1. 1 Genome sizes (sample)

2. 2 Some genomics history 1995: first bacterial genome, Haemophilus influenza, 1.8 Mbp, sequenced at TIGR first use of whole-genome shotgun for a bacterium Fleischmann et al. 1995 became most-cited paper of the year 2869 citations to date 1995-6: 2nd and 3rd bacteria published by TIGR: Mycoplasma genitalium, Methanococcus jannaschii 1996: first eukaryote, S. cerevisiae (yeast), 13 Mbp, sequenced by a consortium of (mostly European) labs 1997: E. coli finished (7th bacterial genome) 1998-2001: T. pallidum (syphilis), B. burgdorferi (Lyme disease), M. tuberculosis, Vibrio cholerae, Neisseria meningitidis, Streptococcus pneumoniae, Chlamydia pneumoniae [all at TIGR] 2000: fruit fly, Drosophila melanogaster 2000: first plant genome, Arabidopsis thaliana 2001: human genome, first draft 2002: malaria genome, Plasmodium falciparum 2002: anthrax genome, Bacillus anthracis TODAY (Sept 4, 2008): 744 complete microbial genomes! 1199 microbial genomes in progress! 476 eukaryotic genomes in progress!

3. 3

4. New directions: sequencing ancient DNA New directions: sequencing ancient DNA (some assembly required)

5. 5 J. P. Noonan et al., Science 309, 597 -599 (2005)

6. 6 Published by AAAS J. P. Noonan et al., Science 309, 597 -599 (2005) Fig. 1. Schematic illustration of the ancient DNA extraction and library construction process

7. 7 Published by AAAS J. P. Noonan et al., Science 309, 597 -599 (2005) Fig. 2. Characterization of two independent cave bear genomic libraries Fig. 2. Predicted origin of 9035 clones from library CB1 (A) and 4992 clones from library CB2 (B) are shown, as determined by BLAST comparison to GenBank and environmental sequence databases. Other refers to viral or plasmid-derived DNAs. Distribution of sequence annotation features in 6,775 nucleotides of carnivore sequence from library CB1 (C) and 20,086 nucleotides of carnivore sequence from library CB2 (D) are shown as determined by alignment to the July 2004 dog genome assembly.

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9. 9

10. 10 Published by AAAS H. N. Poinar et al., Science 311, 392 -394 (2006) Fig. 1. Characterization of the mammoth metagenomic library, including percentage of read distributions to various taxa

11. 11

12. 12 Journals The very best: Science www.sciencemag.org Nature www.nature.com/nature PLoS Biology www.plosbiology.org

13. 13 Bioinformatics Journals Bioinformatics bioinformatics.oxfordjournals.org BMC Bioinformatics www.biomedcentral.com/bioinformatics PLoS Computational Biology compbiol.plosjournals.org Journal of Computational Biology www.liebertpub.com/cmb

14. 14 Radically new journals PLoS ONE www.plosone.org Biology Direct www.biology-direct.com Reviewers’ comments are public Both journals can be annotated by readers Papers can be negative results, confirmations of other results, or brand new

15. 15 Genomics Journals Genome Biology genomebiology.com Genome Research www.genome.org Nucleic Acids Research nar.oxfordjournals.org BMC Genomics www.biomedcentral.com/bmcgenomics

16. Before assembly… … we need to cover a basic sequence alignment algorithm 16

17. 17 Sequence Alignment When we have very similar sequences: Closely related species Very little changed sequence Small differences can be very important Computationally “easy” to align Assembly ONLY deals with these When sequences are not so similar: Distantly related species Most positions changed Sequences that are most highly conserved are under the strongest selective (evolutionary) pressure. –E.g., some genes in humans and E. coli clearly have a common ancestor, the proteins can be aligned Computationally “difficult” to align

