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CSE182-L11

CSE182-L11. Protein sequencing and Mass Spectrometry. Whole genome shotgun. Input: Shotgun sequence fragments (reads) Mate pairs Output: A single sequence created by consensus of overlapping reads First generation of assemblers did not include mate-pairs (Phrap, CAP..)

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CSE182-L11

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  1. CSE182-L11 Protein sequencing and Mass Spectrometry CSE182

  2. Whole genome shotgun • Input: • Shotgun sequence fragments (reads) • Mate pairs • Output: • A single sequence created by consensus of overlapping reads • First generation of assemblers did not include mate-pairs (Phrap, CAP..) • Second generation: CA, Arachne, Euler • We will discuss Arachne, a freely available sequence assembler (2nd generation) CSE182

  3. Arachne (also celera assembler) • Overlap • Problem 1: Large all against all computation • Fast overlap computation using k-mer hashing. • Layout • Problem 2: Small contigs with 10X coverage • Solution 2: Use mate-pairs to build super-contigs • Problem 3: Repetitive structure of the genome. CSE182

  4. Problem 3: Repeats CSE182

  5. 40-50% of the human genome is made up of repetitive elements. Repeats can cause great problems in the assembly! Chimerism causes a clone to be from two different parts of the genome. Can again give a completely wrong assembly Repeats & Chimerisms CSE182

  6. How can you detect if your fragment overlap is due to a repeat? Repeats CSE182

  7. Repeat detection • Lander Waterman strikes again! • The expected number of clones in a Repeat containing island is MUCH larger than in a non-repeat containing island (contig). • Thus, every contig can be marked as Unique, or non-unique. In the first step, throw away the non-unique islands. Repeat CSE182

  8. Detecting Repeat Contigs 1: Read Density • Compute the log-odds ratio of two hypotheses: • H1: The contig is from a unique region of the genome. • The contig is from a region that is repeated at least twice CSE182

  9. Detecting Chimeric reads • Chimeric reads: Reads that contain sequence from two genomic locations. • Good overlaps: G(a,b) if a,b overlap with a high score • Transitive overlap: T(a,c) if G(a,b), and G(b,c) • Find a point x across which only transitive overlaps occur. X is a point of chimerism CSE182

  10. Contig assembly • Reads are merged into contigs upto repeat boundaries. • (a,b) & (a,c) overlap, (b,c) should overlap as well. Also, • shift(a,c)=shift(a,b)+shift(b,c) • Most of the contigs are unique pieces of the genome, and end at some Repeat boundary. • Some contigs might be entirely within repeats. These must be detected CSE182

  11. Creating Super Contigs CSE182

  12. Supercontig assembly • Supercontigs are built incrementally • Initially, each contig is a supercontig. • In each round, a pair of super-contigs is merged until no more can be performed. • Create a Priority Queue with a score for every pair of ‘mergeable supercontigs’. • Score has two terms: • A reward for multiple mate-pair links • A penalty for distance between the links. CSE182

  13. Supercontig merging • Remove the top scoring pair (S1,S2) from the priority queue. • Merge (S1,S2) to form contig T. • Remove all pairs in Q containing S1 or S2 • Find all supercontigs W that share mate-pair links with T and insert (T,W) into the priority queue. • Detect Repeated Supercontigs and remove CSE182

  14. Repeat Supercontigs • If the distance between two super-contigs is not correct, they are marked as Repeated • If transitivity is not maintained, then there is a Repeat CSE182

  15. Filling gaps in Supercontigs CSE182

  16. Consensus Derivation • Consensus sequence is created by converting pairwise read alignments into multiple-read alignments. • The final sequence is reported as a consensus for each of the super contigs. • The supercontigs themselves are ordered using physical markers. • Gaps are filled in using directed sequencing efforts. CSE182

  17. Summary • Whole genome shotgun is now routine: • Human, Mouse, Rat, Dog, Chimpanzee.. • Many Prokaryotes (One can be sequenced in a day) • Plant genomes: Arabidopsis, Rice • Model organisms: Worm, Fly, Yeast • A lot is not known about genome structure, organization and function. • Comparative genomics offers low hanging fruit CSE182

  18. Course Summary 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? • LW statistics • Sequencing and assembly • Next topic: the dynamic aspects of the cell ESTs Protein sequence analysis CSE182

  19. Dynamic aspects of cellular function • Expressed transcripts • Microarrays,…. • Expressed proteins • Mass spectrometry,.. • Protein-protein interactions (protein networks) • Protein-DNA interactions • Population studies CSE182

  20. Mass Spectrometry CSE182

  21. Nobel citation ’02 CSE182

  22. The promise of mass spectrometry • Mass spectrometry is coming of age as the tool of choice for proteomics • Protein sequencing, networks, quantitation, interactions, structure…. • Computation has a big role to play in the interpretation of MS data. • We will discuss algorithms for • Sequencing, Modifications, Interactions.. CSE182

  23. Enzymatic Digestion (Trypsin) + Fractionation Sample Preparation CSE182

  24. Single Stage MS Mass Spectrometry LC-MS: 1 MS spectrum / second CSE182

  25. Tandem MS Secondary Fragmentation Ionized parent peptide CSE182

  26. The peptide backbone The peptide backbone breaks to form fragments with characteristic masses. H...-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH Ri-1 Ri Ri+1 C-terminus N-terminus AA residuei-1 AA residuei+1 AA residuei CSE182

