1. Rationale for searching sequence databases
May 11, 2004
Writing projects due May 25
Quiz #3 on Thurs., May 20
Learning objectives-Why do we search sequence databases? Understand the Smith-Waterman algorithm of local alignment and the concept of backtracing. FASTA and BLAST programs. Psi-Blast
Workshop-Use of Psi-BLAST to determine sequence similarities.
Homework-Due May 20

2. Why search sequence databases?
1. I have just sequenced a gene. What is known about the gene I sequenced?
2. I have a unique sequence. Is there similarity to another gene that has a known function?
3. I found a new gene in a lower organism. Is it similar to a gene from another species?
4. I have decided to work on a new gene. The people in the field will not give me the plasmid. I need the complete cDNA sequence to perform PCR.

3. Perfect Searches First “hit” should be an exact match.
Next “hits” should contain all of the genes that are related to your gene (homologs)
Next “hits” should be similar but are not homologs

4. How does one achieve the “perfect search”?
Comparison Matrices (PAM vs. BLOSUM)
Database Search Algorithms
Databases
Search Parameters
Expect Value-change threshold for score reporting
Translation-of DNA sequence into protein
Filtering-remove repeat sequences

5. Smith-Waterman Algorithm Advances in Applied Mathematics, 2:482-489 (1981)
The Smith-Waterman algorithm is a local alignment tool used
to obtain sensitive pairwise similarity alignments. Smith-Waterman
algorithm uses dynamic programming. Operating via a matrix,
the algorithm uses backtracing and tests alternative paths to
the highest scoring alignments, and selects the optimal path as
the highest ranked alignment. The sensitivity of the
Smith-Waterman algorithm makes it useful for finding local
areas of similarity between sequences that are too dissimilar for
alignment. The S-W algorithm uses a lot of computer memory.
BLAST and FASTA are other search algorithms that use some
aspects of S-W.

6. Smith-Waterman (cont. 1)
a. It searches for both full and partial sequence matches .
b. Assigns a score to each pair of amino acids
-uses similarity scores
-uses positive scores for related residues
-uses negative scores for substitutions and gaps
c. Initializes edges of the matrix with zeros
d. As the scores are summed in the matrix, any sum below 0 is
recorded as a zero.
e. Begins backtracing at the maximum value found
anywhere in the matrix.
f. Continues the backtrace until the score falls to 0.

7. Smith-Waterman (cont. 2)
H E A G A W G H E E
Put zeros on
borders. Assign initial scores
based on a scoring
matrix. Calculate new scores based on
adjacent cell scores.
If sum is less than
zero or equal to zero
begin new scoring
with next cell. P A W H E

8. Smith-Waterman (cont. 3)
H E A G A W G H E E P A W H E
Begin backtrace at the
maximum value found
anywhere on the matrix.
Continue the backtrace
until score falls to zero
AWGHE
|| ||
AW-HE
Score=28

9. Calculation of percent similarity
A W G H E
A W - H E
Blosum45 SCORES -3
GAP EXT. PENALTY
% SIMILARITY =
NUMBER OF POS. SCORES
DIVIDED BY NUMBER OF AAs
IN REGION x 100
% OVERALL SIMILARITY =
NUMBER OF POS. SCORES
DIVIDED BY NUMBER OF TOTAL AAs
IN REGION x 100
% SIMILARITY = 4/5 x 100
= 80%
%OVERALL SIMILARITY = 4/5 x 100
= 80%
Similarity Score = 28

10. FASTA (Pearson and Lipman 1988)
This is a combination of word search and Smith-Waterman algorithm
The query sequence is divided into small words of certain size.
The initial comparison of the query sequence to the database is performed using these “words”.
If these “words” are located on the same diagonal in an array the region surrounding the diagonals are analyzed further.
Search time is only proportional to size of database not (database*query sequence)

11. The FASTA program is the uses Hash tables.
These tables speed the process of word search.
Query Sequence = TCTCTC
(position number)
Database Sequence = TTCTCTC
(position number)
You choose to use word size = 4 for your
table (total number of words in your table is
44 = 256) ?
Sequence (total
of 256)
Position w/in query
Position w/in DB
Offset (Q minus DB)
TCTC , , or -3 or 1
CTCT
TTCT

12. FASTA Steps 2 1 4 3 Local regions of Rescore the local regions
Different offset values 2 1
Identical offset
values in a
contiguous sequence
Diagonals are extended
Local regions of
identity are found
Rescore the local regions
using PAM or Blos. matrix 4 3
Create a gapped alignment in
a narrow segment and then
perform S-W alignment
Eliminate short diagonals
below a cutoff score

13. Summary of FASTA steps
1. Analyzes database for identical matches that are contiguous (between 5 and 10 amino acids in length (same offset values)).
2. Longest diagonals are scored again using the PAM matrix (or other matrix). The best scores are saved as “init1” scores.
3. Short diagonals are removed.
4. Long diagonals that are neighbors are joined. The score for this joined region is “initn”. This score may be lower due to a penalty for a gap.
5. A S-W dynamic programming alignment is performed around the joined sequences to give an “opt” score.
Thus, the time-consuming S-W step is performed only on top scoring sequences

