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CSE182-L10

CSE182-L10. MS Spec Applications + Gene Finding + Projects. Relative abundance computation. run. Once we have features matched across runs, we have data identical to microarrays . Features can be ‘identified’ in separate MS2 experiments. feature. intensity. Structural genomics via MS.

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CSE182-L10

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  1. CSE182-L10 MS Spec Applications + Gene Finding + Projects

  2. Relative abundance computation run • Once we have features matched across runs, we have data identical to microarrays . • Features can be ‘identified’ in separate MS2 experiments feature intensity

  3. Structural genomics via MS

  4. Cross-linking • Cross-links are ‘fixed’ length that bind to amino-acids. • How can they help predict structure? • Protocol • Cross-link native protein • Denature, digest • MS/MS (identify cross-linked peptides) • Potentially valuable, but not widely used

  5. Identifying Cross-linked peptides • Identify all peptide pairs, whose mass explains the parent mass. • Given a list of peptide pairs, find the pair, and the linked position that best explains the MS2 data. • What is the number of possible candidate pairs. • Fragmentation in the presence of linkers is poorly understood • How do you separate cross-linked peptides from singly linked, and non-cross-linked peptides?

  6. Identifying cross-linked peptides • Use isotopically labeled cross-linking agents. • Cross-linked peptides will show up as pairs separated by a small mass. • Non cross-linked peptides appear at one position only.

  7. MS application: Protein-protein interaction • Proteins combine to form functional complexes. • An antibody is a special kind of protein that can recognize a specific protein • Use an antibody to recognize a protein in a complex. Isolate & Purify the complex that binds to the antibody. • Identify all the proteins in the complex via mass spectrometry.

  8. Mass Spectrometry: conclusion • Mass Spectrometry can be used to identify peptides, modifications, quantitation, protein structure, protein-protein interaction (complex formation) • Each of these poses significant computational challenges.

  9. Proteomic Databases/Tools

  10. Eukaryotic Gene Prediction

  11. Eukaryotic gene structure

  12. Translation

  13. Gene Features ATG 5’ UTR 3’ UTR exon intron Translation start Acceptor Donor splice site Transcription start

  14. 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 the development of high throughput methods like EST sequencing

  15. EST Sequencing • Suppose we could collect all of the mRNA. • However, mRNA is unstable • An enzyme called reverse transcriptase is used to make a DNA copy of the RNA. • Use DNA polymerase to get a complementary DNA strand. • Sequence the (stable) cDNA from both ends. • This leads to a collection of transcripts/expressed sequences (ESTs). • Many might be from the same gene AAAA TTTT AAAA TTTT

  16. EST Sequencing • Often, reverse transcriptase breaks off early. Why is this a good thing? • The 3’ end may not have a much coding sequence. • We can assemble the 5’ end to get more of the coding sequence

  17. Project 2 • EST assembly • Given a collection of EST (3’) sequences, your goal is to cluster all ESTs from the same gene, and produce a consensus. • How would you do it if we also had 5’ EST sequences?

  18. Project 1 • Goal: Look for signals in the UTR. • The UTR is not boring. It often folds into a 2 D structure and subsequently affects transcription/translation of genes. • What are Riboswitches? • miRNA?

  19. Project 3 • Goal is to predict expressed genes using ESTs/proteins and mass spectrometry.

  20. Project guidelines • 4 Checkpoints. • The first is mainly to identify a project, project partners, and answer a few simple questions to get started. • Deadline 11/3/05.

  21. 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.

  22. 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 Intons are ATGATG • How can you use this idea to find genes?

  23. Generalizing I E AAAAAA AAAAAC AAAAAG AAAAAT Compute a frequency count for all hexamers. Use this to decide whether a sequence is an exon/intron

  24. Coding versus non-coding • Fickett and Tung (1992) compared various measures • Measures that preserve the triplet frame are the most successful. • Genscan: 5th order Markov Model • Conservation across species

  25. Coding vs. non-coding regions Compute average coding score (per base) of exons and introns, and take the difference. If the measure is good, the difference must be biased away from 0.

  26. Coding differential for 380 genes

  27. Other Signals ATG AG GT Coding

  28. 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

  29. 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

  30. 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

  31. HMMs and gene finding • HMMs allow for a systematic approach to merging many signals. • They can model multiple genes, partial genes in a genomic region, as also genes on both strands.

  32. The Viterbi Algorithm

  33. HMMs and gene finding • The Viterbi algorithm (and backtracking) allows us to parse a string through the states of an HMM • Can we describe Eukaryotic gene structure by the states of an HMM? • This could be a solution to the GF problem.

  34. An HMM for Gene structure

  35. 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.

  36. Length distributions of Introns & Exons

  37. 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.

  38. 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

  39. HMMs and Gene finding • Generalized HMMs are an attractive model for computational gene finding • Allow incorporation of various signals • Quality of gene finding depends upon quality of signals.

  40. DNA Signals • Coding versus non-coding • Splice Signals • Translation start

  41. Splice signals • GT is a Donor signal, and AG is the acceptor signal GT AG

  42. 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

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

  44. Choose the position 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>

  45. MDD for Donor sites

  46. De novo Gene prediction: Sumary • 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

  47. How many genes do we have? Nature Science

  48. Alternative splicing

  49. 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.

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