18. 18 Sequence Alignment Algorithms for sequence alignment Choose best alignment, subject to some mutation model. A common (but overly simplistic) model for DNA mutations is called “edits”, which counts the number of substitutions, insertions and deletions. The resulting alignment suggests a possible “history” for the sequence. This slide and subsequent alignment slides courtesy of Nathan Edwards, available at www.umiacs.umd.edu/~nedwards/teaching/CMSC858E_Fall_2005/

19. 19 Example Alignments ACGTCTAG ||*****^ ACTCTAG- 2 matches, 5 mismatches, 1 not aligned

20. 20 Example Alignments ACGTCTAG ^**||||| -ACTCTAG 5 matches, 2 mismatches, 1 not aligned

21. 21 Example Alignments ACGTCTAG ||^||||| AC-TCTAG 7 matches, 0 mismatches, 1 not aligned Edit distance here = 1

22. 22 Example Alignments...AACTGAGTTTACGCGCATAGA... |^^^||^|^^| T---CG-A--G Many equally good alignments! Even exact matching sequence can be found (at random) in long enough sequences

23. 23 Global Alignment problem Given two related sequences, S (length n) and T (length m), find an alignment of S and T. Edit distance: minimum number of substitutions, insertions and deletions.

24. 24 Dynamic Programming for pairwise alignment

25. 25 Dynamic Programming Formulation Definition: Let D(i,j) be the edit distance of the alignment of S[1...i] and T[1...j]. Edit distance of S and T, then, is D(n,m). Dynamic programming solves the global alignment problem by computing D(i,j) for all i=0...n and j=0...m.

26. 26 Recurrence Relation for D Computation of D is a recursive/iterative process. D(i,j) in terms of D(i’,j’) for i’ < i and j’ < j. Base conditions for D(i,j): D(i,0) = i, for all i = 0,...,n D(0,j) = j, for all j = 0,...,m

27. 27 Recurrence relation for D For i > 0, j > 0: D(i,j) = min { D(i-1,j) + 1, D(i,j-1) + 1, D(i-1,j-1) + δ(S(i),T(j)) }

28. 28 Dynamic programming D(i,j) is computed by optimally solving sub-problems The optimal solution to D(i,j) is a simple combination (addition) of two optimally solved subproblems

29. 29 Using the recurrence We could code this as a recursive function call......but an exponential number of function evaluations –each position explores 3 alternatives There are only (n+1)x(m+1) pairs i and j We must be evaluating D(i,j) multiple times Why not cache the results?

30. 30 Using the recurrence Compute D(i,j) bottom up. Store the intermediate results in a table (the table we already saw). Start with smallest (i,j) = (1,1). Compute D(i,j) after D(i-1,j), D(i,j-1), and D(i-1,j-1) have been determined. (n+1)(m+1) cells to fill, so O(nm) time.

31. 31 Traceback Our dynamic programming table helps us compute the edit distance “score” We need the actual alignment corresponding to this edit distance The corresponding alignment can be read off, by doing a little extra accounting.

32. 32 Traceback If D(i,j) == D(i-1,j) + 1, Pointer(i,j) = (i-1,j) If D(i,j) == D(i,j-1) + 1, Pointer(i,j) = (i,j-1) If D(i,j) == D(i-1,j-1) + δ(S(i),T(j)), Pointer(i,j) = (i-1,j-1) Break ties arbitrarily, or keep multiple pointers

33. 33 Traceback Follow the pointers from cell (n,m). Any path to (0,0) corresponds to the (reverse of the) edits of the optimal alignment “horizontal” pointers: insertion in S “vertical” pointers: insertion in T “diagonal” pointers: match or substitution An optimal alignment can be found in O(n+m) time.

34. 34 Original references T.F. Smith and M.S. Waterman, Identification of common molecular subsequences. J. Molecular Biology (1981), 147(1):195-7. Altschul SF, Gish W, Miller W, Myers EW, and Lipman DJ. Basic local alignment search tool. J. Molecular Biology (1990), 215(3):403-10. - 24,113 citations!