1. Local alignment, BLAST and Psi-BLAST October 25, 2012 Local alignment Quiz 2 Learning objectives-Learn the basics of BLAST and Psi-BLAST Workshop-Use BLAST2 to determine local sequence similarities. Homework #6 due Nov 1 Chapter 5, Problem 8 Chapter 6, Problems 1 and 4.

2. Local Alignment 1. Initialize the i-1 row and j-1 column with zeros. 2. 3. For traceback, start with highest value and traceback to zero.

3. Local Alignment (continued)

4. Which software program should one use for local alignment? Most researchers use methods for determining local similarities: Smith-Waterman (gold standard) FASTA BLAST } Do not find every possible alignment of query with database sequence. These are used because they run faster than S-W

5. BLAST Three phases: 1) List of high scoring words 2) Scan the sequence database 3) Extend hits

6. The threshold and word size The program declares a hit if the word taken from the query sequence has a score >= T when a scoring matrix is used. This allows the word size (W) to be kept high (for speed) without sacrificing sensitivity. If T is increased, the number of background hits is reduced and the program will run faster.

7. Phase 1: Compile a list of high-scoring words at or above threshold T. Query sequence is human p53:... RCPHHERCSD... Words derived from query sequence: RCP, CPH, PHH, HHE, … Threshold T (T = 17): Word Scores from BLOSUM scoring matrix Total score RCP5 + 9 + 721 KCP2 + 9 + 718 QCP1 + 9 + 717 ECP0 + 9 + 716 Note: The line is located at the threshold cutoff. Word size is 3....

8. Phase 2: Scan the database for short segments that match the list of acceptable words/scores above or equal to threshold T. These are potential hits. Phase 3: Extend the potential hits to the left and to the right and terminate when the tabulated score drops below a cutoff score. Query EVVRRCPHHERCSD EVVRRCPHHER S+ Sbjct EVVRRCPHHERSSE ( Ch. hamster p53 O09185) If the sequence alignment is extended far enough, and the score is higher than the alignment score the query/sbjct segment is called a hit.

9. The relationship between extension length and cumulative score

10. The steps to a Gapped BLAST search.

11. What are the different BLAST programs? blastp compares an amino acid query sequence against a protein sequence database blastn compares a nucleotide query sequence against a nucleotide sequence database blastx compares a nucleotide query sequence translated in all reading frames against a protein sequence database tblastn compares a protein query sequence against a nucleotide sequence database dynamically translated in all reading frames tblastx compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database. Please note that tblastx program cannot be used with the nr database on the BLAST Web page.

12. What are the different BLAST programs? (continued) psi-blast Compares a protein sequence to a protein database. Performs the comparison in an iterative fashion in order to detect homologs that are evolutionarily distant. blast2 Compares two protein or two nucleotide sequences.

13. The E value (false positive expectation value) The Expect value (E) is a parameter that describes the number of “hits” one can "expect" to see just by chance when searching a database of a particular size. It decreases exponentially as the Similarity Score (S) increases (inverse relationship). The higher the Similarity Score, the lower the E value. Essentially, the E value describes the random background noise that exists for matches between two sequences. The E value is used as a convenient way to create a “significance” threshold for reporting results. When the E value is increased from the default value prior to a sequence search, a larger list with more low-similarity scoring hits can be reported. An E value of 1 assigned to a hit can be interpreted as meaning that in a database of the current size you might expect to see 1 match with a similar score simply by chance.

14. E value (Karlin-Altschul statistics) E = Kmne -λS Where K is a scaling factor (constant), m is the length of the query sequence, n is the length of the database sequence, λ is the decay constant, S is the similarity score. If S increases, E decreases exponentially. If the decay constant increases, E decreases exponentially If mn increases the “search space” increases. Then there is a greater chance for a random “hit” and E increases. A larger database will increase E. However, larger query sequence often results in a lower E value. Why???

15. Thought problem A homolog to a query sequence resides in two databases. One is the UniProt database and the other is the PDB database. After performing BLAST search against the UniProt database you obtain an E value of 1. After performing the BLAST search against the PDB database you obtain an E value of 0.0625. What is the ratio of the sizes of the two databases?

16. Using BLAST to get quick answers to bioinformatics problems TaskBLAST methodTrad. Method Predict protein function (1) Perform blastp on PIR or Swiss-Prot database Perform wet-lab experiment Predict protein function (2) Perform tblastn on NR database Perform wet-lab experiment Predict protein structure Perform blastp against PDB Structure prediction software, x-ray crystal., NMR

17. Using BLAST to get quick answers to bioinformatics problems (cont.) TaskBLAST methodTrad. Method Locate genes in a genome Divide genome into 2-5 kb sequences. Perform blastx against NR protein datbase Run gene prediction software. Perform microarray analysis or RNAs Find distantly related proteins Perform psi-blastNo traditional method Identify DNA sequence Perform blastnScreen genomic DNA library

18. Filtering Repetitive Sequences Over 50% of genomic DNA is repetitive This is due to: retrotransposons ALU region microsatellites centromeric sequences, telomeric sequences 5’ Untranslated Region of ESTs Example of EST with simple low complexity region: T27311 GGGTGCAGGAATTCGGCACGAGTCTCTCTCTCTCTCTCTCTCTCTCTC TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC

19. Filtering Repetitive Sequences and Masking Options available for user.

20. PSI-BLAST PSI-position specific iterative a position specific scoring matrix (PSSM) is constructed automatically from multiple HSPs of initial BLAST search. Normal E value threshold is used. The PSSM is created as the new scoring matrix for a second BLAST search. A low E value threshold is used (E=.001). Result-1) obtains distantly related sequences 2) finds the important residues that provide function or structure.

22. Workshop Is the American crocodile (Crocodylus acutus) more closely related to the sea turtle (Cheloniidae) or to the turkey (Meleagris gallopavo)? Choose two genes from each species and compare using blast2. Record bit score, E-value, percent nucleotide identities, percent similarities and lengths of coverage query/sbjct sequences in your answer.