April 2006 Xosé Mª Fernández European Bioinformatics Institute Ensembl Sep 2006.

April 2006

XosXosé Mª Fernándezé Mª FernándezEuropean Bioinformatics InstituteEuropean Bioinformatics Institute

EnsemblEnsembl

Sep 2006Sep 2006

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• Overview of Ensembl• Making genomes useful• Beyond Ensembl

Outline of talkOutline of talk

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• Overview of Ensembl– The era of sequencing genomes– Exploring genomes– Gene annotation

• Making genomes useful• Beyond Ensembl

Outline of talkOutline of talk

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Ensembl - ProjectEnsembl - Project

• Joint project– EMBL – European Bioinformatics Institute (EBI) – Wellcome Trust Sanger Institute

• Produce accurate, automatic genome annotation• Focused on selected eukaryotic genomes • Integrate external (distributed) biological data• Presentation of the analysis to all via the Web at

http://www.ensembl.org • Open distribution of the analysis the community• Development of open, collaborative software (databases

and APIs)

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http://www.ensembl.org • Open distribution of the analysis the community• Development of open, collaborative software (databases and

APIs)

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Beyond classical Beyond classical ab initioab initio gene predictiongene prediction

• Ensembl automatic gene prediction relies on homology ‘supporting evidence’ to avoid overprediction.

• Classical ab initio gene prediction (eg GENSCAN) relies partly on global statistics of protein coding potentials, not used in the cell

• Genes are just a series of short signals– Transcription start site– Translation start site– 5’ & 3’ Intron splicing signals– Termination signals

• Short signal sequences difficult to recognise over background noise in large genomes

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APIs)

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Species in Ensembl v40Species in Ensembl v40

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The era of sequencing genomesThe era of sequencing genomes

360

450

990 25

70

140

?

550

25070?

1002003004005001000

Million years

340

1500?

?

Chordata

Vertebrata

AmniotaTetrapoda

Teleostei

Urochordata

Arthropoda

NematodaFungi

Red : whole genome assembly availableGreen : whole genome assembly due within the next year in Ensembl

* 19 species currently in Ensembl* 19 species currently in Ensembl+ 10 + 10 Pre! Pre! EnsemblEnsembl

S. cerevisiae (baker’s yeast) *

C. elegans (nematode) *

A. mellifera (honey bee) *

D. rerio (zebrafish) *

D. melanogaster (fruitfly) *A. gambiae (African malaria mosquito) *A. aegypti (yellow fever mosquito) +

C. intestinalis (transparent sea squirt) * C. savignyi (sea squirt) +

T. rubripes (torafugu) *T. nigroviridis (spotted green pufferfish) *

O. latipes (Japanese medaka)

G. aculeatus (Stickleback) +

23

O. aries (sheep)

G. gallus (chicken) *

X. laevis (African clawed frog)

M. musculus (house mouse) *R. norvegicus (Norway rat) *

M. mulatta (rhesus macaque) *P. troglodytes (chimpanzee) *

C. familiaris (dog) *F. catus (cat)E. caballus (horse)S. scrofa (pig)B. taurus (cow) *

310

197

92

M. domestica (opossum) *

170

L. africana (elephant) +

105

41

91

4574

83

65

20

H. sapiens (human) * +

X. tropicalis (western clawed frog) *Amphibia

AvesMetatheria

Mammalia

Eutheria

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APIs)

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http://das.sanger.ac.uk/registryhttp://das.sanger.ac.uk/registry

DAS DAS RegistryRegistry

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http://www.ensembl.orghttp://www.ensembl.org • Open distribution of the analysis the community• Development of open, collaborative software (databases and

APIs)

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APIs)

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• Object model– standard interface makes it easy for others to build

custom applications on top of Ensembl data

• Open discussion of design ([email protected])• Most major pharma and many academics represented

on mailing list and code is being actively developed externally

• Ensembl locally– Both industry & academia

Open source open Open source open standardsstandards

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Ensembl – Open sourceEnsembl – Open source

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Genome ReviewsGenome Reviews

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http://www.ensembl.org • Open distribution of the analysis the community• Development of open, collaborative software (databases

and APIs)

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APIsAPIs• Used to retrieve data from and to store data in

Ensembl databases.• Ensembl Perl API;

– Written in Object-Oriented Perl,

– Foundation for the Ensembl Pipeline and Ensembl Web interface.

