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Gene Finding

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Gene Finding

- Prokaryotes
- Genome under 10Mb
- >85% of sequence codes for proteins

- Eukaryotes
- Large Genomes (up to 10Gb)
- 1-3% coding for vertebrates

- Humans
- 95% of genes have introns
- 10% of genes have more than 20 introns
- Some have more than 60
- Largest Gene (Duchenne muscular dystrophy locus) spans >2Mb (more than a prokaryote)
- Average exon = 150b
- Introns can interrupt Open Reading Frame at any position, even within a codon
- ORF finding is not sufficient for Eukaryotic genomes

- Without introns, look for long open reading frame (start codon ATG, … , stop codon TAA, TAG, TGA)
- Short genes are missed (<300 nucleotides)
- Shadow genes (overlapping open reading frames on opposite DNA strands) are hard to detect
- Some genes start with UUG, AUA, UUA and CUG for start codon
- Some genes use TGA to create selenocysteine and it is not a stop codon

- Maps are used as scaffolding during sequencing
- Recombination is used to predict the distance genes are from each other (the further apart two loci are on the chromosome, the more likely they are to be separated by recombination during meiosis)
- Pedigree analysis

- Look for strongly conserved regions
- RNA blots - map expressed RNA to DNA
- Identification of CPG islands
- Short stretches of CG rich DNA are associated with the promoters of vertebrate genes

- Exon Trapping - put questionable clone between two exons that are expressed. If there is a gene, it will be spliced into the mature transcript

- Signals - TATA box and other sequences
- TATA box is found 30bp upstream from about 70% of the genes

- Content - Coding DNA and non-coding DNA differ in terms of Hexamer frequency (frequency with which specific 6 nucleotide strings are used)
- Some organisms prefer different codons for the same amino acid

- Homology - blast for sequence in other organisms

- http://genome.ucsc.edu/
- Tables
- Genome browser

- Ribosomal rRNA, transfer tRNA can be recognized by stochastic context-free grammars
- Detection is still an open problem

- Provide a probabilistic view of a process that we don’t fully understand
- The model can be trained with data we don’t understand to learn patterns
- You get to implement one for the first lab!!

- Markov Model Example.
- -x= States of the Markov model
- - a = Transition probabilities
- - b = Output probabilities
- - y = Observable outputs
- How does this differ from a Finite State machine?
- Why is it a Markov process?

- Distant friend that you talk to daily about his activities (walk, shop, clean)
- You believe that the weather is a discrete Markov chain (no memory) with two states (rainy, sunny), but you cant observe them directly. You know the average weather patterns

states = ('Rainy', 'Sunny')

observations = ('walk', 'shop', 'clean')

start_probability = {'Rainy': 0.6, 'Sunny': 0.4}

transition_probability = {

'Rainy' : {'Rainy': 0.7, 'Sunny': 0.3},

'Sunny' : {'Rainy': 0.4, 'Sunny': 0.6},

}

emission_probability = {

'Rainy' : {'walk': 0.1, 'shop': 0.4, 'clean': 0.5},

'Sunny' : {'walk': 0.6, 'shop': 0.3, 'clean': 0.1},

}

- Given (walk, shop, clean)
- What is the probability of this sequence of observations? (is he really still at home, or did he skip the country)
- What was the most likely sequence of rainy/sunny days?

Sunny, Rainy, Rainy = (.4*.6)(.4*.4)(.7*.5)

- Methylation in human genome
- “CG” -> “TG” happens in most places except “start regions” of genes and within genes
- CpG islands = 100-1,000 bases before a gene starts

- Question
- Given a long sequence, how would we find the CpG islands in it?

How can we identify a CpG island in a long sequence?

Idea 1: Test each window of a fixed number of nucleitides

Idea2: Classify the whole sequence

Class label S1: OOOO………….……O

Class label S2: OOOO…………. OCC

…

Class label Si: OOOO…OCC..CO…O

…

Class label SN: CCCC……………….CC

S*=argmaxS P(S|X)

= argmaxS P(S,X)

S*=OOOO…OCC..CO…O

CpG

X=ATTGATGCAAAAGGGGGATCGGGCGATATAAAATTTG

Other

CpG Island

Other

HMM is just one way of modeling p(X,S)…

B

I

0.7

Parameters

Initial state prob:

p(B)= 0.5; p(I)=0.5

State transition prob:

p(BB)=0.7 p(BI)=0.3

p(IB)=0.5 p(II)=0.5

Output prob:

P(a|B) = 0.25,

…

p(c|B)=0.10

…

P(c|I) = 0.25 …

0.5

0.5

P(B)=0.5

P(I)=0.5

0.3

P(x|B)

P(x|I)

0.5

0.5

P(x|HCpG)=p(x|I)

P(a|I)=0.25

P(t|I)=0.25

P(c|I)=0.25

P(g|I)=0.25

P(x|HOther)=p(x|B)

P(a|B)=0.25

P(t|B)=0.40

P(c|B)=0.10

P(g|B)=0.25

Initial state probability:

N states

State transition probability:

M symbols

Output probability:

B

I

P(x|B)

P(x|I)

0.7

0.5

P(a|B)=0.25

P(t|B)=0.40

P(c|B)=0.10

P(g|B)=0.25

P(a|I)=0.25

P(t|I)=0.25

P(c|I)=0.25

P(g|I)=0.25

0.3

model

0.5

P(B)=0.5

P(I)=0.5

a c g t t …

Sequence

B

I

I

I

B

B

I

B

states

I

I

I

B

B

I

I

B

… …

Given a model, follow a path to generate the observations.

B

I

P(x|B)

P(x|I)

0.7

0.5

P(a|B)=0.25

P(t|B)=0.40

P(c|B)=0.10

P(g|B)=0.25

P(a|I)=0.25

P(t|I)=0.25

P(c|I)=0.25

P(g|I)=0.25

0.3

model

0.5

P(B)=0.5

P(I)=0.5

a c g t t …

Sequence

0.3

0.5

0.5

0.5

B

I

I

I

B

0.5

0.25

0.25

0.25

0.25

0.4

t

a

c

g

t

P(“BIIIB”, “acgtt”)=p(B)p(a|B) p(I|B)p(c|I) p(I|I)p(g|I) p(I|I)p(t|I) p(B|I)p(t|B)

Time/Index: t1 t2t3t4 …

Data: o1o2o3o4 …

Sequential data

Random

variables/

process

Observation variable: O1 O2 O3 O4 …

Hidden state variable: S1 S2 S3 S4 …

State transition prob:

Probability of observations with known state transitions:

Output prob.

Joint probability (complete likelihood):

Init state distr.

Probability of observations (incomplete likelihood):

State trans. prob.