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CSE182-L9. Gene Finding (DNA signals) Genome Sequencing and assembly. Basic Math. Given a vector V in an n-dimensional space What is ||V||? What is V/||V||? How can you compute the angle between two vectors?. V. V 1. Transcription and translation.

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CSE182-L9

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Cse182 l9

CSE182-L9

Gene Finding (DNA signals)

Genome Sequencing and assembly


Basic math

Basic Math

  • Given a vector V in an n-dimensional space

    • What is ||V||?

    • What is V/||V||?

  • How can you compute the angle between two vectors?

V

V1


Transcription and translation

Transcription and translation

  • We define a gene as a location on the genome that codes for proteins.

  • The genic information is used to manufacture proteins through transcription, and translation.

  • There is a unique mapping from triplets to amino-acids


Translation

Translation

  • The ribosomal machinery reads mRNA.

  • Each triplet is translated into a unique amino-acid until the STOP codon is encountered.

  • There is also a special signal where translation starts, usually at the ATG (M) codon.


Translation1

Translation

  • The ribosomal machinery reads mRNA.

  • Each triplet is translated into a unique amino-acid until the STOP codon is encountered.

  • There is also a special signal where translation starts, usually at the ATG (M) codon.

  • Given a DNA sequence, how many ways can you translate it?


Eukaryotic gene structure

Eukaryotic gene structure

  • The coding regions of a gene are discontiguous regions (exons), separated by non-coding regions (introns).

  • Transcription initially copies the entire region into RNA

  • The introns are ‘spliced out’ to form the mature mRNA (message)

  • Translation starts from an intitiating ATG somewhere in the message.


Gene features

ATG

5’ UTR

3’ UTR

exon

intron

Translation start

Acceptor

Donor splice site

Transcription start

Gene Features


Gene features1

Gene Features

  • The gene can lie on any strand (relative to the reference genome)

  • The code can be in one of 3 frames.

Frame 1

Frame 2

Frame 3

S R V * W R

V Q Y S G

* S I V D

AGTAGAGTATAGTGGACG

TCATCTCATATCACCTGC

-ve strand


Gene identification

Gene identification

  • Eukaryotic gene definitions:

    • Location that codes for a protein

    • The transcript sequence(s) that encodes the protein

    • The protein sequence(s)

  • Suppose you want to know all of the genes in an organism.

  • This was a major problem in the 70s. PhDs, and careers were spent isolating a single gene sequence.

  • All of that changed with better reagents and the development of high throughput methods like EST sequencing

  • With genome sequencing, the initial problem became computational.


Computational gene finding

ATG

5’ UTR

3’ UTR

exon

intron

Translation start

Acceptor

Donor splice site

Transcription start

Computational Gene Finding

  • Given Genomic DNA, identify all the coordinates of the gene

  • TRIVIA QUIZ! What is the name of the FIRST gene finding program? (google testcode)


Gene finding the 1st generation

Gene Finding: The 1st generation

  • Given genomic DNA, does it contain a gene (or not)?

  • Key idea: The distributions of nucleotides is different in coding (translated exons) and non-coding regions.

  • Therefore, a statistical test can be used to discriminate between coding and non-coding regions.


Coding versus non coding

Coding versus non-coding

  • You are given a collection of exons, and a collection of intergenic sequence.

  • Count the number of occurrences of ATGATG in Introns and Exons.

    • Suppose 1% of the hexamers in Exons are ATGATG

    • Only 0.01% of the hexamers in Intergenic are ATGATG

  • How can you use this idea to find genes?


Generalizing

Generalizing

Frequencies (X10-5)

I

E

X

AAAAAA

AAAAAC

AAAAAG

AAAAAT

10

10

5

5

20

10

  • Compute a frequency count for all hexamers.

  • Exons, Intergenic and the sequence X are all vectors in a multi-dimensional space

  • Use this to decide whether a sequence X is exonic/intergenic.


A geometric approach 2 hexamers

A geometric approach (2 hexamers)

  • Plot the following vectors

    • E= [10, 20]

    • I= [10, 5]

    • V3 = [6, 10]

    • V4 = [9, 15]

  • Is V3 more like E or more like I?

E

20

15

V3

10

I

5

5

10

15


Choosing between intergenic and exonic

E

V3

I

Choosing between Intergenic and Exonic

  • Normalize V’ = V/||V||

  • All vectors have the same length (lie on the unit circle)

  • Next, compute the angle to E, and I.

  • Choose the feature that is ‘closer’ (smaller angle.


Coding versus non coding signals

Coding versus non-coding signals

  • Fickett and Tung (1992) compared various measures

    • Measures that preserve the triplet frame are the most successful.

  • Genscan uses a 5th order Markov Model


5th order markov chain

5th order markov chain

Exon

Intron

A

AAAAAA 20 1

AAAAAC 50 10

AAAAAG 5 30

AAAAAT 3 ..

T

  • PrEXON[AAAAAACGAGAC..]

    =T[AAAAA,A] T[AAAAA,C] T[AAAAC,G] T[AAACG,A]……

    = (20/T) (50/T)……….

C

AAAAC

AAAAA

G

AAAAG


Scoring for coding regions

Scoring for coding regions

  • The coding differential can be computed as the log odds of the probability that a sequence is an exon vs. and intron.