• Ensembl Java API;– Written in Java, but similar in layout to the

Perl API,– Foundation for Apollo,– Development will stop in December 2006.

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• Overview of Ensembl– The Era of Sequencing Genomes– Exploring genomes– Gene annotation

• Making genomes useful• Beyond Ensembl

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ACCCAATAGCAGAACAGCTACTGGAACTAAAATCCTCTGATTTCAAATAACAGCCCCGCCCACTACCACTAAGTGAAGTCATCCACAACCACACACCGACCACTCTAAGCTTTTGTAAGATCGGCTCGCTTTGGGGAACAGGTCTTGAGAGAACATCCCTTTTAAGGTCAGAACAAAGGTATTTCATAGGTCCCAGGTCGTGTCCCGAGGGCGCCCACCCAAACATGAGCTGGAGCAAAAAGAAAGGGATGGGGGACTTGGAGTAGGCATAGGGGCGGCCCCTCCAAGCAGGGTGGCCTGGGACTCTTAAGGGTCAGCGAGAAGAGAACACACACTCCAGCTCCCGCTTTATTCGGTCAGATACTGACGGTTGGGATGCCTGACAAGGAATTTCCTTTCGCCACACTGAGAAATACCCGCAGCGGCCCACCCAGGCCTGACTTCCGGGTGGTGCGTGTGCTGCGTGTCGCGTCACGGCGTCACGTGGCCAGCGCGGGCTTGTGGCGCGAGCTTCTGAAACTAGGCGGCAGAGGCGGAGCCGCTGTGGCACTGCTGCGCCTCTGCTGCGCCTCGGGTGTCTTTTGCGGCGGTGGGTCGCCGCCGGGAGAAGCGTGAGGGGACAGATTTGTGACCGGCGCGGTTTTTGTCAGCTTACTCCGGCCAAAAAAGAACTGCACCTCTGGAGCGGGTTAGTGGTGGTGGTAGTGGGTTGGGACGAGCGCGTCTTCCGCAGTCCCAGTCCAGCGTGGCGGGGGAGCGCCTCACGCCCCGGGTCGCTGCCGCGGCTTCTTGCCCTTTTGTCTCTGCCAACCCCCACCCATGCCTGAGAGAAAGGTCCTTGCCCGAAGGCAGATTTTCGCCAAGCAAATTCGAGCCCCGCCCCTTCCCTGGGTCTCCATTTCCCGCCTCCGGCCCGGCCTTTGGGCTCCGCCTTCAGCTCAAGACTTAACTTCCCTCCCAGCTGTCCCAGATGACGCCATCTGAAATTTCTTGGAAACACGATCACTTTAACGGAATATTGCTGTTTTGGGGAAGTGTTTTACAGCTGCTGGGCACGCTGTATTTGCCTTACTTAAGCCCCTGGTAATTGCTGTATTCCGAAGACATGCTGATGGGAATTACCAGGCGGCGTTGGTCTCTAACTGGAGCCCTCTGTCCCCACTAGCCACGCGTCACTGGTTAGCGTGATTGAAACTAAATCGTATGAAAATCCTCTTCTCTAGTCGCACTAGCCACGTTTCGAGTGCTTAATGTGGCTAGTGGCACCGGTTTGGACAGCACAGCTGTAAAATGTTCCCATCCTCACAGTAAGCTGTTACCGTTCCAGGAGATGGGACTGAATTAGAATTCAAACAAATTTTCCAGCGCTTCTGAGTTTTACCTCAGTCACATAATAAGGAATGCATCCCTGTGTAAGTGCATTTTGGTCTTCTGTTTTGCAGACTTATTTACCAAGCATTGGAGGAATATCGTAGGTAAAAATGCCTATTGGATCCAAAGAGAGGCCAACATTTTTTGAAATTTTTAAGACACGCTGCAACAAAGCAGGTATTGACAAATTTTATATAACTTTATAAATTACACCGAGAAAGTGTTTTCTAAAAAATGCTTGCTAAAAACCCAGTACGTCACAGTGTTGCTTAGAACCATAAACTGTTCCTTATGTGTGTATAAATCCAGTTAACAACATAATCATCGTTTGCAGGTTAACCACATGATAAATATAGAACGTCTAGTGGATAAAGAGGAAACTGGCCCCTTGACTAGCAGTAGGAACAATTACTAACAAATCAGAAGCATTAATGTTACTTTATGGCAGAAGTTGTCCAACTTTTTGGTTTCAGTACTCCTTATACTCTTAAAAATGATCTAGGACCCCCGGAGTGCTTTTGTTTATGTAGCTTACCATATTAGAAATTTAAAACTAAGAATTTAAGGCTGGGCGTGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGGTGGGCGGATCACTTGAGGCCAGAAGTTTGAGACCAGCCTGGCCAACATGGTGAAACCCTATCTCTACTAAAAATACAAAAAATGTGCTGCGTGTGGTGGTGCGTGCCTGTAATCCCAGCTACACGGGAGGTGGAGGCAGGAGAATCGCTTGAACCCTGGAGGCAGAGGTTGCAGTGAGCCAAGATCATGCCACTGCACTCTAGCCTGGGCCACATAGCATGACTCTGTCTCAAAACAAACAAACAAACAAAAAACTAAGAATTTAAAGTTAATTTACTTAAAAATAATGAAAGCTAACCCATTGCATATTATCACAACATTCTTAGGAAAAATAACTTTTTGAAAACAAGTGAGTGGAATAGTTTTTACATTTTTGCAGTTCTCTTTAATGTCTGGCTAAATAGAGATAGCTGGATTCACTTATCTGTGTCTAATCTGTTATTTTGGTAGAAGTATGTGAAAAAAAATTAACCTCACGTTGAAAAAAGGAATATTTTAATAGTTTTCAGTTACTTTTTGGTATTTTTCCTTGTACTTTGCATAGATTTTTCAAAGATCTAATAGATATACCATAGGTCTTTCCCATGTCGCAACATCATGCAGTGATTATTTGGAAGATAGTGGTGTTCTGAATTATACAAAGTTTCCAAATATTGATAAATTGCATTAAACTATTTTAAAAATCTCATTCATTAATACCACCATGGATGTCAGAAAAGTCTTTTAAGATTGGGTAGAAATGAGCCACTGGAAATTCTAATTTTCATTTGAAAGTTCACATTTTGTCATTGACAACAAACTGTTTTCCTTGCAGCAACAAGATCACTTCATTGATTTGTGAGAAAATGTCTACCAAATTATTTAAGTTGAAATAACTTTGTCAGCTGTTCTTTCAAGTAAAAATGACTTTTCATTGAAAAAATTGCTTGTTCAGATCACAGCTCAACATGAGTGCTTTTCTAGGCAGTATTGTACTTCAGTATGCAGAAGTGCTTTATGTATGCTTCCTATTTTGTCAGAGATTATTAAAAGAAGTGCTAAAGCATTGAGCTTCGAAATTAATTTTTACTGCTTCATTAGGACATTCTTACATTAAACTGGCATTATTATTACTATTATTTTTAACAAGGACACTCAGTGGTAAGGAATATAATGGCTACTAGTATTAGTTTGGTGCCACTGCCATAACTCATGCAAATGTGCCAGCAGTTTTACCCAGCATCATCTTTGCACTGTTGATACAAATGTCAACATCATGAAAAAGGGAAATGATTCCATAGCGTTATTATGAAAGTAGTTTTGAACTGTAATGGTAGAGGATGAATAGCTCACAATACAAATTTGTCATTTCCCTTTAAGAGAGAATTCCCATTTTATGTGAGAGTCCACATGTTCCTCATACCCATAGTTTGCCACATCTTGAGTACTCTTCAGAATTATTTGAATTTTTTGAATTTTATCTGTGGAATGTATTTTTTTTTTTTTCTTTTTTGAGACACAGTCTTGCT

