Thursday and Friday

1. Wed (tomorrow) 10am - this suite booked for BLAST searches Thursday and Friday Dr Michael Carton Formerly VO’F group, now National Disease Surveillance Centre (NDSC)

2. TODAY www.nuigalway.ie/microbiology/bioinformaticsnode/home.html Lots of definitions - don’t worry!! But, later on, look stuff up on Google or Scirus

3. Remember: • Homology:- sequences are homologous if they are related by divergence from a common ancestor

4. Sequence alignment • In order to detect sequence homology we must first align sequences. • An alignment is a hypothesis of positional homology between nucleotides/amino acids.

5. Alignment example • Take the case of a hypothetical ancestral sequence (GAATTCGC). Over time mutation may lead to two different forms of this sequence, GAATTCGC and GATTGGC.

6. Example continued • Alignment without gaps GAATTCGC GATTGGC ** * • Alignments with gaps GAATTCGC or GAATTC–GC GA–TTGGC GA–TT–GGC ** ** ** ** ** **

7. Types of alignment • Local • Local alignment finds short regions of similarity between a pair of sequences • Global • Global alignments attempts to find the optimal alignment over the entire length of the sequences.

8. Local alignment • Finds domains and short regions of similarity between a pair of sequences. The two sequences under comparison do not necessarily need to have high levels of similarity over their entire length in order to receive locally high similarity scores.

9. Local alignment • This feature of local similarity searches give them the advantage of being useful when looking for domains within proteins or looking for regions of genomic DNA that contain introns. Local similarity searches do not have the constraint that similarity between two sequences needs to be observed over the entire length of each gene

10. Global alignment • Finds the optimal alignment over the entire length of the two sequences under comparison. Algorithms of this nature are not particularly suited to the identification of genes that have evolved by recombination or insertion of unrelated regions of DNA. In instances such as this, a global similarity score will be greatly reduced. In cases where genes are being aligned whose sequences are of comparable length and also whose entire gene is homologous (descent from a common ancestor), global alignment works well.

11. PROGRAMS USED • Local • Blast • Fasta3 • Global • Clustalw • Clustalx

12. Terminology • Exact (Exhaustive): • This is a method of looking at all possibilities for a particular problem and then choosing the best one. It is the most rigorous method. • Heuristic: • This class of methods takes short-cuts and attempts to arrive at an optimal solution by making educated guesses.

13. Matrices • Write one sequence horizontally • Write the other sequence vertically to form a grid: 1 1 0 0 1 0 1 0 1

14. Calculating an Alignment Score An alignment’s score is calculated using • Scoring matrix • Gap Opening Penalty • Gap extension penalty

15. Scoring an alignment

16. Previous Example • Alignment without gaps GAATTCGC GATTGGC *** • Alignments with gaps GAATTCGC or GAATTC–GC GA–TTGGC GA–TT–GGC ** ** ** ** ** **

17. Dotplot Matrix I

18. Dotplot Matrix II

19. Chimpanzee haeomoglobin intergenic DNA plotted against itself c. 400 bases Noise is caused by matches that have occurred by chance without any homology present. Can use a filter to reduce the noise, eg. only place a dot when a specified portion of a small group of successive bases match, eg. window of 10 only highlighted if 6 of the 10 bases match

20. 8 out of 10, even less noise

21. Chimp and spider monkey DNA, but c. 4,000 bases this time IDENTITY DOT BLOT -identity blocks -looks for blocks of perfect identity, -reduces time required

22. Scoring matrix • In reality, we know that certain mutations are more likely to have occurred than others. • Conservation of the secondary structure of proteins is an important consideration. • The mutation of the third base in a codon often results in no change in the amino acid coded for. • Observations of alignments of amino acid sequences have been used to calculate the probability of certain substitutions.

23. Scoring Matrices • Scoring matrices tell how similar amino acids are. • There are two main sets of scoring matrices: PAM and BLOSUM. • PAM is based on evolutionary distances • BLOSUM is based on structure/function similarities

24. AA Matrices • Assigning a score to all of the 210 possible amino acid substitutions has been done by several authors but 2 are especially noteworthy • Dayhoff et al. (1978) used amino acid alignments of sequences that were 85% similar as a basis for the PAM mutation data matrices

25. AA Matrices • Henikoff and Henikoff (1992) used several different alignments to produce the BLOSUM matrices. • The Blosum 62 Matrix is based on an alignment of sequences that are at least 62% similar • This is possibly the most used of amino acid substitution matrices and is the default matrix used in several applications

26. Scoring matrices • These have been empirically determined and have been calculated by the direct comparison of related protein sequences. • In general, amino acid substitutions that are seen to occur very rarely are given a negative value. • Conservative substitutions (i.e., isoleucine for leucine) are given a positive value. Identical matches are also given a positive value.

