CSE182-L9

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CSE182-L9

• Gene Finding (DNA signals)
• Genome Sequencing and assembly

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An HMM for Gene structure
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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
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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.

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Length distributions of Introns Exons
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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.

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Forward algorithm for gene finding
qk
j
i
Duration Prob. Probability that you stayed in
state qk for j-i1 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
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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

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DNA Signals

• Coding versus non-coding
• Splice Signals
• Translation start

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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
• Di ProbDonor site at position i
• Approach Collect a large number of donor sites,
  align, and look for a signal.

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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

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Improvements to signal detection

• PrGGTA is a donor site?
• 0.50.5
• PrCGTA is a donor site?
• 0.50.5
• Is something wrong with this explanation?

GGTA GGTA GGTA GGTA CGTG CGTG CGTG CGTG
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MDD

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

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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 ltTerminating conditionsgt
• Stop if sequences is small enough

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MDD for Donor sites
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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

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How many genes do we have?
Nature
Science
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Alternative splicing
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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.

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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)

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Databases

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

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Course
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
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Silly Quiz

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

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Genome Sequencing and Assembly
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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.

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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

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• Automated detectors read the terminating bases.
• The signal decays after 1000 bases.

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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

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Shotgun Sequencing

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

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Library

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

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Sequencing
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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.

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Lander Waterman Statistics
Island
L
G
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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.

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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)

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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)

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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

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