3500 bps in chr 13…3500 bps in chr 13…

TT

A

T

G

T

C

C

C

C

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Exploring genomesExploring genomes

• Browse genes in genomic context• Display features in and around a particular gene• Explore larger chromosome regions• Search and retrieve information on a gene- and

genome-scale• Investigate genome organisation• Compare genomes

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We make genomes usefulWe make genomes useful

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Making genomes usefulMaking genomes useful

• Interpretation– Where are the interesting parts of the

genome?– What do they do?– How are they related to elements in other

genomes?

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EnsemblEnsemblContigView

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ContigViewContigView - Close-up

Manualannotationvia Vega

Ensembl predictions

Ensembl EST-based predictions

Chromosomes with manual annotation (http://vega.sanger.ac.uk): 1, 6, 7, 9, 10, 13, 14, 16, 18, 19, 20, 22, X and Y

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ContigViewContigView - Navigation

Click and drag mouse to select region

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CytoViewCytoView

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BioMart - a distributed BioMart - a distributed architecturearchitecture

XML XML XML

MySQL ORACLE PostgreSQL

ANSI SQL

XML

XML

XML

XML

XML

XML

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SNPVega

Retrieval

BioMart API

JAVA Perl

MartExplorer MartShell MartView

Schema transformation

MartBuilder MartEditor

Configuration

Databases

Public data (local or remote)

BioMart architectureBioMart architecture

MSD UniProt Ensembl

XML

myMartmyDatabase

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Comparing genomesComparing genomes

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MultiContigMultiContigViewView

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ChallengesChallenges

• What is the right way to calculate evolutionary relationships between these genomes?– How different is the gene build for each

new genome?• Is there novel information to be deduced

from the set of related genomes?• How do we integrate “close” genomes and

genome variation?

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DNA/DNA matches web DNA/DNA matches web displaydisplay

ContigView human BRCA2

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DotterViewDotterView

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Multiple alignmentsMultiple alignments

• Currently 3 sets:– MLAGAN-amniota vertebrates:

– MLAGAN-eutherian mammals:

– MLAGAN primates:

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MultipleMultiple alignmentsalignments

ContigView human BRCA2

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Multiple alignmentsMultiple alignments GeneSeqalignView human BRCA2 v other mammals

...

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GeneGene EvolutionEvolution

• Divergence

• Speciation / Duplication

• Change within allelic population

• Point Mutations / Selection / Drift

• Exon/domain shuffling

• Transposition / Translocation

• Retroposition (reverse transcription)

Orthologues and ParaloguesOrthologues and Paralogues

Reconstruct the Molecular Evolutionary history from the evidence visible within the known extant genes

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Para/Orthologue Para/Orthologue predictionspredictions

• Homology system (BRH/RHS)

• Pairwise species analysis

• Conservative / missed predictions

• Family system

• MCL Clustering all species (+UniProt)

• No ranking of relationships within cluster

• Liberal / over predict

∆ Tree based analysis to get best of both worlds

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Species treeSpecies tree

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Sequence variation in Sequence variation in EnsemblEnsembl

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Two types of variation dataTwo types of variation data

Natural• Limitless• Dense markers

required• Need for optimal

experimental design (HapMap)

• Human and Anopheles

Managed• Limited strain number• Light density adequate

for some uses• (dense for complete

dataset)• Mouse, Rat

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SNPs in EnsemblMapView: SNP density on chromosome

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TransView &ProtView: SNPs in transcript/ protein

SNPs in Ensembl

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GeneSNPView

• Gene Variation Report• Variations in region of gene• Variations and

consequences

SNPs in Ensembl

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GeneSNPGeneSNPViewView

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Variation dataVariation data

• Recombination variability and population history of a species– “HapMap”

• Includes individual, cohort, population and genotype concepts

• Population substructure, close species and individual variation – Understanding positive and negative selection

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TranscriptSNPTranscriptSNPViewView

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Linkage DisequilibriumLinkage Disequilibrium

LD values between two variants are displayed by means of inverted coloured triangles going from white (low LD) to red (high LD).

