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CSE182-L9

CSE182-L9. Gene Finding (DNA signals) Genome Sequencing and assembly. An HMM for Gene structure. Gene Finding via HMMs. I G. Gene finding can be interpreted as a d.p. approach that threads genomic sequence through the states of a ‘gene’ HMM. E init , E fin , E mid , I, I G (intergenic).

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CSE182-L9

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  1. CSE182-L9 Gene Finding (DNA signals) Genome Sequencing and assembly

  2. An HMM for Gene structure

  3. 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

  4. 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.

  5. Length distributions of Introns & Exons

  6. 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.

  7. 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

  8. 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

  9. 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

  10. 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.

  11. 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

  12. 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

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

  14. 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’

  15. MDD for Donor sites

  16. 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

  17. How many genes do we have? Nature Science

  18. Alternative splicing

  19. 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.

  20. 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)

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

  22. 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

  23. Silly Quiz • Who are these people, and what is the occasion?

  24. Genome Sequencing and Assembly

  25. 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.

  26. 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

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

  28. 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

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

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

  31. Sequencing

  32. 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

  33. Lander Waterman Statistics Island L G

  34. 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.

  35. 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)

  36. 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)

  37. 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

  38. Expected # of clones in an island Why?

  39. Expected length of an island

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