1. An Introduction to Bioinformatics
Database Searching - Pairwise Alignments

2. AIMS
To explain the principles underlying local and global alignment programs
To explain what substitution matrices are and how they are used
To introduce the commonly used pairwise alignment programs
To explore the significance of alignment results
OBJECTIVES
Carry out FastA and Blast searches
To select appropriate substitution matrices
To evaluate the significance of alignment/search results

3. INTRODUCTION
Sequence comparisons
Protein v Protein
DNA v DNA
Protein v DNA
DNA v Protein
Pair-wise comparison
Methodology

4. Similarity v Homology……….
“If two genes shared a common ancestor then they are homologous”
They did or they didn’t, they are or they arn’t
% Homology

5. Definitions

6. Similarity v Homology…….
But :-
Comparison of two sequences complex
Differences need to be quantified
infer homology from degree of similarity

7. Information theory……….?
Protein sequence = message
4.19 bits per residue
bits = log2M
bit: The amount of information required to distinguish between two equally likely choices
Ref: Molecular Information theory -

8. Are two proteins related ?
Average protein size of 150 residues
Information content of 630 bits.
Probability that two random sequences specify the same message is or about
Convergent evolution giving rise to two similar sequences would be very rare
If two sequences exhibit significant similarity arose from a common ancestor and are homologous.

9. Basic concept
The English alphabet contains 26 letters, that of DNA 4, and that of protein 20
Measure similarity or dissimilarity

10. Basic concept………. Hamming Distance AGATCTAG ACGA
Measure No of differences between two sequences
The answer to the above is…………..
The proportional or p-distance. Hamming distance divided by the total sequence length, so ranges from 0 to 1. In the above example the p-distance is 10/14
AGATCTAG ACGA
AGGCATCATGCAGT 10

11. Basic concept………. The log-odds ratio.
- measure of how unlikely two sequences should be so similar.
- based on the observed frequencies of each of the characters (bases or amino acids) in the sequences, and the probability of observing each homologous pair in the two sequences.
- positive score, measuring similarity, calculated by adding the scores from pre-calculated matrices (PAM and BLOSUM for protein, unitary for DNA).

12. Two problems to consider:
GAPS
genes evolve
deletions, insertions, recombination
give penalties for gap creations and extensions
Global or Local Alignments
Will sequences be similar over their whole length?
Use different algorithms
AGATCTAG-ACGA-TGCAGT
AGGCATCATGCAGT

13. Global and Local Alignments
A global approach will attempt to align two sequences along their entire length
A local alignment will look for local regions of similarity or subsequences.

14. T H E C A T S A T O N T H E M A T
T l l l l l
H l l
E l l R
A l l l
T l l l l l
S l
O l
N l
T l l l l l
C l
Dotplots are the simplest form of alignment
Identical sequences, or subsequences are
identified by diaganol lines

15. DOTTUP website does this analysis
Example of Rabbit v
Emperor Penguin
Haemoglobin

16. Matrices - PAM and BLOSUM
Certain groups of amino acids have similar physico-chemical properties e.g Lysine and Arginine
conservative substitution
Genetic code is degenerate - silent mutations
Dayhoff - Point Accepted Mutation (PAM)

17. Matrices - PAM and BLOSUM
1 PAM unit is the extent of evolutionary divergence in which 1% of amino acid residues are altered
Alignment of 15 very closely related proteins
Calculate a matrix of probability of a mutation altering one amino acid residue to any other amino acid on the basis of 1 PAM.
Extrapolate to PAM250
more useful for proteins not well conserved

18. Problems: derived from proteins of only slight divergence
PAM250 matrix

19. BLOSUM
Henikov and Henikov (1992) derived matrices based on sequences more divergent.
The BLOSUM (BLOcks SUbstition Matrix) matrices cover sequences with 80% or more similarity (BLOSUM 80), 62% or greater similarity (BLOSUM 62) etc
Based on local not global alignments

21. Alignments - local Basic principle
Choose one sequence to be searched against the other
Query sequence (q) and target sequence (t)
Divide the query sequence into small subsequences, called words
For each word of q, look along t to find other words in t which are similar
Matching words "anchors" build up a better alignment between q and t
Assess how good this alignment is.