14. The ktup value
The ktup (for k-tuples) value stands for the length of the word
used to search for identity.
For proteins a ktup value of 3 would give a hash table of 203
elements (8000 entries).
The higher the ktup value the less likely you will get a match unless it is identical (remember the dot plots).
The lower the ktup value the more background you will have
The higher the ktup value the faster analysis (fewer diagonals).
The following rules typically apply when using FASTA:
ktup analysis____________________
proteins- distantly related
proteins- somewhat related (default)
DNA-default

15. FASTA Versions FASTA-nucleotide or protein sequence searching
FASTx/-compares a translated DNA query sequence
FASTy to a protein sequence database (forward
or backward translation of the query)
tFASTx/-compares protein query sequence to
tFASTy DNA sequence database that has been
translated into three forward and three
reverse reading frames

16. FASTA Statistical Significance
A way of measuring the significance of a score considers the mean
of the random score distribution.
The difference between the similarity score for your single alignment
and the mean of the random score distribution is normalized by
the standard deviation of that random score
distribution. This is the Z-score.
Higher Z-scores are better because
the further the real score is from this mean (in standard deviation units)
the more significant it is.

17. FASTA Statistical Significance
Z score for a single alignment=
(similarity score - mean score from database)
standard deviation from database 
( scores)2
 scores2 -
Stand. Dev. =
Total#ofSequences
Total#ofSequences

18. Mean similarity scores
of complete database
Mean similarity scores
of related records

19. FASTA statistics (cont.)
Using the distribution of the z-scores in the database, the FastA
program can estimate the number of sequences that would
be expected to produce, purely by chance, a z-score greater than or
equal to the z-score obtained in the search.
This is reported as the E() value. This value is
the number of sequences you would expect to find with this score by
searching a database of random sequences.
Thus, when z the E()

20. Evaluating the Results of FASTA
Best
SCORES Init1: Initn: Opt: 2847
z-score: E(): 1.4e-138
Smith-Waterman score: 2847; 100.0% identity in 413 overlap
Good
SCORES Init1: 719 Initn: 748 Opt: 793
z-score: E(): 3.8e-34
Smith-Waterman score: 796; 41.3% identity in 378 overlap
Mediocre
SCORES Init1: 249 Initn: 304 Opt: 260
z-score: E(): 8.3e-07
Smith-Waterman score: 270; 35.0% identity in 183 overlap

21. BLAST Basic Local Alignment Search Tool Speed is achieved by:
Pre-indexing the database before the search
Parallel processing
Uses a hash table that contains neighborhood words rather than just random words.

22. Neighborhood words
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 (this is similar to ktup value)) to be kept high (for speed) without sacrificing sensitivity.
If T is increased by the user the number of background hits is reduced and the program will run faster

24. Comparison Matrices
In general, the BLOSUM series is thought to be superior to the
PAM series because it is derived from areas of conserved sequences.
It is important to vary the parameters when performing a sequence
comparison. Similarity scores for truly related sequences are
usually not sensitive to changes in scoring matrix and gap penalty.
Thus, if your “hits list” holds up after changing these parameters
you can be more sure that you are detecting similar sequences.

25. Which Program should one use?
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

26. What are the different BLAST programs?
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.

27. When to use the correct program
Problem Program Explanation
Identify
Unknown
Protein
BLASTP;
FASTA3
General protein comparison. Use ktup=2 for speed; ktup=1 for sensitive search.
Smith-Waterman
Slower than FASTA3 and BLAST but provides maximum sensitivity
TFASTX3;TFASTY3;
TBLASTN
Use if homolog cannot be found in protein databases; Approx. 33% slower
Psi-BLAST
Finds distantly related sequences. It replaces the query sequence with a position-specific score matrix after an initial BLASTP search. Then it uses the matrix to find distantly related sequences

28. When to use the correct program (cont. 1)
Problem Program Explanation
Identify
new
orthologs
TFASTX3;TFASTY3
TBLASTN:TBLASTX
Use PAM matrix <=20 or BLOSUM90 to avoid detecting distant relationships. Search EST sequences w/in the same species.
Always attempt to translate your sequence into protein prior to searching.
Identify
EST
Sequence
FASTX3;FASTY3;
BLASTX;TBLASTX
Identify
DNA
Sequence
FASTA;BLASTN
Nucleotide sequence comparision

29. Choosing the database
Remember that the E value increases approximately linearly with database size.
When searching for distant relationships always use the smallest database likely to contain the homolog of interest.
Thought problem: If the E-value one obtains for a search is 12 in Swiss-PROT and the E-value one obtains for same search is 74 in PIR how large is PIR compared to Swiss-PROT?
74/12 = ~6

30. 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 ESTs with simple low complexity regions:
T27311
GGGTGCAGGAATTCGGCACGAGTCTCTCTCTCTCTCTCTCTCTCTCTC
TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC

31. Filtering Repetitive Sequences (cont. 1)
Programs like BLAST have the option of filtering out low complex regions.
Repetitive sequences increase the chance of a match during a database search

32. 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 is used
This PSSM is as the new scoring matrix for a second BLAST search. Low E value is used E=.001.
Result-1) obtain distantly related sequences
2) find out the important residues that provide function or structure.