  • In Genscan, separate transition matrices are trained for each frame, as different frames have different hexamer distributions


Coding differential for 380 genes

Coding differential for 380 genes


Coding region can be detected

Coding region can be detected

  • Plot the coding score using a sliding window of fixed length.

  • The (large) exons will show up reliably.

  • Not enough to predict gene boundaries reliably

Coding


Other signals

Other Signals

  • Signals at exon boundaries are precise but not specific. Coding signals are specific but not precise.

  • When combined they can be effective

ATG

AG

GT

Coding


Combining signals

Combining Signals

  • We can compute the following:

    • E-score[i,j]

    • I-score[i,j]

    • D-score[i]

    • I-score[i]

    • Goal is to find coordinates that maximize the total score

i

j


The second generation of gene finding

The second generation of Gene finding

  • Ex: Grail II. Used statistical techniques to combine various signals into a coherent gene structure.

  • It was not easy to train on many parameters. Guigo & Bursett test revealed that accuracy was still very low.

  • Problem with multiple genes in a genomic region


Combining signals using d p

Combining signals using D.P.

  • An HMM is the best way to model and optimize the combination of signals

  • Here, we will use a simpler approach which is essentially the same as the Viterbi algorithm for HMMs, but without the formalism.


Hidden states gene structure

i1

i2

i3

i4

IIIIIEEEEEEIIIIIIEEEEEEIIIIEEEEEE IIIII

Hidden states & gene structure

  • Identifying a gene is equivalent to labeling each nucleotide as E/I/intergenic etc.

  • These ‘labels’ are the hidden states

  • For simplicity, consider only two states E and I


Gene finding reformulated

i1

i2

i3

i4

IIIIIEEEEEEIIIIIIEEEEEEIIIIEEEEEE IIIII

Gene finding reformulated

  • Given a labeling L, we can score it as

  • I-score[0..i1-1] + E-score[i1..i2] + D-score[i2+1] + I-score[i2+1..i3-1] + A-score[i3-1] + E-score[i3..i4] + …….

  • Goal is to compute a labeling with maximum score.


Optimum labeling using d p viterbi

Optimum labeling using D.P. (Viterbi)

  • Define VE(i) = Best score of a labeling of the prefix 1..i such that the i-th position is labeled E

  • Define VI(i) = Best score of a labeling of the prefix 1..i such that the i-th position is labeled I

  • Why is it enough to compute VE(i) & VI(i) ?


Optimum parse of the gene

Optimum parse of the gene

j

i

j

i


Generalizing1

Generalizing

  • Note that we deal with two states, and consider all paths that move between the two states.

E

I

i


Generalizing2

Generalizing

IG

  • We did not deal with the boundary cases in the recurrence.

  • Instead of labeling with two states, we can label with multiple states,

    • Einit, Efin, Emid,

    • I, IG (intergenic)

I

Efin

Emid

Note: all links are not

shown here

Einit


An hmm for gene structure

An HMM for Gene structure


Gene finding via hmms

Gene Finding via HMMs

IG

  • 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)

I

Efin

Emid

Note: all links are not

shown here

Einit

i


End of l9

End of L9


Generalized hmms and other refinements

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.


Length distributions of introns exons

Length distributions of Introns & Exons


Generalized hmm for gene finding

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.


Forward algorithm for gene finding

Forward algorithm for gene finding

qk

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


De novo gene prediction summary

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


Dna signals

ATG

5’ UTR

3’ UTR

exon

intron

Translation start

Acceptor

Donor splice site

Transcription start

DNA Signals

  • Coding versus non-coding

  • Splice Signals

  • Translation start


Dna signal example

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.


Cse182 l9

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


Improvements to signal detection

Improvements to signal detection

  • Pr[GGTA] is a donor site?

    • 0.5*0.5

  • Pr[CGTA] is a donor site?

    • 0.5*0.5

  • Is something wrong with this explanation?

GGTA

GGTA

GGTA

GGTA

CGTG

CGTG

CGTG

CGTG


Cse182 l9

MDD

  • PWMs do not capture correlations between positions

  • Many position pairs in the Donor signal are correlated


Maximal dependence decomposition

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’


Mdd for donor sites

MDD for Donor sites


Gene prediction summary

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


How many genes do we have

How many genes do we have?

Nature

Science


Alternative splicing

Alternative splicing


Comparative methods

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.


Comparative gene finding tools

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)


Databases

Databases

  • RefSeq and other databases maintain sequences of full-length transcripts/genes.

  • We can query using sequence.


Course

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


Silly quiz

Silly Quiz

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


Genome sequencing and assembly

Genome Sequencing and Assembly


Dna sequencing

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.


Sequencing

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


Cse182 l9

  • Automated detectors ‘read’ the terminating bases.

  • The signal decays after 1000 bases.


Sequencing genomes clone by clone

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


Shotgun sequencing

Shotgun sequencing of clones was considered viable

However, researchers in 1999 proposed shotgunning the entire genome.

Shotgun Sequencing


Library

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

Library


Sequencing1

Sequencing


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.

Questions


Lander waterman statistics

Lander Waterman Statistics

Island

L

G


Lw statistics questions

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.


Analysis expected number islands

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)


Prob of an island ending at i

Prob. of an island ending at i

L

i

T

  • 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)


Lw statistics

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


Expected of clones in an island

Expected # of clones in an island

Why?


Expected length of an island

Expected length of an island


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