1. Lecture 1 BNFO 240 Usman Roshan

2. Course overview Perl progamming language (and some Unix basics) Sequence alignment problem –Algorithm for exact pairwise alignment –Heuristics for exact multiple alignment –Computational complexity –Heuristics for pairwise alignment and BLAST, FASTA database search –Real world alignment problems –Substitution matrices Phylogeny reconstruction –Estimating distance matrices –Distance based phylogeny reconstruction ---- UPGMA and neighbor joining algorithms

3. Overview (contd) Wednesdays --- meet in GITC 2305 Fridays --- meet in PC Mall room number PC 36 Grade: 50% monthly programming assignment and 50% final exam Texts: –Introduction to Bioinformatics by Arthur Lesk –Beginning Perl for Bioinformatics by James Tisdall

4. DNA Sequence Evolution AAGACTT -3 mil yrs -2 mil yrs -1 mil yrs today AAGACTT T_GACTTAAGGCTT _GGGCTTTAGACCTTA_CACTT ACCTT (Cat) ACACTTC (Lion) TAGCCCTTA (Monkey) TAGGCCTT (Human) GGCTT (Mouse) T_GACTTAAGGCTT AAGACTT _GGGCTTTAGACCTTA_CACTT AAGGCTTT_GACTT AAGACTT TAGGCCTT (Human) TAGCCCTTA (Monkey) A_C_CTT (Cat) A_CACTTC (Lion) _G_GCTT (Mouse) _GGGCTTTAGACCTTA_CACTT AAGGCTTT_GACTT AAGACTT

5. Comparative bioinformatics What is the evolutionary relationship of a set of DNA sequences? What are the evolutionary conserved regions of a set of proteins? How evolutionary close is a pair of species? How similar are two DNA sequences? How similar are a set of DNA sequences?

6. Representing DNA in a format manipulatable by computers DNA is a double-helix molecule made up of four nucleotides: –Adenosine (A) –Cytosine (C) –Thymine (T) –Guanine (G) Since A (adenosine) always pairs with T (thymine) and C (cytosine) always pairs with G (guanine) knowing only one side of the ladder is enough We represent DNA as a sequence of letters where each letter could be A,C,G, or T. For example, for the helix shown here we would represent this as CAGT.

7. Transcription and translation

8. Amino acids Proteins are chains of amino acids. There are twenty different amino acids that chain in different ways to form different proteins. For example, FLLVALCCRFGH (this is how we could store it in a file) This sequence of amino acids folds to form a 3-D structure

9. Protein folding

10. The protein folding problem is to determine the 3-D protein structure from the sequence. Experimental techniques are very expensive. Computational are cheap but difficult to solve. By comparing sequences we can deduce the evolutionary conserved portions which are also functional (most of the time).

11. Protein structure Primary structure: sequence of amino acids. Secondary structure: parts of the chain organizes itself into alpha helices, beta sheets, and coils. Helices and sheets are usually evolutionarily conserved and can aid sequence alignment. Tertiary structure: 3-D structure of entire chain Quaternary structure: Complex of several chains

12. Key points DNA can be represented as strings consisting of four letters: A, C, G, and T. They could be very long, e.g. thousands and even millions of letters Proteins are also represented as strings of 20 letters (each letter is an amino acid). Their 3-D structure determines the function to a large extent.

13. Pairwise sequence alignment How to align two sequences?

14. Pairwise alignment

16. Dynamic programming Define V(i,j) to be the optimal pairwise alignment score between S 1..i and T 1..j (|S|=m, |T|=n)

17. Dynamic programming Time and space complexity is O(mn) Define V(i,j) to be the optimal pairwise alignment score between S 1..i and T 1..j (|S|=m, |T|=n)

18. Tabular computation of scores

19. Traceback to get alignment

20. How do we understand this dynamic programming algorithm? Let’s first look at some example alignments Let’s look at gaps. How do we know where to insert gaps Let’s look at the structure of an optimal alignment of two sequences x and y and how it relates optimal alignments of subsequences of x and y