Transcript Information Encoding in Biological Molecules: DNA and

The Ensembl Database
Erin Pleasance
Steven Jones
Canada’s Michael Smith Genome Sciences Centre, Vancouver
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www.ensembl.org
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What is Ensembl?
• Public annotation of mammalian and other
genomes
• Open source software
• Relational database system
• The future of genomic bioinformatics?
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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”
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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
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Goal: An Accessible, Annotated
Genome
Diagram of
contigview as
“what we want
in the end”
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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).
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Ensembl Genome Annotation
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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
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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….
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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
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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
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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
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“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
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“Best in genome” predictions
• Alignments shown in ContigView
UTRs predicted
Known gene
(p53)
ContigView of best in genome gene
with associated evidence
Proteins aligned
Unigene clusters aligned
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cDNAs aligned
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Homology predictions
• Align homologous proteins using BLAST,
genewise
– Paralogs (from same organism)
– Orthologs (from closely related organisms)
• Assemble novel genes
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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
Unigene
ContigView of homology gene with
clusters
associatedaligned
evidence
Proteins aligned
GenScan predictions
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Novel gene
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Pseudogenes
• Many pseudogenes also predicted
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Ensembl Gene Build System
• Resulting “Ensembl genes” are highly accurate with
low false positive rates
Snapshot or
stats on genes
• Ensembl human gene identifiers are 95% stable
between builds
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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
EST genes
Unigene clusters aligned
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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
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Ensembl EST genes
• Evidence for genes shown (ExonView)
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Manual gene annotation: Otter
• Manual annotation
done with applications
eg. Apollo
• Otter database/server
allows manual
annotations to be
integrated with
automated annotations
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Manually curated genes: VEGA
• Chromosomes
6,7,13,14, 20
and 22
contain
manually
curated genes
from VEGA
database
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Gene information in Ensembl:
GeneView
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Transcript information in Ensembl:
TransView
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Protein information in Ensembl:
ProteinView
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Comparative genomics in Ensembl
Gene orthologue pairs:
• Human <-> Mouse <-> Rat
<-> Fugu <-> Zebrafish
• C. elegans <-> C. briggsae
• Fly <-> Mosquito
DNA homology:
• Human <-> Mouse <-> Rat
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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)
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Comparative genomics in
Ensembl: DNA homology
• DNA homology shown in ContigView
Mouse and rat homology
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Comparative genomics in
Ensembl: Synteny
• Large-scale homology
shown in SyntenyView
– Synteny = homologous
sequence blocks, in
same order and
orientation
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Other features in Ensembl
• Menus
provide
other feature
options
• Features eg.
SNPs and
markers
have special
views
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Other data sources in Ensembl
• Ensembl incorporates gene and feature info
from many other datasources
OMIM
SwissProt
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Other data sources in Ensembl:
Link out
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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
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DAS: Selecting data
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GeneDAS
• GeneDAS allows exchange of annotations on
gene level
– eg. access to SwissProt annotations from GeneView
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DAS: Add your own annotations
• Anyone can add data and upload it to DAS
server for others to view
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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
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Hits relative to
genome
Show alignment
[A], sequence [S],
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or ContigView
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BLAST results
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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
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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
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• Choose
focus: gene
set or
SNPS
• Choose
organism
(any
species in
Ensembl)
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Filter genes
based on info
about:
• Region
• Genes
• Diseases
• Expression
patterns
• Multi-species
comparisons
• Protein
domains and
families
• SNPs
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• Choose output
type:
–Features
(genes with
associated info)
–SNPs
–Structures
(of genes –
eg. exons)
–Sequences
• Choose what
information to
output
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Multiple Programming Interfaces now exist for Ensembl
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Another example of how to utilize
the Ensembl database – Sockeye
www.bcgsc.ca/bioinfo/software
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Apollo – java viewer
www.ensembl.org/apollo
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Ensembl updates
• Monthly
• Include:
– Changes in genome builds (with new annotations)
– Changes in code or database schema
– Additional views and tools on website
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Pre-Ensembl
• Full annotation can take weeks
• Pre-Ensembl site provides in-progress annotation
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Placement of known proteins
Ab initio gene predictions
Repeat masking
BLAST and SSAHA searching
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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.
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Further Information
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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.
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