22. FastA and BLAST FastA Pearson and Lipman Method (late 80s)
Query sequence compared to each sequence in a database
matching words (up to 6 nucleotides, or two amino acids in a row)
Rescore best regions with matrices
Algorithm checks concatenation
Best sequences displayed

23. FastA and BLAST BLAST Basic Local Alignment Search Tool
Compares query to database
For each pair - finds maximal segment pair (using BLOSUM)
The algorithm calculates probability of random occurrence
Faster than FastA, less accurate, method of choice since introduction of GAP-BLAST

25. Significance? Only Local Alignments - without gaps
HSPs/MSPs - alignment occurring by chance (p value) is derived from the observed score (S) to the expected distribution of scores
larger databases - larger probability of a sequence match by chance
the closer the p-value to zero the more confidence can be given to the alignment

26. Types of BLAST Nucleotide BLAST
Standard nucleotide-nucleotide BLAST [blastn]
MEGABLAST
Search for short nearly exact matches
Protein BLAST
Standard protein-protein BLAST [blastp]
PSI- and PHI-BLAST
Translated BLAST Searches
Nucleotide query - Protein db [blastx]
Protein query - Translated db [tblastn]
Nucleotide query - Translated db [tblastx]

27. Example. I have a new mRNA sequence:
TGGCGGCGGCGGCGGCGGTTGTCCCGGCTGTGCCGGTTGGTGTGGCCCGTCAGCCCGCGTACCACAGCGCCCGGGCCGCG
TCGAGCCCAGTACAGCCAAGCCGCTGCGGCCGGGTCCGGCGCGGGCGGCGCGCGCAGACGGAGGGCGGCGGCCGCGGCCA
GGGCGGCCCGTGGGACCGCGGGCCCCCGGCGCAGCGCTGCCCGGCTCCCGGCCCTGCCGGCCTCCTCCCTTGGCGCCGCG
GCCATGGCGGCCAGCGCGAAGCGGAAGCAGGAGGAGAAGCACCTGAAGATGCTGCGGGACATGACCGGCCTCCCGCGCAA
CCGAAAGTGCTTCGACTGCGACCAGCGCGGCCCCACCTACGTTAACATGACGGTCGGCTCCTTCGTGTGTACCTCCTGCT
CCGGCAGCCTGCGAGGATTAAATCCACCACACAGGGTGAAATCTATCTCCATGACAACATTCACACAACAGGAAATTGAA
TTCTTACAAAAACATGGAAATGAAGTCTGTAAACAGATTTGGCTAGGATTATTTGATGATAGATCTTCAGCAATTCCAGA
CTTCAGGGATCCACAAAAAGTGAAAGAGTTTCTACAAGAAAAGTATGAAAAGAAAAGATGGTATGTCCCGCCAGAACAAG
CCAAAGTCGTGGCATCAGTTCATGCATCTATTTCAGGGTCCTCTGCCAGTAGCACAAGCAGCACACCTGAGGTCAAACCA
CTGAAATCTCTTTTAGGGGATTCTGCACCAACACTGCACTTAAATAAGGGCACACCTAGTCAGTCCCCAGTTGTAGGTCG
TTCTCAAGGGCAGCAGCAGGAGAAGAAGCAATTTGACCTTTTAAGTGATCTCGGCTCAGACATCTTTGCTGCTCCAGCTC
CTCAGTCAACAGCTACAGCCAATTTTGCTAACTTTGCACATTTCAACAGTCATGCAGCTCAGAATTCTGCAAATGCAGAT
