An Introduction to Bioinformatics

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 - http://www-lmmb.ncifcrf.gov/~toms/ http://www.lecb.ncifcrf.gov/~toms/paper/nano2/latex/index.html

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 2-630 or about 10-190. • 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 • 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 A l l l T l l l l l O l N l T l l l l l H ll E ll C l A l l l T l l l l 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 PAM 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 • Search for short nearly exact matches • 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