1. Last lecture summary

2. The outline of sequence alignment
How to recognize which sequence alignment is better.
Scoring system
Scoring DNA alignment
Scoring protein alignment – substitution matrices (PAM, BLOSUM)
How to perform sequence alignment.
Algorithm
Dot plot, dynamic programming, heuristic algorithms (BLAST)
Bod 2 je algoritmus sekvencniho zarovnani. Pro spravne zarovnani potrebujete bod 1.

3. Flavors of sequence alignment
Homology
Scoring DNA alignment, gaps
Substitution matrix
Scoring protein alignment
PAM matrices, PAM1, higher PAM

4. New stuff

5. PAM 120 small, polar small, nonpolar polar or acidic basic
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

6. PAM matrices assumptions
Mutation of amino acid is independent of previous mutations at the same position.
Only PAM1 was “measured”, all other are predictions.
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

7. How to calculate score? substitution matrix 2 - BLOSUM62 shown here
Selzer, Applied bioinformatics.
How to calculate score?
substitution matrix 2
- BLOSUM62 shown here

8. 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 are accounted for by scoring systems.
When nucleotide sequence is analyzed, it is usually preferable to study the protein sequences.
Particularly 3rd position in codon does not change the coded amino acid.

9. Similarity vs. identity
Similarity refers to the percentage of aligned residues that can be more readily substituted for each other.
have similar physicochemical characteristics and
the selective pressure results in some mutations being accepted and others being eliminated
S = [(Ls × 2)/(La + Lb)] × 100
number of aligned residues
with similar characteristics
total lengths of each sequence

10. Homology vs. similarity
Two sequences are homologous when they descended from a common ancestor sequence.
Similarity can be quantified: “two sequences share 40% similarity”.
But NOT “two sequences share 40% homology”. Just “two sequences are homologous”
Qualitative statement
And it is a conclusion about a common ancestral relationship drawn from sequence similarity comparison
- homology is like pregnancy, you’re either pregnant, or you’re not. You are not pregnant for 80%

11. Gaps How will I score this alignment?
The gaps can’t be inserted freely.
Indels are relatively slow evolutionary processes.
And alignments with large gaps do not make biological sense.
Each gap is penalized – a gap penalty
The gap penalty is an adjustable parameter.
Let’s use the gap penalty equaling to -11.
V D S - C Y
V E S L C Y
V D S - C Y
V E S L C Y
S = – =15

12. Gap penalty Affine gap penalty
different for opening and extending
constant for extending
The gap penalty is high – fewer gaps will be inserted
If you’re searching for sequences that are a strict match for your query sequence, the gap penalty should be set high.
This will retrieve regions with very closely related sequences.
The gap penalty is low – more and larger gaps will be inserted
If you are searching for similarity between distantly related sequences, the gap penalty should be set low.

13. Percentage identity = 10%
High gap penalty. Gaps has been inserted only at the beginning and end.
Percentage identity = 10%
(B) Low gap penalty. More gaps. Percentage identity = 18%
Zvelebil, Baum, Understanding bioinformatics.

14. Protein substitution matrices – BLOSUM

15. BLOSUM matrices I
BLOck SUbstitution Matrix by Henikoff and Henikoff, 1992.
Blocks are ungapped sequence motifs. Sequence motif is a conserved stretch of amino acids confering a specific function to a protein.
Any given protein can contain one or more blocks corresponding to its structural/functional motifs.
BLOCKS database – contains multiple alignments of ungapped segments (blocks).
These alignments correspond to highly conserved regions.
BLOCKS database was used to construct BLOSUM matrices
- Henikoff S, Henikoff JG. Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci U S A Nov 15;89(22): PMID:

16. Blocks .
In BLOCKS Database ( Keyword Search (IE only!!!) search for cytosine and methylase. Block Maps – Graphical Map
Odpoved na tento dotaz je na
At the top picture each sequence has 6 blocks.
Bottom are logos of each of six blocks.
A sequence logo is a graphical representation of aligned sequences where at each position the size of each residue is proportional to its frequency in that position and the total height of all the residues in the position is proportional to the conservation (information content) of the position. See paper Schneider TD, Stephens RM. Sequence logos: a new way to display consensus sequences. Nucleic Acids Res. 1990;18(20): PubMed PMID:
Right – sequences forming given block. Just a segment, not whole. And just two blocks out of six are shown

17. BLOSUM matrices II
The Henikoffs focused on substitution patterns only in the most conserved regions of a protein. These regions are (presumably) least prone to change.
The substitution patterns of 2000 blocks were examined and BLOSUM matrices were generated.
Sequences sharing no more than 62% identity were used to calculate the BLOSUM62 matrix.
block is the whole alignment, not individual sequences within it
Short and clear explanation of BLOSUM62 derivation: Eddy SR. Where did the BLOSUM62 alignment score matrix come from? Nat Biotechnol (8): PMID:

18. BLOSUM matrices III
BLOSUM matrices are based on entirely different type of sequence analysis (local vs. global alignment) and on a much larger data set than PAM.
All BLOSUM matrices are based on observed alignments. They are not based on extrapolations (evolutionary model) like PAM.
BLOSUM numbering system goes in reversing order as the PAM numbering system.
The lower the BLOSUM number, the more divergent sequence they represent.

