Gene predictions for eukaryotes

1
Gene predictions for eukaryotes

• attgccagtacgtagctagctacacgtatgctattacggatctgtagc
  ttagcgtatctgtatgctgttagctgtacgtacgtatttttctagagctt
  cgtagtctatggctagtcgtagtcgtagtcgttagcatctgtatgctgtt
  agctgtacgtacgtatttttctagagcttcgtagtctatggctagtcgta
  gtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtatttttc
  taggggagcttcgtagtctatggctagtcgtagtcgtagtcgttagcatc
  tgtatgctgttagctgtacgtacgtatttttctaggggagcttcgtagtc
  tatggctagtcgtagtcgtagtcgttagcatctgtatgctgttagctgta
  cgtacgtatttttctaggggagcttcgtagtctatggctagtcgtagtcg
  tagtcgttagcttagtcgtgtagtcttgatctacgtacgtatttttctag
  agcttcgtagtctatggctagtcgtagtcgtagtcgttagcatctgtatg
  ctgttagctgtacgtacgtatttttctagagcttcgtagtctatggctag
  tcgtagtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtat
  ttttctaggggagcttcgtagtctatggctagtcgtagtcgtagtcgtta
  gcatctgtatgctgttagctgtacgtacgtatttttctaggggagcttcg
  tagtctatggctagtcgtagtcgtagtcgttagcatctgtatgtacgtac
  gtatttttctagagcttcgtagtctatggctagtcgtagtcgtagtcgtt
  agcatctgtatgctgttagctgtacgtacgtatttttctagagcttcgta
  gtctatggctagtcgtagtcgtagtcgttagcatctgtatgctgttagct
  gtacgtacgtatttttctaggggagcttcgtagtctatggctagtcgtag
  tcgtagtcgttagcatctgtatgctgttagctgtacgtacgtatttttct
  aggggagcttcgtagtctatggctagtcgtagtcgtagtcgttagcatct
  gtatggtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtat
  ttttctaggggagcttcgtagtctatggctag

2
Gene predictions for eukaryotes

• attgccagtacgtagctagctacacgtatgctattacggatctgtagc
  ttagcgtatctgtatgctgttagctgtacgtacgtatttttctagagctt
  cgtagtctatggctagtcgtagtcgtagtcgttagcatctgtatgctgtt
  agctgtacgtacgtatttttctagagcttcgtagtctatggctagtcgta
  gtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtatttttc
  taggggagcttcgtagtctatggctagtcgtagtcgtagtcgttagcatc
  tgtatgctgttagctgtacgtacgtatttttctaggggagcttcgtagtc
  tatggctagtcgtagtcgtagtcgttagcatctgtatgctgttagctgta
  cgtacgtatttttctaggggagcttcgtagtctatggctagtcgtagtcg
  tagtcgttagcttagtcgtgtagtcttgatctacgtacgtatttttctag
  agcttcgtagtctatggctagtcgtagtcgtagtcgttagcatctgtatg
  ctgttagctgtacgtacgtatttttctagagcttcgtagtctatggctag
  tcgtagtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtat
  ttttctaggggagcttcgtagtctatggctagtcgtagtcgtagtcgtta
  gcatctgtatgctgttagctgtacgtacgtatttttctaggggagcttcg
  tagtctatggctagtcgtagtcgtagtcgttagcatctgtatgtacgtac
  gtatttttctagagcttcgtagtctatggctagtcgtagtcgtagtcgtt
  agcatctgtatgctgttagctgtacgtacgtatttttctagagcttcgta
  gtctatggctagtcgtagtcgtagtcgttagcatctgtatgctgttagct
  gtacgtacgtatttttctaggggagcttcgtagtctatggctagtcgtag
  tcgtagtcgttagcatctgtatgctgttagctgtacgtacgtatttttct
  aggggagcttcgtagtctatggctagtcgtagtcgtagtcgttagcatct
  gtatggtcgtagtcgttagcatctgtatgctgttagctgtacgtacgtat
  ttttctaggggagcttcgtagtctatggctag

3
Gene predictions for eukaryotes

4
Gene predictions for eukaryotes

• Three different approaches to computational
  gene-finding
• 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

