An Introduction to ENSEMBL

Cédric Notredame

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

ENSEMBL: Our Scope

-What is ENSEMBL ?

-Searching Genes in ENSEMBL

-Viewing Genes in ENSEMBL?

-Doing Research With ENSEMBL?

-Where do ENSEMBL Genes Come From

• 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

• 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 ?

Mr ENSEMBL ?

Richard Durbin (ACEDB)

Ewan Birney (EBI)

• Scale and data flow– mainly engineering problems

• Presentation, ease of use– mainly engineering problems

• Algorithmic– Partly engineering– Partly research

Challenges ?

ENSEMBL Home

Help!

• context sensitive help pages - click

• access other documentation via generic home page

• email the helpdeskHelpDesk / Suggestions

Finding What You Need

Human homepage

Text search

BLAST/SSAHA

BLAST/SSAHA ????

Changing Angle…

Anchor View

Map View

Detailed ViewGenes, ESTs, CpG etc.100kb

OverviewGenes and Markers1Mb

Chromosome

Configuration

Contig View

Contig View

close-up

Evidence

Transcriptsred & black(Ensembl predictions)

Customising& short cuts

Pop-up menu

Cyto View

Marker View

SNP View

Synteny View

Dotter View

GeneView

Gene-View

Gene-View

Gene-View

Trans View

Exon-View

Protein-View

Protein-View

Protein-View

CDK-like

Family-View

CDK-like

Family-View

The Right View On My Gene

-Where Is My Gene ?Map ViewCyto ViewContig View

-How Many Transcript for My GeneGene ViewExon View

-What is the Function of my GeneProtein ViewSNP ViewFamily View

-How does My Gene compare with other Species

Synteny ViewDotter View

Getting The Stuff Back Home

Export-View

• 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

EnsMart I

EnsMart II

Asking Questions With

ENSEMBL

Asking Questions

1-Selecting AND Downloading Genes using-Functional-And Evolutive Criteria

2-Comparing Two Pieces of Genome

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 ???

Which Specie

Select the regionSelect the region

Where?

What kindof Gene ?

Select the Select the kind of datakind of data

Choose AnEvolutionnary Trace

What Kind of Function ?

Select the Select the kind of datakind of data

Control of Genetic Variation

Control of Regulatory Region

Control ofBiochemicalFunction

Human GeneCell Death

Human GeneCell DeathMouse

Human GeneCell DeathChicken

Human GeneCell DeathC. Elegans

1133 genes 1106 genes 880 genes 338 genes

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 ???

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 ???

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 ???

Where Do ENSEMBLGenes Come From

Genebuild

• 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

The Aim…

Ensembl transcript predictions

evidence

other groups’ models

manual curation

Overview…

Automatic Gene Annotationhuman proteins

Ensembl Genes

Other proteins cDNAs

Pmatch Exonerate

Genewise Est2Genome

ESTs

Genscan exons

Add UTRs

EST genes

other evidence

Merge

• Place all available species-specific proteins to make transcripts

• Place similar proteins to make transcriptsUse 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

blast and Miniseq

Human protein sequencesSwissProt/TrEMBL/RefSeq

pmatch* v. assembly

Genewise

*R. Durbin, unpublished

Getting Genes from Known Proteins

Translatable gene with UTRs

cDNAs - Est2Genome – UTRs, no phases

proteins - Genewise – phases, no UTRs

Adding the UTRs

•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

• 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

• 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) UTRs 2% (9%) – cDNA alignment only 0.8%

A Typical Human Release:NCBI 34 (Dec 2003)

Genes Sensitivity ~90% of manual genes are in Specificity ~75% of genes are in the manual sets

Exon bps Sensitivity ~70% of manual bps are in exons (90% of coding bps)Specificity ~80% of bps are in manual exons

Alternative transcripts per genemanual 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

Data availabilityHard evidences in mouse, rat, human Similarity build more important For other species;

Structural IssuesZebrafish Many similar genes near each other

Genome from different haplotypes

C. briggsae Very dense genomeShort introns

Mosquito Many single-exon genesGenes within genes

Configuration Files provide flexibility

Each Genebuild is a Story…

Species Gene number Exons/geneHomo sapiens 21787 8.7

Mus musculus 24948 8.7

Rattus norvegicus 23751 7.9

Danio rerio (zebra fish) 20062 7.9

Caenorhabditis briggsae (nematode)

11884 7.2

Anopheles gambiae (mosquito)

14707 4.0

Life in Release 2003

• 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

human proteins

Ensembl Genes

Other proteins cDNAs

Pmatch Exonerate

Genewise Est2Genome

ESTs

Genscan exons

Add UTRs

EST genes

Other evidence

Merge

Using ESTs

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

ExonerateGolden path contigs

cDNA hits

•Exonerate positions cDNA sequences to assembly contigs

• Store hits as Ensembl FeaturePairs in database

Exonerate

Blast and Est2GenomeVirtual contig

cDNA hits

FilterBlast & MiniseqEst_genome

EST2Genome

Merge ESTs according to consecutive exon overlap and set splice ends

Genomewise

Alternative transcripts with translation and UTRs

ESTs

Reconstructing Alternative Splicing

Human ESTs

EST transcripts

Display limited to 7 at any one point – full data accessible in the databases

Display of EST Evidences

• 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

Ab initio Genscan predictions

Genscan prediction

Evidence supporting Genscan

exons

• 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

Manual Curation: VErtebrate Genome Annotation

Sanger / Vega manual curation

Manual Curation: VEGA

• 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

Other models as ‘DAS sources’

Turn on DAS sources

FASTAView display

Other Gene-Models

• 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

• 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

• 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

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

Stability…

www.ensembl.org/Docs/wiki/html/EnsemblDocs/Answer006.html

Evidence used to build the transcript

links to ExonVie

w

Mapping to external

databases

Links to putative orthologues

Transcript name

Gene name &

descriptionAlternative transcripts

Geneview and Exonview

Compressed tracks

Expanded tracks

Evidence Tracks in ContigView

•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

ENSEMBL

-Finding the right DATA: ENSMART and BLAST

-The central View of ENSEMBL: ContigView

-Genome Comparison: Synteny View

-ENSEMBL incorporate all the evidences intoits gene models

Genebuild overview

Pmatch

Other Proteins

Genewise genes with UTRs

HumanProteins

Genewise

Genewisegenes

GenebuilderSupportedgenscans(optional)

Preliminarygene set

cDNA genes

ClusterMerge

GeneCombiner

Core Ensemblgenes

PseudogenesFinal set

+ pseudogenesEnsembl

EST genes

Est2Genome

AlignedcDNAs

Exonerate

Human cDNAs

Aligned ESTs

Human ESTs

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

Sn Sp

chr13 0.90 0.74

chr14 0.92 0.77

chr6 0.94 0.72

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 only All exons

Sn Sp Sn Sp

chr13 0.83 0.90 0.73 0.78

chr14 0.78 0.88 0.69 0.77

chr6 0.85 0.89 0.73 0.76

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