1. Gene Finding (DNA signals) Genome Sequencing and assembly
CSE182-L9
Gene Finding (DNA signals)
Genome Sequencing and assembly

2. An HMM for Gene structure

3. Gene Finding via HMMs
Gene finding can be interpreted as a d.p. approach that threads genomic sequence through the states of a ‘gene’ HMM.
Einit, Efin, Emid,
I, IG (intergenic) IG I
Efin
Emid
Note: all links are not
shown here
Einit i

4. Generalized HMMs, and other refinements
A probabilistic model for each of the states (ex: Exon, Splice site) needs to be described
In standard HMMs, there is an exponential distribution on the duration of time spent in a state.
This is violated by many states of the gene structure HMM. Solution is to model these using generalized HMMs.

5. Length distributions of Introns & Exons

6. Generalized HMM for gene finding
Each state also emits a ‘duration’ for which it will cycle in the same state. The time is generated according to a random process that depends on the state.

7. Forward algorithm for gene findingqk j i
Duration Prob.: Probability that you stayed
in state qk for j-i+1 steps
Emission Prob.: Probability that you emitted Xi..Xj in
state qk (given by the 5th order markov model)
Forward Prob: Probability that you emitted i symbols and ended
up in state qk

8. De novo Gene prediction: Summary
Various signals distinguish coding regions from non-coding
HMMs are a reasonable model for Gene structures, and provide a uniform method for combining various signals.
Further improvement may come from improved signal detection

9. DNA Signals 5’ UTR intron exon 3’ UTR Acceptor Donor splice site
Coding versus non-coding
Splice Signals
Translation start
ATG
5’ UTR
intron
exon
3’ UTR
Acceptor
Donor splice site
Transcription start
Translation start

10. DNA signal example:
The donor site marks the junction where an exon ends, and an intron begins.
For gene finding, we are interested in computing a probability
D[i] = Prob[Donor site at position i]
Approach: Collect a large number of donor sites, align, and look for a signal.

11. PWMs 321123456 AAGGTGAGT CCGGTAAGT GAGGTGAGG TAGGTAAGG
Fixed length for the splice signal.
Each position is generated independently according to a distribution
Figure shows data from > 1200 donor sites

12. Improvements to signal detection
Pr[GGTA] is a donor site?
0.5*0.5
Pr[CGTA] is a donor site?
Is something wrong with this explanation?
GGTA
CGTG

13. MDD PWMs do not capture correlations between positions
Many position pairs in the Donor signal are correlated

14. Maximal Dependence Decomposition
Choose the position i which has the highest correlation score.
Split sequences into two: those which have the consensus at position i, and the remaining.
Recurse until <Terminating conditions>
Stop if #sequences is ‘small enough’

15. MDD for Donor sites

16. Gene prediction: Summary
Various signals distinguish coding regions from non-coding
HMMs are a reasonable model for Gene structures, and provide a uniform method for combining various signals.
Further improvement may come from improved signal detection

17. How many genes do we have?
Nature
Science

18. Alternative splicing

19. Comparative methods
Gene prediction is harder with alternative splicing.
One approach might be to use comparative methods to detect genes
Given a similar mRNA/protein (from another species, perhaps?), can you find the best parse of a genomic sequence that matches that target sequence
Yes, with a variant on alignment algorithms that penalize separately for introns, versus other gaps.
There is a genome sequencing project for a different Hirudo species. You could compare the Hirudo ESTs against the genome to do gene finding.

20. Comparative gene finding tools
Procrustes/Sim4: mRNA vs. genomic
Genewise: proteins versus genomic
CEM: genomic versus genomic
Twinscan: Combines comparative and de novo approach.
Mass Spec related?
Later in the class we will consider mass spectrometry data.
Can we use this data to identify genes in eukaryotic genomes? (Research project)

21. Databases
RefSeq and other databases maintain sequences of full-length transcripts/genes.
We can query using sequence.

22. Course Sequence Comparison (BLAST & other tools) Protein Motifs:
Gene finding
Sequence Comparison (BLAST & other tools)
Protein Motifs:
Profiles/Regular Expression/HMMs
Discovering protein coding genes
Gene finding HMMs
DNA signals (splice signals)
How is the genomic sequence itself obtained?
ESTs
Protein sequence analysis

23. Silly Quiz
Who are these people, and what is the occasion?

24. Genome Sequencing and Assembly

25. DNA Sequencing DNA is double-stranded
The strands are separated, and a polymerase is used to copy the second strand.
Special bases terminate this process early.

26. Sequencing A break at T is shown here.
Measuring the lengths using electrophoresis allows us to get the position of each T
The same can be done with every nucleotide. Fluorescent labeling can help separate different nucleotides

27. Automated detectors ‘read’ the terminating bases.
The signal decays after 1000 bases.

28. Sequencing Genomes: Clone by Clone
Clones are constructed to span the entire length of the genome.
These clones are ordered and oriented correctly (Mapping)
Each clone is sequenced individually

29. Shotgun Sequencing Shotgun sequencing of clones was considered viable
However, researchers in 1999 proposed shotgunning the entire genome.

30. Library
Create vectors of the sequence and introduce them into bacteria. As bacteria multiply you will have many copies of the same clone.

31. Sequencing

32. Questions
Algorithmic: How do you put the genome back together from the pieces? Will be discussed in the next lecture.
Statistical?
EX: Let G be the length of the genome, and L be the length of a fragment. How many fragments do you need to sequence?
The answer to the statistical questions had already been given in the context of mapping, by Lander and Waterman.

33. Lander Waterman Statistics
Island L G

34. LW statistics: questions
As the coverage c increases, more and more areas of the genome are likely to be covered. Ideally, you want to see 1 island.
Q1: What is the expected number of islands?
Ans: N exp(-c)
The number increases at first, and gradually decreases.

35. Analysis: Expected Number Islands
Computing Expected # islands.
Let Xi=1 if an island ends at position i, Xi=0 otherwise.
Number of islands = ∑i Xi
Expected # islands = E(∑i Xi) = ∑i E(Xi)

36. Prob. of an island ending at i
E(Xi) = Prob (Island ends at pos. i)
=Prob(clone began at position i-L+1
AND no clone began in the next L-T positions)

37. LW statistics Pr[Island contains exactly j clones]?
Consider an island that has already begun. With probability e-c, it will never be continued. Therefore
Pr[Island contains exactly j clones]=
Expected # j-clone islands

38. Expected # of clones in an island
Why?

39. Expected length of an island