5
Hidden-Markov-Models (HMM) for gene prediction

• 3 5 6 6 6 4 6 5 1 6 5 1 2 s
• B F F U U U U U F F F F F F E f
• For sequence s and parse f
• P(f) probability of f
• P(f,s) joint probability of f and s
• P(f) P(sf)
• P(fs) a-posteriori probability of f

6
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 f with maximum a-posteriori
  probability P(fs)
• Equivalent find path that maximizes joint
  probability P(f,s)
• Optimal path calculated by dynamic programming
  (Viterbi algorithm)

7
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

8
Hidden-Markov-Models (HMM) for gene prediction
Application to gene prediction A T A A T G C C
T A G T C s (DNA) Z Z Z E E E E E E I I I I f
(parse) Introns, exons etc modeled as states in
GHMM (generalized HMM) Given sequence s, find
parse that maximizes P(fs) (S. Karlin and C.
Burge, 1997)

9

10
AUGUSTUS

• Basic model for GHMM-based intrinsic gene finding
  comparable to GenScan (M. Stanke)

11
AUGUSTUS

12
AUGUSTUS

13
AUGUSTUS

• Features of AUGUSTUS
• Intron length model
• Initial pattern for exons
• Similarity-based weighting for splice sites
• Interpolated HMM
• Internal 3 content model

14
Hidden-Markov-Models (HMM) for gene prediction
A T A A T G C C T A G T C s (DNA) Z Z Z E E E
E I I I I f (parse) Explicit intron length
model computationally expensive.

15
AUGUSTUS

Intron length model

Intron (expl.)
Exon
Exon
Intron (geo.)
Intron (fixed)

• Explicit length distribution for short introns
• Geometric tail for long introns

16
AUGUSTUS

17
AUGUSTUS

• Extension of AUGUSTUS using include extrinsic
  information
• Protein sequences
• EST sequences
• Syntenic genomic sequences
• User-defined constraints

18
Gene prediction by phylogenetic footprinting

• Comparison of genomic
  sequences
• (human and mouse)

19
Gene prediction by phylogenetic footprinting

20
AUGUSTUS

• Extended GHMM using extrinsic information
• Additional input data collection h of hints
  about possible gene structure f for sequence s
• Consider s, f and h result of random process.
  Define probability P(s,h,f)
• Find parse f that maximizes P(fs,h) for given s
  and h.

21
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

22
AUGUSTUS

EST
G1
Alignment to EST hint to (partial) exon
23
AUGUSTUS

Protein
EST
G1
EST alignment supported by protein hint to exon
(part), start codon
24
AUGUSTUS

ESTs, Protein
G1
Alignment to ESTs, Proteins hints to introns,
exons
25
AUGUSTUS

G2
G1
Alignment of genomic sequences hint to (partial)
exon
26
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.

27
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

28
AUGUSTUS
Standard program version, without hints A T A A
T G C C T A G T C s (sequence) Z Z Z E E E E E
E I I I I f (parse) Find parse that maximizes
P(fs)

29
AUGUSTUS
AUGUSTUS using hints A T A A T G C C T A G T C
s (sequence) X h (type
1) h (type 2)
X h (type 3) . . . . Z Z Z E
E E E E E I I I I f (parse) Find parse that
maximizes P(fs,h)

30
AUGUSTUS

As in standard HMM theory maximize joint
probability P(f,s,h) How to calculate P(f,s,h)
?

31
AUGUSTUS

Simplifying assumption Hints of different types
t and at different positions i independent of
each other (for redundant hints ignore weaker
types).
32
AUGUSTUS

Simplifying assumption Hints of different types
t and at different positions i independent of
each other (for redundant hints ignore weaker
types).
33
AUGUSTUS

Simplifying assumption Hints of different types
t and at different positions i independent of
each other (for redundant hints ignore weaker
types).
34
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)

35
AUGUSTUS

36
AUGUSTUS

• Using hints from DIALIGN alignments
• Obtain large human/mouse sequence pairs (up 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

37
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
• gt 222 8 grades

38
AUGUSTUS

EGASP competition to evaulate and compare
gene-prediction methods (Sanger Center,
2005) AUGUSTUS best ab-initio method at EGASP

39
EGASP test results

40
EGASP test results

41
EGASP test results

42
EGASP test results

43
EGASP test results
44
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)