1. CSE182-L12
Gene Finding

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

3. Gene Features 5’ UTR intron exon 3’ UTR Acceptor Donor splice site
ATG
5’ UTR
intron
exon
3’ UTR
Acceptor
Donor splice site
Transcription start
Translation start

4. 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

5. 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

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

7. MDD method
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>

8. MDD for Donor sites

9. 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

10. How many genes do we have?
Nature
Science

11. Alternative splicing

12. 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.

13. Comparative gene finding tools
Genscan/Genie
Procrustes/Sim4: mRNA vs. genomic
Genewise: proteins versus genomic
CEM: genomic versus genomic
Twinscan: Combines comparative and de novo approach.

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

15. 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

16. How many genes do we have?
Nature
Science

17. Alternative splicing

18. 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.

19. Comparative gene finding tools
Procrustes/Sim4: mRNA vs. genomic
Genewise: proteins versus genomic
CEM: genomic versus genomic
Twinscan: Combines comparative and de novo approach.

20. Course Sequence Comparison (BLAST & other tools) Protein Motifs:
Profiles/Regular Expression/HMMs
Protein Sequence Identification via Mass Spec.
Discovering protein coding genes
Gene finding HMMs
DNA signals (splice signals)

21. Genome Assembly

22. 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.

23. 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. Color coding can help separate different nucleotides

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

25. 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

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

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

28. Sequencing

29. Questions
Algorithmic: How do you put the genome back together from the pieces? Will be discussed in the next lecture.
Statistical? How many pieces do you need to sequence, etc.?
The answer to the statistical questions had already been given in the context of mapping, by Lander and Waterman.

30. Lander Waterman Statistics
Island L G

31. 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.

32. 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)

33. 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)

34. 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

35. Expected # of clones in an island
Why?

36. Expected length of an island