genewise (Ewan Birney) –Protein – genomic alignment –Has splice site model –Penalises stop codons –Allows for frameshifts Gene Build is Protein-Based

82. Combine results of all Genewises and Genscans: Group transcripts which share exons Reject non-translating transcripts Remove duplicate exons Attach supporting evidence Write genes to database Making Genes

83. NCBI 34 assembly, released Dec 2003 Ensembl genes: 21,787 (23.762 in release 35) Ensembl coding transcripts: 31,609 (plus 1,744 pseudogenes) Ensembl exons: 225,897 Input human seqs: 48,176 proteins; 86,918 cDNAs Transcripts made from: –Human proteins with (without) UTRs 68% (19%) –Non-human proteins with (without) UTRs2% (9%) –cDNA alignment only0.8% A Typical Human Release: NCBI 34 (Dec 2003)

84. GenesSensitivity ~90% of manual genes are in Specificity ~75% of genes are in the manual sets Exon bpsSensitivity ~70% of manual bps are in exons (90% of coding bps) Specificity ~80% of bps are in manual exons Alternative transcripts per gene manual 3 1.3 Figures are for the gene build on NCBI 33 (human) and manual annotation for chromosomes 6, 14 & 14 Manual Vs Automatic Annotation

85. Data availability Hard evidences in mouse, rat, human Similarity build more important For other species; Structural Issues Zebrafish Many similar genes near each other Genome from different haplotypes C. briggsae Very dense genome Short introns Mosquito Many single-exon genes Genes within genes Configuration Files provide flexibility Each Genebuild is a Story…

86. SpeciesGene numberExons/gene Homo sapiens217878.7 Mus musculus249488.7 Rattus norvegicus237517.9 Danio rerio (zebra fish)200627.9 Caenorhabditis briggsae (nematode) 118847.2 Anopheles gambiae (mosquito) 147074.0 Life in Release 2003

87. Ensembl gene set Ensembl EST genes Ab initio predictions Manual curation (Vega / Sanger) Gene models from other groups Known v. novel genes Gene names & descriptions Evaluating genes and transcripts

88. human proteins Ensembl Genes Other proteinscDNAs Pmatch Exonerate GenewiseEst2Genome ESTs Genscan exons Add UTRs EST genes Other evidence Merge Using ESTs

89. EST analysis Map to genome using Est2Genome (determine strand, splicing) Map ESTs using Exonerate (determine coverage, % identity and location in genome) Filter on %identity and depth (5.5 million ESTs from dbEST – maping of about 1/3) Using ESTs

90. Exonerate Golden path contigs cDNA hits Exonerate positions cDNA sequences to assembly contigs Store hits as Ensembl FeaturePairs in database Exonerate

91. Blast and Est2Genome Virtual contig cDNA hits Filter Blast & Miniseq Est_genome EST2Genome

92. Merge ESTs according to consecutive exon overlap and set splice ends Genomewise Alternative transcripts with translation and UTRs ESTs Reconstructing Alternative Splicing

93. Human ESTs EST transcripts Display limited to 7 at any one point – full data accessible in the databases Display of EST Evidences

94. Ensembl gene set Ensembl EST genes Ab initio predictions Manual curation (Vega / Sanger) Gene models from other groups Known v. novel genes Gene names & descriptions Evaluating genes and transcripts

95. Ab initio Genscan predictions Genscan prediction Evidence supporting Genscan exons

96. Ensembl gene set Ensembl EST genes Ab initio predictions Manual curation (Vega / Sanger) Gene models from other groups Known v. novel genes Gene names & descriptions Evaluating genes and transcripts

97. Manual Curation: VErtebrate Genome Annotation

98. Sanger / Vega manual curation Manual Curation: VEGA

99. Ensembl gene set Ensembl EST genes Ab initio predictions Manual curation (Vega / Sanger) Gene models from other groups Known v. novel genes Gene names & descriptions Evaluating Genes and Transcripts

100. Other models as ‘DAS sources’ Turn on DAS sources FASTAView display Other Gene-Models

101. Ensembl gene set Ensembl EST genes Ab initio predictions Manual curation (Vega / Sanger) Gene models from other groups Known v. novel genes Gene names & descriptions Evaluating Genes and Transcripts

102. Naming takes place after the gene build is completed Transcripts/proteins mapped to SwissProt, RefSeq and SPTrEMBL entries If mapped = ‘known’ : if not = ‘novel’ Require high sequence similarity, but allow incomplete coverage Note:  Difficult for families of closely-related genes  Wrongly annotated pseudogenes may also cause problems Known Vs novel transcripts

103. Ensembl gene set Ensembl EST genes Ab initio predictions Manual curation (Vega / Sanger) Gene models from other groups Known v. novel genes Gene names & descriptions Evaluating Genes and Transcripts

104. Names and descriptions Names taken from mapped database entries Official HGNC (HUGO) name used if available (or equivalent for other species) Otherwise SwissProt > RefSeq > SPTrEMBL Novel transcripts have only Ensembl stable ids Genes named after ‘best-named’ transcript Gene description taken from mapped database entries (source given) Hints:  Orthology can provide useful confirmation  If no description, check for any Family description Gene Names and Descriptors

105. Stability… www.ensembl.org/Docs/wiki/html/EnsemblDocs/Answer006.html

106. Evidence used to build the transcript links to ExonView Mapping to external databases Links to putative orthologues Transcript name Gene name & description Alternative transcripts Geneview and Exonview

107. Compressed tracks Expanded tracks Evidence Tracks in ContigView

108. Improved pseudogene annotation, for all species Upstream regulatory elements - using CpG islands, Eponine predictions, motifs to aid in prediction of transcription start sites Improve use of cDNAs - can already use to add alternatively spliced transcripts Improve UTR extension Make use of comparative data Non coding RNAs - currently filtered out of build sets Future Directions

109. ENSEMBL -Finding the right DATA: ENSMART and BLAST -The central View of ENSEMBL: ContigView -Genome Comparison: Synteny View-ENSEMBL incorporate all the evidences into its gene models

112. Genebuild overview Pmatch Other Proteins Genewise genes with UTRs Human Proteins Genewise genes Genebuilder Supported genscans (optional) Preliminary gene set cDNA genes ClusterMerge Gene Combiner Core Ensembl genes Pseudogenes Final set + pseudogenes Ensembl EST genes Est2Genome Aligned cDNAs Exonerate Human cDNAs Aligned ESTs Human ESTs

113. Place all known genes Map all AVAILABLE species specific proteins in the genome and find gene structure using Genewise Annotate novel genes Use protein from other species to build new transcripts based on homology Use AVAILABLE mRNAs to add UTRs to the built transcripts Use further homology to proteins, mRNAs and ESTs to build transcripts using Genscan exons Combine annotations Annotation Stages

114. SnSp chr130.900.74 chr140.920.77 chr60.940.72 Numbers are for NCBI33 genebuild Gene locus level ENSEMBL predictions cover 90% or more of manually annotated gene structures, with around 75% of the predictions covered by a manual annotation Exon level (based on transcript pairs) Coding exons onlyAll exons SnSpSnSp chr130.830.900.730.78 chr140.780.880.690.77 chr60.850.890.730.76 UTR exons predictions are less accurate than coding exons. 92% of coding exons and 80% of all exons are exact matches Manual Vs Automatic Annotation