1. Last lecture summary

2. New generation sequencing (NGS) The completion of human genome was just a start of modern DNA sequencing era – “high-throughput next generation sequencing” (NGS). New approaches, reduce time and cost. Holly Grail of sequencing – complete human genome below $ 1000. 1 st generation – Sanger dideoxy method 2 nd generation – sequencing by synthesis (pyrosequencing) 3 rd generation – single molecule sequencing

3. cDNA, EST libraries cDNA – reverse transcriptase, contains only expressed genes (no introns) cDNA library – a collection of different DNA sequences that have been incorporated into a vector EST – Expressed Sequence Tag short, unedited (single-pass read), randomly selected subsequence (200-800 bps) of cDNA sequence generated either from 5’ or from 3’ higher quality in the middle cDNA/EST – direct evidence of transcriptome

4. What is sequence alignment ? CTTTTCAAGGCTTA GGCTTATTATTGC CTTTTCAAGGCTTA GGCTATTATTGC CTTTTCAAGGCTTA GGCT-ATTATTGC Fragments overlaps

5. What is sequence alignment ? CCCCATGGTGGCGGCAGGTGACAG CATGGGGGAGGATGGGGACAGTCCGG TTACCCCATGGTGGCGGCTTGGGAAACTT TGGCGGCTCGGGACAGTCGCGCATAAT CCATGGTGGTGGCTGGGGATAGTA TGAGGCAGTCGCGCATAATTCCG TTACCCCATGGTGGCGGCTGGGGACAGTCGCGCATAATTCCG “EST clustering” CCCCATGGTGGCGGCAGGTGACAG CATGGGGGAGGATGGGGACAGTCCGG TTACCCCATGGTGGCGGCTTGGGAAACTT TGGCGGCTCGGGACAGTCGCGCATAAT CCATGGTGGTGGCTGGGGATAGTA TGAGGCAGTCGCGCATAATTCCG consensus

6. Sequence alphabet side chain charge at physiological pH 7.4 Name3 letters1 letter Positively charged side chains ArginineArgR HistidineHisH LysineLysK Negatively charged side chains Aspartic AcidAspD Glutamic AcidGluE Polar uncharged side chains SerineSerS ThreonineThrT AsparagineAsnN GlutamineGlnQ Special CysteineCysC SelenocysteineSecU GlycineGlyG Proline\ProP Hydrophobic side chains AlanineAlaA LeucineLeuL IsoleucineIleI MethionineMetM PhenylalaninePheF TryptophanTrpW TyrosineTyrY ValineValV AdenineA ThymineT CytosineG GuanineC

7. Sequence alignment Procedure of comparing sequences Point mutations – easy More difficult example However, gaps can be inserted to get something like this ACGTCTGATACGCCGTATAGTCTATCT ACGTCTGATTCGCCCTATCGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT CTGATTCGCATCGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT ----CTGATTCGC---ATCGTCTATCT gapless alignment gapped alignment insertion × deletion indel

8. Why align sequences – continuation The draft human genome is available Automated gene finding is possible Gene: AGTACGTATCGTATAGCGTAA What does it do? One approach: Is there a similar gene in another species? Align sequences with known genes Find the gene with the “best” match

9. Flavors of sequence alignment pair-wise alignment × multiple sequence alignment

10. Flavors of sequence alignment global alignment × local alignment global local align entire sequence stretches of sequence with the highest density of matches are aligned, generating islands of matches or subalignments in the aligned sequences

11. New stuff

12. Evolution wikipedia.org common ancestors

13. Evolution of sequences The sequences are the products of molecular evolution. When sequences share a common ancestor, they tend to exhibit similarity in their sequences, structures and biological functions. Similar function Sequence similarity Similar 3D structure Protein1Protein2 DNA1DNA2 However, this statement is not a rule. See Gerlt JA, Babbitt PC. Can sequence determine function? Genome Biol. 2000;1(5) PMID: 11178260 Similar sequences produce similar proteins

14. Homology During the time period, the molecular sequences undergo random changes, some of which are selected during the process of evolution. Selected sequences accumulate mutations, they diverge over time. Two sequences are homologous when they are descended from a common ancestor sequence. Traces of evolution may still remain in certain portions of the sequences to allow identification of the common ancestry. Residues performing key roles are preserved by natural selection, less crucial residues mutate more frequently.