LDView

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Manual CurationManual Curation• People are the best at

– Resolving conflicting heterogeneous information– Recognising “out of the ordinary” biology

• For high investment genomes an automated pipeline with human intervention is the endgame– Human and Mouse

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Automatic vs ManualAutomatic vs Manual

AutomaticAutomatic AnnotationAnnotation• Quick whole genome

analysis ~ weeks• Consistent annotation• Use unfinished

sequence or shotgun assembly

• No polyA sites or signals, pseudogene

• Predicts ~ 70% loci

ManualManual AnnotationAnnotation• Extremely slow

~ 3 months for Chr 6• Need finished sequences• Flexible, can deal with

inconsistencies• Most rules have exception• Consult publications as

well as databases

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Manual CurationManual Curation

– 7 (Washington University)– 14 (Genoscope)– 18 (Broad Institute)– 16, 19 (DOE Joint Genome Institute)

• Other groups will also contribute to Vega

Manual annotation of finished clonesVega Genome Browser http://vega.sanger.ac.uk/Currently only chromosomes

– 1, 6, 9, 10, 13, 20, 22, X and Y (Sanger Institute)

http://vega.sanger.ac.uk/

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• Several human gene sets– Ensembl, Vega, NCBI, UCSC, UniProt

• Aim: Converge on, and maintain, a set of fixed CDS structures we are confident in– Unique identifier number and version number (e.g.,

CCDS1.1, CCDS234.1). • Version number will update if either the CDS structure or

the underlying genome sequence at that location changes

• The CCDS set will be mapped forward, maintaining identifiers. All changes to existing CCDS genes will be done by collaboration agreement.

Human gene set Human gene set convergenceconvergence

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Conserved CoDing Conserved CoDing SequenceSequence

CCDS quality requirements:

1. Coding region annotated as full-length (with an initiating ATG and valid stop-codon).

2. Protein can be translated from the genome without frameshifts.

3. Consensus splice-sites are used.

14,795 CDS 16,085 transcripts 13,031 genes

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There are more than genes!There are more than genes!

• RNA genes– “well known” structural RNA genes– Newer miRNA genes– Pseudogenes/duplications a massive headache

• Cis-regulatory motifs– Transcriptional motifs– RNA processing motifs

• Yet unknown other stuff

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PseudogenesPseudogenesFilter retro-transposed (processed) pseudogenes

Query: 3 SRLLLNNGAKMPILGLGTWKSPPGQVTEAVKVAIDVGYRHIDCAHVYQNENEVGVAIQEK 62 S ++LNNG K +LGLGTWKSPPGQV EAVKVAI+ YRHIDC+HV+QN++ QE+Sbjct: 2 SHIMLNNGTKTDMLGLGTWKSPPGQVAEAVKVAINTVYRHIDCSHVHQNKD------QEQ 55

Query: 63 LREQVVKREELFIVSKLWCTYHEKGLVKGACQKTLSDLKLDYLDLYLIHWPTGFKPGKEF 122 L+EQVV+RE LFI+SK W H K LV+G+C+K LS L+LDYLDL+LIHWPTG PGKEFSbjct: 56 LKEQVVRREWLFIISKPWGICHRKCLVRGSCRKVLSGLELDYLDLHLIHWPTGCHPGKEF 115

Query: 123 FPLDESGNVVPSDTNILDTWAAMEELVDEGLVKAIGISNFNHLQVEMILNKPGLKYKPAV 182 LDESG + +GLVKA GISNF HLQ E LNK GLK Sbjct: 116 SFLDESGLI-------------------QGLVKAAGISNF-HLQAERTLNKSGLKLSATG 155