19. PAM vs. BLOSUM I – PAM
At the time of deriving PAM matrices, most known proteins were small, globular and hydrophilic. If researcher believes his protein contains substantial hydrophobic regions, PAM matrices are not that useful.
Most widely used is PAM250.
It is capable of detecting similarities in the 30% range of identity.
Another point of view – PAM250 provides the best look-back in evolutionary time.
PAM250 is most effective if the goal is to know the widest possible range of proteins similar to the given protein.
you may ask a question which particular matrix should be used?
BLOSUM can be compared with PAM using a measure of average information per residue pair in bit units called relative entropy. Relative entropy is 0 when the target (or observed) distribution of pair frequencies is the same as the background (or expected) distribution and increases as these two distributions become more distinguishable. Relative entropy was used by Altschul to characterize the Dayhoff matrices, which show a decrease with increasing PAM. For the BLOSUM series, relative entropy increases nearly linearly with increasing clustering percentage. Based on relative entropy, the PAM 250 matrix is comparable to BLOSUM 45 with relative entropy of =0.4 bit, while PAM 120 is comparable to BLOSUM 80 with relative entropy of =1 bit. BLOSUM 62 is intermediate in both clustering percentage and relative entropy (0.7 bit) and is comparable to PAM 160. Matrices with comparable relative entropies also have similar expected scores.
Altschul SF. Amino acid substitution matrices from an information theoretic perspective. J Mol Biol Jun 5;219(3): PMID:
Selecting the Right Similarity-Scoring Matrix, William R. Pearson, Curr Protoc Bioinformatics. 2013; 43: 3.5.1–3.5.9.,

20. PAM vs. BLOSUM II – PAM
Assume a protein is a known member of the serine protease family.
Using the protein as a query against protein databases with PAM 250 will detect virtually all serine proteases, but also considerable amount of irrelevant hits.
In this case, the PAM120 matrix should be used. It detects similarities in the 50% to 60% identity range.
And to find only those proteins most similar (identity: 70% - 90%) to the query protein, use PAM40.
Let’s summarize:
Locate all potential similarities – PAM250
Determine if the protein belongs to the protein family, database searches – PAM120
Determine the most similar proteins – PAM40
Rule of thumb:
Try PAM250, PAM120, PAM80, PAM60, and PAM30 matrix and use the one that gives the highest ungapped aligment score.

21. PAM vs. BLOSUM III – BLOSUM
Most widely used is BLOSUM62.
BLOSUM62 appears to be superior to PAM250 in detecting distant relationships even if the PAM method is updated with current data sets.
BLOSUM62 is capable of accurately detecting similarities down to the 30% range of an identity.
Determine if the protein belongs to protein family – BLOSUM80 (detects identities at the 50% level)
Determine the most similar proteins – BLOSUM90
- BLOSUM is better than PAM: Altschul, S.F Amino acid substitution matrices from an information theoretic perspective. J. Mol. Biol. 219:

22. Selecting an Appropriate Matrix
Best use
Pam40
Short highly similar alignments
PAM120
Detecting members of a protein family, database searches
PAM250
Longer alingments of more divergent sequences, suspected homology
BLOSUM90
BLOSUM80
Detecting members of a protein family
BLOSUM62
Most effective in finding all potential similarities

23. There are two main families of amino acids substitution scoring matrices:
PAM substitution matrices based on the rate of divergence between sequences
BLOSUM substitution matrices based on the conservation of domains in proteins

24. Sequence alignment algorithms

25. Pairwise alignment algorithms
Dot plot (dot matrix)
Graphical way of comparing two sequences
Dynamic programming
Slow, but formally optimizing
Heuristic methods
Efficient, but not as thorough
Word (also k-tuples) methods
Used in database searches

26. Dot plot

27. Dot plot
Graphical method that allows the comparison of two biological sequences and the identification of regions of a close similarity between them.
Also used for finding direct or inverted repeats in sequences.
good resource on dot plots:

28. - Lines linking the dots in diagonals indicate sequence alignment
- Lines linking the dots in diagonals indicate sequence alignment. Diagonal lines above or below the main diagonal represent internal repeats of either sequence.