28. The bottom line on PAM Frequencies of alignment Frequencies of occurrence The probability that two amino acids, i and j are aligned by evolutionary descent divided by the probability that they are aligned by chance

29. BLOSUM Matrices • BLOSUM is built from distantly related sequences whereas PAM is built from closely related sequences. • BLOSUM is built from conserved blocks of aligned protein segment found in the BLOCKS database.

30. PAM and BLOSUM • Running searches with different matrices will help find different sorts of hits. • PAM30 will preferentially find homologues that are evolutionarily close • PAM250 will tend to find long, weak diffuse matches typical of distantly related proteins. • BLOSUM62 is based on alignments of proteins that are at least 62% similar.

31. Evolutionary Basis of Sequence Alignment 1. Similarity: Quantity that relates to how alike two sequences are. 2. Identity: Quantity that describes how alike two sequences are in the strictest terms. 3. Homology: a conclusion drawn from data suggesting that two genes share a common evolutionary history.

32. Evolutionary Basis of Sequence Alignment (Cont. 1) 1. Example: Shown on the next page is a pairwise alignment of two proteins. One is mouse trypsin and the other is crayfish trypsin. They are homologous proteins. The sequences share 41% identity. 2. Underlined residues are identical. Asterisks and diamond represent those residues that participate in catalysis. Five gaps are placed to optimize the alignment.

34. Evolutionary Basis of Sequence Alignment (Cont. 2) Why are there regions of identity? 1) Conserved function-residues participate in reaction. 2) Structural-residues participate in maintaining structure of protein. (For example, conserved cysteine residues that form a disulfide linkage) 3) Historical-Residues that are conserved solely due to a common ancestor gene.

35. Sequence Homology Searching Find related sequences in the database

36. Original BLAST • Segment pair- this is a pair of subsequences of the same length that form an ungapped alignment. • BLAST searches for all segment pairs between the query sequence and all of the sequences in the database (above a certain threshold). • HSP-High-Scoring Pair.

37. Original Blast • HSPs are derived by first finding the pairs that satisfy the threshold (T) conditions. Then the alignment is extended in both directions unyil the quality of the alignment drops off dramatically or falls to zero • The HSPs are then sorted according to their score

38. Gapped BLAST • The original BLAST suffered from the limitation of not being able to introduce gaps into the alignment. • Gapped BLAST is an effort to circumvent this shortcoming. • Experience shows that often several ungapped non-overlapping alignments result from a match to a single database entry.

39. Two-Hit method • Find 2 HSPs within a distance m of each other on the same diagonal. • Do not attempt an HSP extension unless you find two regions that meet this criterion. • Attempt to generate a single gapped alignment in this region.

40. FastA algorithm • Is the alignment significant? • Could we see an alignment like this purely by chance? • What are the statistics involved?

41. ktups Sequence X GAATTCGCATC This 11 base sequence can be divided into six 6-long segments of DNA • GAATTC • AATTCG • ATTCGC • TTCGCA • TCGCAT • CGCATC These are known as ‘ktuples’ (ktup Fasta). Sequences in databases are stored in this form.

42. Global Alignment vs. Local Alignment • Global alignment is used when the overall gene sequence is similar to another sequence-often used in multiple sequence alignment e.g. Clustal W algorithm • Local alignment is used when only a small portion of one gene is similar to a small portion of another gene. • BLAST • FASTA

43. Different forms of BLAST and FASTA • You have a nucleotide sequence. • Want to compare with other nucleotide sequences • Blastn • Fasta3

44. Different forms of BLAST and FASTA • To compare the 6-frame conceptual translation of the nucleotide sequence against a protein database • Blastx • Fastx3 • Fasty3

45. Different forms of BLAST and FASTA • If we translate our nucleotide sequence, we can compare it to the translation of a nucleotide database; • tBlastn • tFasty3

46. Homology Search Tools • BLAST (Basic Local Alignment Search Tool) by Stephen Altschul • http://www.ncbi.nih.gov/ • FASTA by William Pearson • http://www.ebi.ac.uk/ Open a new word file and 3 web browser windows