  27. Ionization The peptide backbone breaks to form fragments with characteristic masses. H+ H...-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH Ri-1 Ri Ri+1 C-terminus N-terminus AA residuei-1 AA residuei+1 AA residuei Ionized parent peptide CSE182

  28. Fragment ion generation The peptide backbone breaks to form fragments with characteristic masses. H+ H...-HN-CH-CONH-CH-CO-NH-CH-CO-…OH Ri-1 Ri Ri+1 C-terminus N-terminus AA residuei-1 AA residuei AA residuei+1 Ionized peptide fragment CSE182

  29. Tandem MS for Peptide ID 88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions 100 % Intensity [M+2H]2+ 0 250 500 750 1000 m/z CSE182

  30. Peak Assignment 88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions y6 100 Peak assignment implies Sequence (Residue tag) Reconstruction! y7 % Intensity [M+2H]2+ y5 b3 b4 y2 y3 b5 y4 y8 b8 b9 b6 b7 y9 0 250 500 750 1000 m/z CSE182

  31. Database Searching for peptide ID • For every peptide from a database • Generate a hypothetical spectrum • Compute a correlation between observed and experimental spectra • Choose the best • Database searching is very powerful and is the de facto standard for MS. • Sequest, Mascot, and many others CSE182

  32. Spectra: the real story • Noise Peaks • Ions, not prefixes & suffixes • Mass to charge ratio, and not mass • Multiply charged ions • Isotope patterns, not single peaks CSE182

  33. xn-i yn-i yn-i-1 vn-i wn-i zn-i -HN-CH-CO-NH-CH-CO-NH- CH-R’ Ri i+1 ai R” i+1 bi bi+1 ci di+1 low energy fragments high energy fragments Peptide fragmentation possibilities(ion types) CSE182

  34. Ion types, and offsets • P = prefix residue mass • S = Suffix residue mass • b-ions = P+1 • y-ions = S+19 • a-ions = P-27 CSE182

  35. Mass-Charge ratio • The X-axis is not mass, but (M+Z)/Z • Z=1 implies that peak is at M+1 • Z=2 implies that peak is at (M+2)/2 • M=1000, Z=2, peak position is at 501 • Quiz: Suppose you see a peak at 501. Is the mass 500, or is it 1000? CSE182

  36. Isotopic peaks • Ex: Consider peptide SAM • Mass = 308.12802 • You should see: • Instead, you see 308.13 308.13 310.13 CSE182

  37. Isotopes • C-12 is the most common. Suppose C-13 occurs with probability 1% • EX: SAM • Composition: C11 H22 N3 O5 S1 • What is the probability that you will see a single C-13? • Note that C,S,O,N all have isotopes. Can you compute the isotopic distribution? CSE182

  38. All atoms have isotopes • Isotopes of atoms • O16,18, C-12,13, S32,34…. • Each isotope has a frequency of occurrence • If a molecule (peptide) has a single copy of C-13, that will shift its peak by 1 Da • With multiple copies of a peptide, we have a distribution of intensities over a range of masses (Isotopic profile). • How can you compute the isotopic profile of a peak? CSE182

  39. Nc=50 +1 Isotope Calculation • Denote: • Nc : number of carbon atoms in the peptide • Pc : probability of occurrence of C-13 (~1%) • Then Nc=200 +1 CSE182

  40. Isotope Calculation Example • Suppose we consider Nitrogen, and Carbon • NN: number of Nitrogen atoms • PN: probability of occurrence of N-15 • Pr(peak at M) • Pr(peak at M+1)? • Pr(peak at M+2)? How do we generalize? How can we handle Oxygen (O-16,18)? CSE182

  41. General isotope computation • Definition: • Let pi,a be the abundance of the isotope with mass i Da above the least mass • Ex: P0,C : abundance of C-12, P2,O: O-18 etc. • Characteristic polynomial • Prob{M+i}: coefficient of xi in (x) (a binomial convolution) CSE182

  42. Isotopic Profile Application • In DxMS, hydrogen atoms are exchanged with deuterium • The rate of exchange indicates how buried the peptide is (in folded state) • Consider the observed characteristic polynomial of the isotope profile t1, t2, at various time points. Then • The estimates of p1,H can be obtained by a deconvolution • Such estimates at various time points should give the rate of incorporation of Deuterium, and therefore, the accessibility. CSE182

  43. Quiz • How can you determine the charge on a peptide? • Difference between the first and second isotope peak is 1/Z • Proposal: • Given a mass, predict a composition, and the isotopic profile • Do a ‘goodness of fit’ test to isolate the peaks corresponding to the isotope • Compute the difference CSE182

  44. Post-translational modifications CSE182

  45. Tandem MS summary • The basics of peptide ID using tandem MS is simple. • Correlate experimental with theoretical spectra • In practice, there might be many confounding problems. • Isotope peaks, noise peaks, varying charges, post-translational modifications, no database. • Recall that we discussed how peptides could be identified by scanning a database. • What if the database did not contain the peptide of interest? CSE182

  46. De novo analysis basics • Suppose all ions were prefix ions? Could you tell what the peptide was? • Can post-translational modifications help? CSE182

  47. CSE182

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