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Finding genes in human
using the mouse
Finding genes in mouse
using the human
Lior Pachter
Department of Mathematics
U.C. Berkeley
The Gene Finding Problem
Exon 1
Intron 1
Exon 2
Exon 3
Intron 2
Intron 3
Exon 4
5’
Promoter
TATA
DNA
3’
Splice site
GGTGAG
Translation
Initiation
ATG
Splice site
CAG
Pyrimidine
tract
Branchpoint
CTGAC
polyA signal
Stop codon
TAG/TGA/TAA
Approaches to Gene
Recognition
• Naïve (mid80s - mid90s)
ORFfinder, BLAST..
• Statistical de novo
Genie (96), Genscan (97),
FGENESH..
• Systems
Ensembl..
“Ask not what mathematics can do for biology,
ask what biology can do for mathematics” - Stanislaw Ulam
Difficulty of naïve approaches
n = number of acceptor splice sites
m = number of donor splice sites
Number of gene structures = Fn+m+1 (Fibonacci #)
1,1,2,3,5,8,13,21,34…
statistical gene finding
TAATATG TCC AC GG G TATTGA G CA TTG TAC AC GG G G TATTGA G C ATG TAA TGAA
TAAT ATG TCC AC GG G TATTGAG CATTG TAC AC GG G G TATTGAG C ATG TAA TGAA
Using GHMMs for ab-initio gene
finding
In practice, have observed sequence
TAATATG TCC AC GG G TATTGA G CA TTG TAC AC GG G G TATTGA G C ATG TAA TGAA
Predict genes by estimating hidden state sequence
TAAT ATG TCC AC GG G TATTGAG CATTG TAC AC GG G G TATTGAG C ATG TAA TGAA
Usual solution: single most likely sequence
of hidden states (Viterbi).
Results
• High sensitivity / low specificity
• Exon / Intron length distributions
• Identification of GC isochore - gene richness dep.
• Splice site models
Comparative Gene Finding
http://www-gsd.lbl.gov/vista/
Comparison of 1196 orthologous mRNAs
(Makalowski et al., 1996)
• Sequence identity:
–
–
–
–
exons: 84.6%
protein: 85.4%
5’ UTRs: 67%
3’ UTRs: 69%
• 27 proteins were 100% identical.
Comparison of 117 complete genes
Batzoglou/Pachter et al. 1999
• 95% of genes equal number of coding exons
Exceptions:
Spermidine Synthase
Lymphotoxin Beta
• 73% of coding exons have equal length
• 95% of coding exons have length equal mod 3
• Intron conservation 35%
• Intron length ratio longer/shorter: 1.5
SLAM- alignment & gene
finding
• Input:
– Pair of syntenic sequences (FASTA).
• Output:
– CDS and CNS predictions in both
sequences.
– Protein predictions.
– Protein and CNS alignment.
http://bio.math.berkeley.edu/slam/
SLAM components
• Splice site detector
– VLMM
• Intron and intergenic regions
– 2nd order Markov chain
– independent geometric lengths
• Coding sequence
– PHMM on protein level
– generalized length distribution
• Conserved non-coding sequence
– PHMM on DNA level
Input:
Output:
What have we learned from
comparative gene finding?
• conservation is a stronger splice site indicator than
consensus
• intron lengths have diverged
• gene structure conservation is more powerful than
sequence conservation for prediction
• consensus for GC splice sites
SLAM whole genome run
•
•
•
•
•
Align the genomes
Construct a synteny map
Chop up into SLAMable pieces
Run SLAM
Collate results
Alignment project:
http://zilla.lbl.gov/
Linux cluster with
15 1.2GHz PC,
750Mb of RAM
Three days to align
the entire mouse
genome against the
human genome
Finding regulatory regions
Godzilla
-Experimentally defined enhancer (beta- enolase)
Gene name
Enolase
http://lemur.lbl.gov/vistatrack/
Experimental gene
verification with RT-PCR
predicted intron
primer
Intron > 1000bp
Aligning human/mouse
Exons > 60bp
SLAM CNS data
Single exon data
Acknowledgments
Marina Alexandersson – Gothenburg, Sweden (SLAM)
Nick Bray – LBNL/UCB math (Avid alignment program)
Simon Cawley - Affymetrix (SLAM)
Olivier Couronne – LBNL (Godzilla)
Colin Dewey - Berkerley (SLAM)
Alex Poliakov - LBNL (Godzilla, VISTA)
Chuck Sugnet - UCSC (SLAM)
Inna Dubchak - LBNL
Eddy Rubin - LBNL