Query: 183 NQIECHPYLTQEKLIQYCQSKGIVVTAYSPLGSPDRPWAKPEDPSLLEDPRIKAIAAKHN 242 LTQE LIQY QSK VTAYSPLGSPDRP AKPEDPSLLEDPRIK IAAKHNSbjct: 156 RS------LTQENLIQYYQSKA-AVTAYSPLGSPDRPRAKPEDPSLLEDPRIKVIAAKHN 208

Query: 243 KTTAQVLIRFPMQRNLVVIPKSVTPERIAENFKVFDFELSSQDMTTLLSYNRN 295 + T+QVL+ QRNLVV P SVT +RIAENFKVFDFELSSQDMT+LLS NRNSbjct: 209 E-TSQVLMWLLTQRNLVVTPTSVTLDRIAENFKVFDFELSSQDMTSLLSCNRN 260

Chromosome 18:8,535,635-8,536,757

Chromosome 7:133,584,367-133,601,097

Mostly dead-on-arrival

Intronless, poly-A tail, short direct repeats

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ncRNAsncRNAs• Functional RNAs (tRNAs, miRNA)• Families share conserved secondary

structure• Low sequence identity• Ribosome• Spliceosome

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miRNAmiRNA• Highly conserved across species• Identified using BLAST genomic vs miRBase

precursors• RNAfold used to test for stem loop• Precursor stem loop sequence ~ 70nt• Mature miRNA ~ 21nt(only 2 nt changes tolerated)

Start with ~ 290,000 BLAST hits

End with 222 miRNA

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• Overview of Ensembl– The Era of Sequencing Genomes– Exploring genomes– Gene annotation

• Making genomes useful– Website– BioMart

• Beyond Ensembl

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Display Display 3D structures3D structures

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Display Display 3D structures3D structures

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GeneDASGeneDAS

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Example: Epigenomic Example: Epigenomic ChIP-chip dataChIP-chip data

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Help!Help!

• context sensitive help pages - click

• access other documentation via generic home page

• email the helpdesk

Ensembl TeamEnsembl TeamJuly 2006July 2006

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Leaders Ewan Birney (EBI), Tim Hubbard (Sanger Institute)

Database Schema and Core API Glenn Proctor, Ian Longden, Patrick Meidl

BioMart Arek Kasprzyk, Damian Smedley, Richard Holland, Syed Haldar

Distributed Annotation System (DAS) Andreas Kähäri, Eugene Kulesha

Outreach Xosé M Fernández, Bert Overduin, Giulietta Spudich, Michael Schuster

Web Team James Smith, Fiona Cunningham, Anne Parker, Steve Trevanion (VEGA), Matt Wood

Comparative GenomicsAbel Ureta-Vidal, Kathryn Beal, Benoît Ballester, Stephen Fitzgerald, Javier Herrero Sánchez, Albert Vilella

Analysis and Annotation PipelineVal Curwen, Steve Searle, Browen Aken, Juilo Banet, Laura Clarke, Sarah Dyer, Jan-Hinnerck Vogel, Kevin Howe, Felix Kokocinski, Stephen Rice, Simon White

Functional Genomics Paul Flicek, Yuan Chen, Stefan Gräf, Nathan Johnson, Daniel Rios

Zebrafish Annotation Kerstin Howe, Mario Caccamo, Ian Sealy

VectorBase Annotation Martin Hammond, Dan Lawson, Karyn Megy

Systems & Support Guy Coates, Tim Cutts, Shelley Goddard

Research Damian Keefe, Guy Slater, Michael Hoffman, Alison Meynert, Benedict Paten, Daniel Zerbino

Ensembl TeamEnsembl Team

Sep 2006Sep 2006

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Training...Training... Somewhere near you Somewhere near you

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Ensembl annotation pipelineEnsembl annotation pipeline

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SNPViewSNPView

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Measures of LD

• D = P(AB) – P(A)P(B)– D ranges from – 0.25 to + 0.25

– D = 0 indicates linkage equilibrium

– dependent on allele frequencies, therefore of little use

• D’ = D / maximum possible value– D’ = 1 indicates perfect LD

– estimates of D’ strongly inflated in small samples

• r2 = D2 / P(A)P(B)P(a)P(b)– r2 = 1 indicates perfect LD

– measure of choice

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BioMart - a distributed BioMart - a distributed architecturearchitecture

