Last lecture summary

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. • 1st generation – Sanger dideoxy method • 2nd generation – sequencing by synthesis (pyrosequencing) • 3rd 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 Fragments overlaps CTTTTCAAGGCTTA GGCTATTATTGC CTTTTCAAGGCTTA GGCT-ATTATTGC

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

6. Sequence alphabet

7. Sequence alignment • Procedure of comparing sequences • Point mutations – easy • More difficult example • However, gaps can be inserted to get something like this ACGTCTGATACGCCGTATAGTCTATCTACGTCTGATTCGCCCTATCGTCTATCT gapless alignment ACGTCTGATACGCCGTATAGTCTATCTCTGATTCGCATCGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT----CTGATTCGC---ATCGTCTATCT 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 align entire sequence stretches of sequence with the highest density of matches are aligned, generating islands of matches or subalignments in the aligned sequences local

11. New stuff

12. Evolution common ancestors wikipedia.org

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. DNA1 DNA2 Protein1 Protein2 Sequence similarity Similar 3D structure Similar function Similar sequences produce similar proteins However, this statement is not a rule. See Gerlt JA, Babbitt PC. Can sequence determine function? Genome Biol. 2000;1(5) PMID: 11178260

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 • Unequal cross-over • 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. • 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. 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 Identity 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? • Triplet-based genetic code GAT (D) → GAA (E), GAT (D) → TGG (W) • 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-------ATAGTCTATCTACGTCTGATACGCCGTATAGTCTATCT 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 A new species of frog has been introduced into an area where it has too few natural predators. In an attempt to restore the ecological balance, a team of scientists is considering introducing a species of bird which feeds on this frog. Experimental data suggests that the population of frogs and birds from one year to the next can be modeled by linear relationships. Specifically, it has been found that if the quantities Fk and Bk represent the populations of the frogs and birds in the kth year, then The question is this: in the long run, will the introduction of the birds reduce or eliminate the frog population growth?

34. Linear dynamical system II • So this system evolves in time according to x(k+1) = Ax(k). Such a system is called discrete linear dynamical system, matrix A is called transition matrix. • If we need to know the state of the system in time k = 50, we have to compute x(50) = A50x(0). • And the same is true for Dayhoff’s model of evolution. • If we need to obtain probability matrices for higher percentage of accepted mutations (i.e. covering longer evolutionary time), we do matrix powers. • Let’s say we want PAM120 – 120 mutations fixed on average per 100 residues. We do PAM1120.

35. 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.

36. Zvelebil, Baum, Understanding bioinformatics. PAM 120 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. small, polar small, nonpolar polar or acidic basic large, hydrophobic aromatic

37. 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

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

39. Protein vs. DNA sequences • Given the choice of aligning DNA or protein, it is often more informative to compare protein sequences. • There are several reasons for this: • Many changes in DNA do not change the amino acid that is specified. • Many amino acids share related biophysical properties. Though these amino acids are not identical, they can be more easily substituted each with other. These relationships can be accounted for using scoring systems. • When is it appropriate to compare nucleic sequences? • confirming the identity of DNA sequence in database search, searching for polymorphisms, confirming identity of cloned cDNA