CSE182-L12 Gene Finding Quiz Who are these people, and what is - - PowerPoint PPT Presentation

CSE182-L12

Gene Finding

Quiz

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

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

How many genes do we have?

Nature Science

Alternative splicing

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.

Comparative gene finding tools

• Procrustes/Sim4: mRNA vs. genomic
• Genewise: proteins versus genomic
• CEM: genomic versus genomic
• Twinscan: Combines comparative and de

novo approach.

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)

Genome Assembly

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.

• 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

• Automated

detectors ‘read’ the terminating bases.

• The signal decays

after 1000 bases.

Sequencing Genomes: Clone by Clone

• Clones are constructed to span the entire length
• f the genome.
• These clones are ordered and oriented correctly

(Mapping)

• Each clone is sequenced individually

Shotgun Sequencing

• Shotgun sequencing
• f clones was

considered viable

• However,

researchers in 1999 proposed shotgunning the entire genome.

Library

• Create vectors
• f the sequence

and introduce them into

• bacteria. As

bacteria multiply you will have many copies of the same clone.

Sequencing

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.

Lander Waterman Statistics

G L

G = Genome Length L = Clone Length N = Number of Clones T = Required Overlap c = Coverage = LN/G a = N/G q = T/L s = 1-q

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

• f islands?
• Ans: N exp(-cs)
• The number

increases at first, and gradually decreases.

Analysis: Expected Number Islands

• Computing Expected # islands.
• Let Xi=1 if an island ends at position i, Xi=0
• therwise.
• Number of islands = ∑i Xi
• Expected # islands = E(∑i Xi) = ∑i E(Xi)

• 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)

i

L T

E(Xi) =a 1-a

( )

L-T =ae

• cs

Expected # islands = E(Xi) =

i

Â

Gae-cs = Ne-cs

LW statistics

• Pr[Island contains exactly j clones]?
• Consider an island that has already begun. With

probability e-cs, it will never be continued. Therfore

• Pr[Island contains exactly j clones]=

(1- e-cs ) j-1e-cs

• Expected # j-clone islands

= Ne-cs (1- e-cs ) j-1e-cs

Expected # of clones in an island

ecs

Why?

Expected length of an island

L ecs -1 c Ê Ë Á ˆ ¯ ˜ + (1-s) È Î Í ˘ ˚ ˙