15. Orhology, paralogy I Orthologs – homologous proteins from different species that possess the same function (e.g. corresponding kinases in signal transduction pathway in humans and mice) Paralogs – homologous proteins that have different function in the same species (e.g. two kinases in different signal transduction pathways of humans) However, these terms are controversially discussed: Jensen RA. Orthologs and paralogs - we need to get it right. Genome Biol. 2001;2(8), PMID: 11532207 and references therein

16. Orthology, paralogy II Orthologs – genes separated by the event of speciation Sequences are direct descendants of a common ancestor. Most likely have similar domain structure, 3D structure and biological function. Paralogs – genes separated by the event of genetic duplication Gene duplication: An extra copy of a gene. Gene duplication is a key mechanism in evolution. Once a gene is duplicated, the identical genes can undergo changes and diverge to create two different genes. http://www.globalchange.umich.edu/globalchange1/current/lectures/speciation/speciation.html

17. Gene duplication 1. Unequal cross-over 2. Entire chromosome is replicated twice This error will result in one of the daughter cells having an extra copy of the chromosome. If this cell fuses with another cell during reproduction, it may or may not result in a viable zygote. 3. Retrotransposition Sequences of DNA are copied to RNA and then back to DNA instead of being translated into proteins resulting in extra copies of DNA being present within cell.

18. Unequal cross-over Homologous chromosomes are misaligned during meiosis. The probability of misalignment is a function of the degree of sharing of repetitive elements.

19. Comparing sequences through alignment – patterns of conservation and variation can be identified. The degree of sequence conservation in the alignment reveals evolutionary relatedness of different sequences The variation between sequences reflects the changes that have occurred during evolution in the form of substitutions and/or indels. Identifying the evolutionary relationships between sequences helps to characterize the function of unknown sequences. Protein sequence comparison can identify homologous sequences from common ancestor 1 billions year ago (BYA). DNA sequences typically only 600 MYA.

20. Identity matrix Scoring systems I DNA and protein sequences can be aligned so that the number of identically matching pairs is maximized. Counting the number of matches gives us a score (3 in this case). Higher score means better alignment. This procedure can be formalized using substitution matrix. A T T G - - - T A – - G A C A T ATCG A1 T01 C001 G0001 How looks such a substitution matrix for proteins? 20x20 unity matrix.

21. Scoring systems II For nucleotide sequences identity matrix is usually good enough. For protein sequences identity matrix is not sufficient to describe biological and evolutionary proceses. It’s because amino acids are not exchanged with the same probability as can be conceived theoretically. For example substitution of aspartic acids D by glutamic acid E is frequently observed. And change from aspartic acid to tryptophan W is very rare. Why is that? 1. Triplet-based genetic code GAT (D) → GAA (E), GAT (D) → TGG (W) 2. Both D and E have similar properties, but D and W differ considerably. D is hydrophylic, W is hydrophobic, D → W mutation can greatly alter 3D structure and consequently function.

22. Genetic code http://www.doctortee.com/dsu/tiftickjian/bio100/gene-expression.html

23. Gaps or no gaps

24. Scoring DNA sequence alignment (1) Match score:+1 Mismatch score:+0 Gap penalty:–1 ACGTCTGATACGCCGTATAGTCTATCT ||||| ||| || |||||||| ----CTGATTCGC---ATCGTCTATCT Matches: 18 × (+1) Mismatches: 2 × 0 Gaps: 7 × (– 1) Score = +11

25. Length penalties We want to find alignments that are evolutionarily likely. Which of the following alignments seems more likely to you? ACGTCTGATACGCCGTATAGTCTATCT ACGTCTGAT-------ATAGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT AC-T-TGA--CG-CGT-TA-TCTATCT We can achieve this by penalizing more for a new gap, than for extending an existing gap  

26. Scoring DNA sequence alignment (2) Match/mismatch score:+1/+0 Origination/length penalty:–2/–1 ACGTCTGATACGCCGTATAGTCTATCT ||||| ||| || |||||||| ----CTGATTCGC---ATCGTCTATCT Matches: 18 × (+1) Mismatches: 2 × 0 Origination: 2 × (–2) Length: 7 × (–1) Score = +7

27. Substitution matrices Substitution (score) matrices show scores for amino acids substitution. Higher score means higher probability of mutation. Conservative substitutions – conserve the physical and chemical properties of the amino acids, limit structural/functional disruption Substitution matrices should reflect: Physicochemical properties of amino acids. Different frequencies of individual amino acids occuring in proteins. Interchangeability of the genetic code.