29. Self-similarity dot plot I
The DNA sequence EU compared against itself.
- a bit messy. So sliding window can be used.
Introduction to dot-plots, Jan Schulz

30. background
noise
runs of
matched
residues

31. Self-similarity dot plot II
The DNA sequence EU compared against itself.
Window size = 16.
Linear color mapping
Introduction to dot-plots, Jan Schulz

32. Improving dot plot Sliding window – window size (lets say 11)
Stringency (lets say 7) – a dot is printed only if 7 out of the next 11 positions in the sequence are identical
Color mapping
Scoring matrices can be used to assign a score to each substitution. These numbers then can be converted to gray/color.

33. Interpretation of dot plot I
Plot two homologous sequences of interest. If they are similar – diagonal line will occur (matches).
frame shifts
mutations
gaps in diagonal
insertions
shift of main diagonal
deletions

34. Interpretation of dot plot II
Identify repeat regions (direct repeats, inverted repeats) – lines parallel to the diagonal line in self-similarity plot
Microsatellites and minisatellites (these are also called low-complexity regions) can be identified as “squares”.
Palindromatic sequences are shown as lines perpendicular to the main diagonal.
Plaindromatic sequence: V ELIPSE SPI LEV
Bioinformatics explained: Dot plots,

35. Repeats in dot plot direct repeats minisattelites inverted repeats
self-similarity dot plot of NA sequence ofhuman LDL receptor
window 23, stringency 7
inverted repeats
from the book Bioinformatics, David. M. Mount,

36. Interpretation of dot plot – summary
perfect match
palindrom
repeats
partial palindrom
microsatellites
minisatellites
homologous
indel
a-f) are self similiarity dot plots (T1=T2). g-h) are dot plots comparing two different sequences of similar length.
a) A continuous main diagonal shows perfect similarity for symbols with the same indices.
b) Parallels to the main diagonal indicate repeated regions in the same reading direction on different parts of the sequences. In this case a region D is found twice in the sequence (D1, D2, so called ‘duplications’).
c) Lines perpendicular to the main diagonal indicate palindromic areas. In this case the sequence is completely palindromic in the displayed area. As an example the latin sentence ‘SATOR AREPO TENET OPERA ROTAS’ might be consulted.
d) Partially palindromic sequence (For DNA sequences this refers to a perfect match of the normal strand with its reverse complement, which is frequently found for many transposable elements.
e) Bold blocks on the main diagonal indicate repetition of the same symbol in both sequences, e.g. (G)50, so called microsatellite repeats
f) Parallel lines indicate tandem repeats of a larger motif in both sequences, e.g. (AGCTCTGAC)20, so called minisatellite patterns. The distance between the diagonals equals the distance of the motif.
g) When the diagonal is a discontinuous line this indicates that the sequences T1 and T2 share a common source. In literal analyses we may have to deal with plagiarism or in DNA analyses sequences may be homologous because of a common ancestor. The number of interruptions increases with modifications on the text or the time of independent evolution and mutation rate.
h) Partial deletion in sequence 1 or insertion in sequence 2, so called ‘indel’. In protein coding sequences this can be often observed for many different types of domains, which got lost or substituted during evolution (Beaussart et al. 2007). Also comparing mRNA (cDNA) sequences without introns (T1) against the unspliced DNA sequence (T2) generally yields this picture.

37. Dot plot of the human genome
The draft assembly of chromosome 7, compared to the near complete Build34. Diagonals of the same slope but shifted from the main diagonal indicate order disagreements, diagonals with opposite slope represent inversions. The inset magnifies 500 kb covering 3 BACs at a finer resolution and revealing the central BAC was poorly assembled in the draft version.
A. M. Campbell, L. J. Heyer, Discovering genomics, proteomics and bioinformatics

38. Dot plot rules
Larger windows size is used for DNA sequences because the number of random matches is much greater due to the presence of only four characters in the alphabet.
A typical window size for DNA is 15, with stringency 10. For proteins the matrix has not to be filtered at all, or windows 2 or 3 with stringency 2 can be used.
If two proteins are expected to be related but to have long regions of dissimilar sequence with only a small proportion of identities, such as similar active sites, a large window, e.g., 20, and a small stringency, e.g., 5, should be useful for seeing any similarity.

39. Dot plot advantages/disadvantages
All possible matches of residues between two sequences are found. It’s just up to you to choose the most significant ones.
Readily reveals the presence of insertions/deletions and direct and inverted repeats that are more difficult to find by other, more automated methods.
Disadvantages: Most dot matrix computer programs do not show an actual alignment. Does not return a score to indicate how ‘optimal’ a given alignment is (no statistical significance that could be tested).