CSE182-L12

1
CSE182-L12

• Gene Finding

2
Silly Quiz

• Who are these people, and what is the occasion?

3
Gene Features
4
DNA Signals

• Coding versus non-coding
• Splice Signals
• 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 gt 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 ltTerminating conditionsgt

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 Xi1 if an island ends at position i, Xi0
  otherwise.
• Number of islands ?i Xi
• Expected islands E(?i Xi) ?i E(Xi)

33
Prob. of an island ending at i
i
L
T

• E(Xi) Prob (Island ends at pos. i)
• Prob(clone began at position i-L1
• AND no clone began in the next L-T positions)

34
LW statistics

• PrIsland contains exactly j clones?
• Consider an island that has already begun. With
  probability e-c?, it will never be continued.
  Therefore
• PrIsland contains exactly j clones

• Expected j-clone islands

35
Expected of clones in an island
Why?
36
Expected length of an island