TTTGCAAACTTTGATGCATTTGGACAGTCTAGTGGTTCGAGTAATTTTGGAGGTTTCCCCACAGCAAGTCACTCTCCTTT
TCAGCCCCAAACTACAGGTGGAAGTGCTGCATCAGTAAATGCTAATTTTGCTCATTTTGATAACTTCCCCAAATCCTCCA
GTGCTGATTTTGGAACCTTCAATACTTCCCAGAGTCATCAAACAGCATCAGCTGTTAGTAAAGTTTCAACGAACAAAGCT
GGTTTACAGACTGCAGACAAATATGCAGCACTTGCTAATTTAGACAATATCTTCAGTGCCGGGCAAGGTGGTGATCAGGG
AAGTGGCTTTGGGACCACAGGTAAAGCTCCTGTTGGTTCTGTGGTTTCAGTTCCCAGTCAGTCAAGTGCATCTTCAGACA
AGTATGCAGCTCTGGCAGAACTAGACAGCGTTTTCAGTTCTGCAGCCACCTCCAGTAATGCGTATACTTCCACAAGTAAT
GCTAGCAGCAATGTTTTTGGAACAGTGCCAGTGGTTGCTTCTGCACAGACACAGCCTGCTTCATCAAGTGTGCCTGCTCC
ATTTGGACGTACGCCTTCCACAAATCCATTTGTTGCTGCTGCTGGTCCTTCTGTGGCATCTTCTACAAACCCATTTCAGA
CCAATGCCAGAGGAGCAACAGCGGCAACCTTTGGCACTGCATCCATGAGCATGCCCACGGGATTCGGCACTCCTGCTCCC
TACAGTCTTCCCACCAGCTTTAGTGGCAGCTTTCAGCAGCCTGCCTTTCCAGCCCAAGCAGCTTTCCCTCAACAGACAGC
TTTTTCTCAACAGCCCAATGGTGCAGGTTTTGCAGCATTTGGACAAACAAAGCCAGTAGTAACCCCTTTTGGTCAAGTTG
CAGCTGCTGGAGTATCTAGTAATCCTTTTATGACTGGTGCACCAACAGGACAATTTCCAACAGGAAGCTCATCAACCAAT
CCTTTCTTATAGCCTTATATAGACAATTTACTGGAACGAACTTTTATGTGGTCACATTACATCTCTCCACCTCTTGCACT
GTTGTCTTGTTTCACTGATCTTAGCTTTAAACACAAGAGAAGTCTTTAAAAAGCCTGCATTGTGTATTAAACACCAGGTA
ATATGTGCAAAACCGAGGGCTCCAGTAACACCTTCTAACCTGTGAATTGGCAGAAAAGGGTAGCGGTATCATGTATATTA
AAATTGGCTAATATTAAGTTATTGCAGATACCACATTCATTATGCTGCAGTACTGTACATATTTTTCTTAGAAATTAGCT
ATTTGTGCATATCAGTATTTGTAACTTTAACACATTGTTATGTGAGAAATGTTACTGGGGAAATAGATCAGCCACTTTTA
AGGTGCTGTCATATATCTTGGAATGAATGACCTAAAATCATTTTAACCATTGCTACTGGAAAGTAACAGAGTCAAAATTG
GAAGGTTTTATTCATTCTTGAATTTTTCCTTTCTAAAGAGCTCTTCTATTTATACATGCCTAAATTCTTTTAAAATGTAG
AGGGATACCTGTCTGCATAATAAAGCTGATCATGTTTTGCTACAGTTTGCAGGTGAAAAAAAATAAATATTATAAAATAA
AAAAAAAAAAAAAGAAAAAAAAAA

28. I’ve pasted my sequence
I’ve selected the database
I hit BLAST!

29. Record this number
Press Format!

34. Setting up a BLAST search
Step 1. Plan the search
Step 2. Enter the query sequence
Step 3. Choose the appropriate search parameters
Step 4. Submit the query
Deciphering the BLAST output
Step 1. Examine the alignment scores and statistics
Step 2. Examine the alignments
Step 3. Review search details to plan the next step
Post-BLAST analysis
Perform a PSI-BLAST analysis
Create a multiple alignment
Try motif searching with PHI-BLAST