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.
Local Alignment • Initialize the i-1 row and j-1 column with zeros. • For traceback, start with highest value and traceback to zero.
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
BLAST Three phases: 1) List of high scoring words 2) Scan the sequence database 3) Extend hits
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.
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): . . . . . . Note: The line is located at the threshold cutoff. Word size is 3.
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) 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. 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.
The steps to a Gapped BLAST search.
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.
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.
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.
E value (Karlin-Altschul statistics) E = K•m•n•e-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 m•n 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???
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?
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
Filtering Repetitive Sequences and Masking • Options available for user.
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.
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.