Download AUGUSTUS+

Gene predictions for eukaryotes
attgccagtacgtagctagctacacgtatgctattacggatctgtagcttagcgtatct
gtatgctgttagctgtacgtacgtatttttctagagcttcgtagtctatggctagtcgt
agtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtatttttctagagctt
cgtagtctatggctagtcgtagtcgtagtcgttagcatctgtatgctgttagctgtacg
tacgtatttttctaggggagcttcgtagtctatggctagtcgtagtcgtagtcgttagc
atctgtatgctgttagctgtacgtacgtatttttctaggggagcttcgtagtctatggc
tagtcgtagtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtatttttct
aggggagcttcgtagtctatggctagtcgtagtcgtagtcgttagcttagtcgtgtagt
cttgatctacgtacgtatttttctagagcttcgtagtctatggctagtcgtagtcgtag
tcgttagcatctgtatgctgttagctgtacgtacgtatttttctagagcttcgtagtct
atggctagtcgtagtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtatt
tttctaggggagcttcgtagtctatggctagtcgtagtcgtagtcgttagcatctgtat
gctgttagctgtacgtacgtatttttctaggggagcttcgtagtctatggctagtcgta
gtcgtagtcgttagcatctgtatgtacgtacgtatttttctagagcttcgtagtctatg
gctagtcgtagtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtattttt
ctagagcttcgtagtctatggctagtcgtagtcgtagtcgttagcatctgtatgctgtt
agctgtacgtacgtatttttctaggggagcttcgtagtctatggctagtcgtagtcgta
gtcgttagcatctgtatgctgttagctgtacgtacgtatttttctaggggagcttcgta
gtctatggctagtcgtagtcgtagtcgttagcatctgtatggtcgtagtcgttagcatc
tgtatgctgttagctgtacgtacgtatttttctaggggagcttcgtagtctatggctag
Gene predictions for eukaryotes
attgccagtacgtagctagctacacgtatgctattacggatctgtagcttagcgtatct
gtatgctgttagctgtacgtacgtatttttctagagcttcgtagtctatggctagtcgt
agtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtatttttctagagctt
cgtagtctatggctagtcgtagtcgtagtcgttagcatctgtatgctgttagctgtacg
tacgtatttttctaggggagcttcgtagtctatggctagtcgtagtcgtagtcgttagc
atctgtatgctgttagctgtacgtacgtatttttctaggggagcttcgtagtctatggc
tagtcgtagtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtatttttct
aggggagcttcgtagtctatggctagtcgtagtcgtagtcgttagcttagtcgtgtagt
cttgatctacgtacgtatttttctagagcttcgtagtctatggctagtcgtagtcgtag
tcgttagcatctgtatgctgttagctgtacgtacgtatttttctagagcttcgtagtct
atggctagtcgtagtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtatt
tttctaggggagcttcgtagtctatggctagtcgtagtcgtagtcgttagcatctgtat
gctgttagctgtacgtacgtatttttctaggggagcttcgtagtctatggctagtcgta
gtcgtagtcgttagcatctgtatgtacgtacgtatttttctagagcttcgtagtctatg
gctagtcgtagtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtattttt
ctagagcttcgtagtctatggctagtcgtagtcgtagtcgttagcatctgtatgctgtt
agctgtacgtacgtatttttctaggggagcttcgtagtctatggctagtcgtagtcgta
gtcgttagcatctgtatgctgttagctgtacgtacgtatttttctaggggagcttcgta
gtctatggctagtcgtagtcgtagtcgttagcatctgtatggtcgtagtcgttagcatc
tgtatgctgttagctgtacgtacgtatttttctaggggagcttcgtagtctatggctag
Gene predictions for eukaryotes
Gene predictions for eukaryotes
Three different approaches to computational genefinding:

Intrinsic: use statistical information about known
genes (Hidden Markov Models)