XML XML XML

MySQL ORACLE PostgreSQL

ANSI SQL

XML

XML

XML

XML

XML

XML

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SNPVega

Retrieval

BioMart API

JAVA Perl

MartExplorer MartShell MartView

Schema transformation

MartBuilder MartEditor

Configuration

Databases

Public data (local or remote)

BioMart architectureBioMart architecture

MSD UniProt Ensembl

XML

myMartmyDatabase

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SNPs in Ensembl

ContigView: SNPs in genomic context

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Linkage DisequilibriumLinkage DisequilibriumLDTableView

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Atime

Duplication

M 2’

Speciation

Duplication

M 2

A 1 A 2

M 1 H 1

H 2

Inparalogues

OutparaloguesOrthologues

Inparalogues

Inparalogues

Orthologous genes have originated from a single ancestor (often have equivalent functions).Paralogous are genes related via duplication:

•Inparalogues (ortholog_one2one, ortholog_one2many, etc.) duplication follows speciation and •Between_species_paralog (outparalogues). Duplication precedes speciation

Homologue RelationshipsHomologue Relationships

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……in Ensembl…in Ensembl…

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Genome ReviewsGenome Reviews

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• Several human gene sets– Ensembl, Vega, NCBI, UCSC, UniProt

• Aim: Converge on, and maintain, a set of fixed CDS structures we are confident in– Unique unique identifier number and version number

(e.g., CCDS1.1, CCDS234.1). • Version number will update if either the CDS structure or

the underlying genome sequence at that location changes

• The CCDS set will be mapped forward, maintaining identifiers. All changes to existing CCDS genes will be done by collaboration agreement.

Human gene set Human gene set convergenceconvergence

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PseudogenesPseudogenesFilter retro-transposed (processed) pseudogenes

Query: 3 SRLLLNNGAKMPILGLGTWKSPPGQVTEAVKVAIDVGYRHIDCAHVYQNENEVGVAIQEK 62 S ++LNNG K +LGLGTWKSPPGQV EAVKVAI+ YRHIDC+HV+QN++ QE+Sbjct: 2 SHIMLNNGTKTDMLGLGTWKSPPGQVAEAVKVAINTVYRHIDCSHVHQNKD------QEQ 55

Query: 63 LREQVVKREELFIVSKLWCTYHEKGLVKGACQKTLSDLKLDYLDLYLIHWPTGFKPGKEF 122 L+EQVV+RE LFI+SK W H K LV+G+C+K LS L+LDYLDL+LIHWPTG PGKEFSbjct: 56 LKEQVVRREWLFIISKPWGICHRKCLVRGSCRKVLSGLELDYLDLHLIHWPTGCHPGKEF 115

Query: 123 FPLDESGNVVPSDTNILDTWAAMEELVDEGLVKAIGISNFNHLQVEMILNKPGLKYKPAV 182 LDESG + +GLVKA GISNF HLQ E LNK GLK Sbjct: 116 SFLDESGLI-------------------QGLVKAAGISNF-HLQAERTLNKSGLKLSATG 155

Query: 183 NQIECHPYLTQEKLIQYCQSKGIVVTAYSPLGSPDRPWAKPEDPSLLEDPRIKAIAAKHN 242 LTQE LIQY QSK VTAYSPLGSPDRP AKPEDPSLLEDPRIK IAAKHNSbjct: 156 RS------LTQENLIQYYQSKA-AVTAYSPLGSPDRPRAKPEDPSLLEDPRIKVIAAKHN 208

Query: 243 KTTAQVLIRFPMQRNLVVIPKSVTPERIAENFKVFDFELSSQDMTTLLSYNRN 295 + T+QVL+ QRNLVV P SVT +RIAENFKVFDFELSSQDMT+LLS NRNSbjct: 209 E-TSQVLMWLLTQRNLVVTPTSVTLDRIAENFKVFDFELSSQDMTSLLSCNRN 260

