an introduction to ensembl cédric notredame. the top 5 surprises in the human genome map 1.the blue...
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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
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