28. PAM matrices I How to assign scores? Let’s get nature – evolution – involved! If you choose set of proteins with very similar sequences, you can do alignment manually. Also, if sequences in your set are similar, then there is high probability that amino acid difference are due to single mutation. From the frequencies of mutations in the set of similar protein sequences probabilities of substitutions can be derived. This is exactly the approach take by Margaret Dayhoff in 1978 to construct PAM (Accepted Point Mutation) matrices. Dayhoff, M.O., Schwartz, R. and Orcutt, B.C. (1978). "A model of Evolutionary Change in Proteins". Atlas of protein sequence and structure (volume 5, supplement 3 ed.). Nat. Biomed. Res. Found.. pp. 345–358.

29. PAM matrices II Alignments of 71 groups of very similar (at least 85% identity) protein sequences. 1572 substitutions were found. These mutations do not significantly alter the protein function. Hence they are called accepted mutations (accepted by natural selection). Probabilities that any one amino acid would mutate into any other were calculated. If I know probabilities of individual amino acids, what is the probability for the given sequence? Product Thus probabilities are converted to logarithms, and an alignment score can be calculated by summation. Excellent discussion of the derivation and use of PAM matrices: George DG, Barker WC, Hunt LT. Mutation data matrix and its uses. Methods Enzymol. 1990,183:333-51. PMID: 2314281.

30. PAM matrices III Dayhoff’s definition of accepted mutation was thus based on empirically observed amino acids substitutions. The used unit is a PAM. Two sequences are 1 PAM apart if they have 99% identical residues. PAM1 matrix is the result of computing the probability of one substitution per 100 amino acids. PAM1 matrix represents probabilities of point mutations over certain evolutionary time. in Drosophila 1 PAM corresponds to ~2.62 MYA in Human 1 PAM corresponds to ~4.58 MYA

31. PAM1 matrix numbers are multiplied by 10 000

32. Higher PAM matrices What to do if I want get probabilities over much longer evolutionary time? i.e. I want to align sequences with far less than 85% identity. Dayhoff proposed a model of evolution that is a Markov process. We already met (in Lin Alg lecture) linear dynamical system, which is a case of Markov process.

33. Linear dynamical system I

34. Linear dynamical system II

35. Linear dynamical system III PAM1 120. How to avoid multiplications? Diagonalization: A = SΛS -1 Which property of PAM1 matrix helps us in its diagonalization? Its symmetry. And why does it help? It means that eigenvectors are orthonormal. S is orthogonal matrix Q. And what is Q -1 ? Q -1 = Q T ! PAM1 120 = (QΛQ T ) 120 = QΛ 120 Q T

36. Higher PAM matrices Biologically, the PAM120 matrix means that in 100 amino acids there have been 50 substitutions, while in PAM250 there have been 2.5 amino acid mutation at each side. This may sound unusual, but remember, that over evolutionary time, it is possible that an alanine was changed to glycine, then to valine, and then back to alanine. These are called silent substituions.

37. PAM 120 small, polar small, nonpolar polar or acidic basic large, hydrophobic aromatic Zvelebil, Baum, Understanding bioinformatics. Positive score – frequency of substitutions is greater than would have occurred by random chance. Zero score – frequency is equal to that expected by chance. Negative score – frequency is less than would have occurred by random chance.

38. PAM matrices assumptions Mutation of amino acid is independent of previous mutations on the same position (Markov process requirement). Only PAM1 was “measured”, all other are extrapolations (i.e. predictions based on some model). Each amino acid position is equally mutable. Mutations are assumed to be independent of surrounding residues. Forces responsible for sequence evolution over short time are the same as these over longer times. PAM matrices are based on protein sequences available in 1978 (bias towards small, globular proteins) New generation of Dayhoff-type – e.g. PET91

39. How to calculate score? Selzer, Applied bioinformatics. substitution matrix 2