Chromosome 18:8,535,635-8,536,757

Chromosome 7:133,584,367-133,601,097

Mostly dead-on-arrival

Intronless, poly-A tail, short direct repeats

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miRNAmiRNA• Highly conserved across species• Identified using BLAST genomic vs miRBase

precursors• RNAfold used to test for stem loop• Precursor stem loop sequence ~ 70nt• Mature miRNA ~ 21nt(only 2 nt changes tolerated)

Start with ~ 290,000 BLAST hits

End with 222 miRNA

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Distributed Annotation SystemDistributed Annotation SystemDASDAS

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Server

Distributed AnnotationDistributed AnnotationExternal Contributors

Server

Database providers

UsersViewer

xml

html

SequencePrograms

Annotation

Server

CoordinateSynchronisation

Server

xml

Dowell, R.D. et al. (2001) “The Distributed Annotation System” BMC Bioinformatics. 2, 7

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Histone-3-Lysine-4-Trimethylation data of Mus musculus chr 17 (GEN-AU)

• DAS serverhttp://www.ebi.ac.uk/das-srv/test3

• dsn requesthttp://www.ebi.ac.uk/das-srv/test3/dsn

• Example feature requesthttp://www.ebi.ac.uk/das-srv/test3/das/

GEN-AU_MEFB1_H3K4me3_vs_MEFB1_NCBIm35/

features?segment=17:19558400,19658400

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Display of uploaded dataDisplay of uploaded data

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browser position chr2:1-10000track name=Ensembl_test description="Ensembl workshop (BED)" color=000000 url=http://www.ebi.ac.uk/~xose/ensembl_test.html2 1000 1100 bed_feature_1 1000 + 2 2000 2100 bed_feature_2 500 + 2 3000 3100 bed_feature_2 100 +

URL-basedURL-based

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Display of data via URLDisplay of data via URL

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Display of uploaded dataDisplay of uploaded data

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#<col1> <col2> <col3> <col4>#<group> <name> <type> <subtype>

Similarity Fake_match_1 homology wublastn Transcription Fake_tscr_1 transcript exonTranscription Fake_tscr_1 transcript exon

#<col5> <col6> <col7> <col8> <col9> <col10>#<chr> <start> <end> <strand><phase> <score>

2 4000 4050 + . 100 2 4200 4300 + . 1002 4400 4500 + . 100

File upload-basedFile upload-based

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ExonerateExonerate

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Display Display 3rd party data3rd party data

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• Phenotyping based on terms in London neurogenetics database*

• Based solely on genomic rather than chromosomal analysis

• Flexible – can be configured to work with a wide range of arrays

• Lists genes implicated in the deletion with their function (if known)

• Collects data on copy number changes in individuals with a normal phenotype as well as those with an abnormal phenotype to aid in the definition of new syndromes and polymorphisms

* With kind permission

DECIPHER reportDECIPHER report

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• Displays the microdeletion/duplication in its genomic location

• Shows whether a similar change has been reported to the database before

• Phenotype visible by ‘mouse-over’, enabling individual records to be compared

DECIPHER – Ensembl DECIPHER – Ensembl interfaceinterface

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GeneDASGeneDAS

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• New data– More species– Variation data– Comparative data– Phylogenetic info

• More comparative views– OrthoView

• LDView add export data in Haploview formathttp://www.broad.mit.edu/mpg/haploview

• Greater integration of user data– New developments in DAS

Future plansFuture plans

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Insulin clusterInsulin cluster

Duplication nodeSpeciation node or leaf

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HaplotypesHaplotypes

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PARPAR

There are 2 pseudoautosomal regions in the human chrX/chrY chromosomes.

•PAR1 region (chrX:1-2709520 2.7 Mb), comprises the beginning tip of the short arm for both. •PAR2 (chrX:154,494,747-154,824,264 about 330kb), comprises the ending tip of the long arm for both.

April 2006 Xosé Mª Fernández European Bioinformatics Institute Ensembl Sep 2006.

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Transcript of April 2006 Xosé Mª Fernández European Bioinformatics Institute Ensembl Sep 2006.