Extrinsic: compare genomic sequence with known
proteins / genes

Cross-species sequence comparison: search for
similarities among genomes
Hidden-Markov-Models (HMM) for gene prediction
3 5 6 6 6 4 6 5 1 6 5 1 2
B F F U U U U U F F F F F F E
For sequence s and parse φ:
P(φ) probability of φ
P(φ,s) joint probability of φ and s
= P(φ) * P(s|φ)
P(φ|s) a-posteriori probability of φ
s
φ
Hidden-Markov-Models (HMM) for gene prediction
3 5 6 6 6 4 6 5 1 6 5 1 2
B F F U U U U U F F F F F F E
Goal: find path φ with maximum a-posteriori
probability P(φ|s)
Equivalent: find path that maximizes joint
probability P(φ,s)
Optimal path calculated by dynamic
programming (Viterbi algorithm)
Hidden-Markov-Models (HMM) for gene prediction
3 5 6 6 6 4 6 5 1 6 5 1 2
B F F U U U U U F F F F F F E
Program parameters learned from training data
Hidden-Markov-Models (HMM) for gene prediction
Application to gene prediction:
A T A A T G C C T A G T C
Z Z Z E E E E E E I I I I
s (DNA)
φ (parse)
Introns, exons etc modeled as states in GHMM
(„generalized HMM“)
Given sequence s, find parse that maximizes P(φ|s)
(S. Karlin and C. Burge, 1997)
AUGUSTUS
Basic model for GHMM-based intrinsic gene
finding comparable to GenScan (M. Stanke)
AUGUSTUS
AUGUSTUS
AUGUSTUS
Features of AUGUSTUS:
 Intron length model
 Initial pattern for exons
 Similarity-based weighting for splice sites
 Interpolated HMM
 Internal 3’ content model
Hidden-Markov-Models (HMM) for gene prediction
A T A A T G C C T A G T C
Z Z Z E E E E I I I I
s (DNA)
φ (parse)
Explicit intron length model computationally
expensive.
AUGUSTUS
Intron length model:
Intron
(expl.)
Exon
Exon
Intron
(fixed)
Intron
(geo.)
• Explicit length distribution for short introns
• Geometric tail for long introns
AUGUSTUS
AUGUSTUS+
Extension of AUGUSTUS using include extrinsic
information:




Protein sequences
EST sequences
Syntenic genomic sequences
User-defined constraints
Gene prediction by phylogenetic footprinting
Comparison of genomic sequences
(human and mouse)
Gene prediction by phylogenetic footprinting
AUGUSTUS+
 Extended GHMM using extrinsic information
 Additional input data: collection h of `hints’
about possible gene structure φ for sequence s
 Consider s, φ and h result of random process.
Define probability P(s,h,φ)
 Find parse φ that maximizes P(φ|s,h) for given
s and h.
AUGUSTUS+
Hints created using
 Alignments to EST sequences
 Alignments to protein sequences
 Combined EST and protein alignment (EST
alignments supported by protein alignments)
 Alignments of genomic sequences
 User-defined hints
AUGUSTUS+
EST
G1
Alignment to EST: hint to (partial) exon
AUGUSTUS+
Protein
EST
G1
EST alignment supported by protein: hint to exon (part),
start codon
AUGUSTUS+
ESTs, Protein
G1
Alignment to ESTs, Proteins: hints to introns, exons
AUGUSTUS+
G2
G1
Alignment of genomic sequences: hint to (partial) exon
AUGUSTUS+
Consider different types of hints:
type of hints: start, stop, dss, ass, exonpart,
exon, introns
 Hint associated with position i in s (exons etc.
associated with right end position)
 max. one hint of each type allowed per
position in s
 Each hint associated with a grade g that
indicates its source.
AUGUSTUS+
hi,t = information about hint of type t at position i
hi,t = [grade, strand, (length, reading frame)] if
hint available
(hints created by protein alignments contain
information about reading frame)
hi,t = $ if no hint of type t available at i
AUGUSTUS+
Standard program version, without hints
A T A A T G C C T A G T C
Z Z Z E E E E E E I I I I
Find parse that maximizes P(φ|s)
s (sequence)
φ (parse)
AUGUSTUS+
AUGUSTUS+ using hints
A
$
$
$
.
Z
T
$
$
$
.
Z
A
$
$
$
.
Z
A
$
$
$
.
E
T
$
$
X
G
$
$
$
C
$
$
$
C
X
$
$
T
$
$
$
A
$
$
$
G
$
$
$
T
$
$
$
C
$
$
$
E E E E E I I I I
Find parse that maximizes P(φ|s,h)
s (sequence)
h (type 1)
h (type 2)
h (type 3)
φ (parse)
AUGUSTUS+
As in standard HMM theory: maximize joint
probability P(φ,s,h)
How to calculate P(φ,s,h) ?
AUGUSTUS+
Simplifying assumption: Hints of different types t
and at different positions i independent of each
other (for redundant hints: ignore „weaker“
types).
AUGUSTUS+
Simplifying assumption: Hints of different types t
and at different positions i independent of each
other (for redundant hints: ignore „weaker“
types).
P( , s, h)  P( , s )  P(h |  , s )
AUGUSTUS+
Simplifying assumption: Hints of different types t
and at different positions i independent of each
other (for redundant hints: ignore „weaker“
types).
P( , s, h)  P( , s )  P(h |  , s )
 P( , s )   P(hi ,t |  , s )
i ,t
AUGUSTUS+
 Results:
 Gene (sub-)structures supported by hints
receive bonus compared to non-supported
structures
 Gene (sub-)structures not supported by hints
receive malus
(M. Stanke et al. 2006, BMC Bioinformatics)
AUGUSTUS+
AUGUSTUS+
Using hints from DIALIGN alignments:
1. Obtain large human/mouse sequence pairs (up
2.
3.
4.
5.
to 50kb) from UCSC
Run CHAOS to find anchor points
Run DIALIGN using CHAOS anchor points
Create hints h from DIALIGN fragments
Run AUGUSTUS with hints
AUGUSTUS+
Hints from DIALIGN fragments:
Consider fragments with score ≥ 20
 Distinguish high scores (≥ 45) from low scores
 Consider reading frame given by DIALIGN
 Consider strand given by DIALIGN
=> 2*2*2 = 8 grades
AUGUSTUS+
EGASP competition to evaulate and compare
gene-prediction methods (Sanger Center,
2005)
AUGUSTUS best ab-initio method at EGASP
EGASP test results
Nukleotid Level
100
90
80
70
60
50
Sensitivität
40
Spezifität
30
20
10
0
AUGUS
GENSC
geneid
GeneMa
Genezill
EGASP test results
Exon Level
100
90
80
70
60
50
Sensitivität
Spezifität
40
30
20
10
0
AUGUS
GENSC
geneid
GeneMa
Genezill
EGASP test results
Transkript Level
30
27,5
25
22,5
20
17,5
15
Sensitivität
Spezifität
12,5
10
7,5
5
2,5
0
AUGUST
US
GENSCA
N
geneid
GeneMar
k.hmm
Genezilla
EGASP test results
Gen Level
30
27,5
25
22,5
20
17,5
15
Sensitivität
12,5
Spezifität
10
7,5
5
2,5
0
AUGUST
US
GENSCA
N
geneid
GeneMar
k.hmm
Genezilla
EGASP test results
Accuracy
100%
AUGUSTUS
90%
AUGUSTUS+DIALIGN
80%
DOGFISH-C
70%
SGP2
60%
TWINSCAN
TWINSCAN-MARS
50%
N-SCAN
40%
30%
20%
10%
0%
Sn
Sp
Base
Sn
Sp
Exon
Sn
Sp
Transcript
Sn
Sp
Gene
Application of AUGUSTUS in genome projects
 Brugia malayi (TIGR)
 Aedes aegypti (TIGR)
 Schistosoma mansoni (TIGR)
 Tetrahymena thermophilia (TIGR)
 Galdieria Sulphuraria (Michigan State Univ.)
 Coprinus cinereus (Univ. Göttingen)
 Tribolium castaneum (Univ. Göttingen)