Rationale for searching sequence databases

May 11, 2004Writing projects due May 25Quiz #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-BlastWorkshop-Use of Psi-BLAST to determine sequence similarities.Homework-Due May 20

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.

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

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

Smith-Waterman Algorithm Advances inApplied Mathematics, 2:482-489 (1981)

The Smith-Waterman algorithm is a local alignment tool used to obtain sensitive pairwise similarity alignments. Smith-Watermanalgorithm uses dynamic programming. Operating via a matrix, the algorithm uses backtracing and tests alternative paths tothe highest scoring alignments, and selects the optimal path asthe highest ranked alignment. The sensitivity of the Smith-Waterman algorithm makes it useful for finding localareas 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 someaspects of S-W.

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 zerosd. 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.

0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 00 0 0 5 0 5 0 0 0 0 0 00 0 0 0 3 0 2012 4 0 0 00 10 2 0 0 1 12182214 6 00 2 16 8 0 0 4101828 20 00 0 82113 5 0 41020 27 00 0 6131912 4 0 416 26 00 0 0 0 0 0 0 0 0 0 0 0

H E A G A W G H E E

PAWHEAE

Smith-Waterman (cont. 2)

Put zeros onborders. Assign initial scoresbased on a scoringmatrix. Calculate new scores based onadjacent cell scores.If sum is less thanzero or equal to zerobegin new scoring with next cell.

0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 00 0 0 5 0 5 0 0 0 0 0 00 0 0 0 3 0 2012 4 0 0 00 10 2 0 0 1 12182214 6 00 2 16 8 0 0 4101828 20 00 0 82113 5 0 41020 27 00 0 6131912 4 0 416 26 00 0 0 0 0 0 0 0 0 0 0 0

H E A G A W G H E E

PAWHEAE

Smith-Waterman (cont. 3)

Begin backtrace at themaximum value foundanywhere on the matrix.Continue the backtraceuntil score falls to zero

AWGHE|| ||AW-HE

Score=28

Calculation of percent similarity

A W G H EA W - H E

Blosum45 SCORES 5 15 -5 10 6

GAP EXT. PENALTY -3

% SIMILARITY = NUMBER OF POS. SCORESDIVIDED BY NUMBER OF AAsIN REGION x 100

% SIMILARITY = 4/5 x 100= 80%

% OVERALL SIMILARITY = NUMBER OF POS. SCORESDIVIDED BY NUMBER OF TOTAL AAsIN REGION x 100

%OVERALL SIMILARITY = 4/5 x 100= 80%

Similarity Score = 28

FASTA (Pearson and Lipman 1988)

This is a combination of word search and Smith-Waterman algorithmThe 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)

The FASTA program is the uses Hash tables. These tables speed the process of word search.

Query Sequence = TCTCTC 123456 (position number)Database Sequence = TTCTCTC 1234567 (position number)You choose to use word size = 4 for yourtable (total number of words in your table is44 = 256)

Sequence (totalof 256)

Position w/in query Position w/in DB Offset (Q minus DB)

TCTC 1,3 2,4 -1 or -3 or 1CTCT 2 3 -1TTCT 1

?

FASTA Steps

1

Local regions ofidentity are found

Different offset values

Identical offsetvalues in acontiguous sequence

2

Rescore the local regions using PAM or Blos. matrix

Diagonals are extended

3

Eliminate short diagonalsbelow a cutoff score

4

Create a gapped alignment ina narrow segment and thenperform S-W alignment

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

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____________________ 1 proteins- distantly related 2 proteins- somewhat related (default) 3 DNA-default

FASTA Versions

FASTA-nucleotide or protein sequence searching

FASTx/-compares a translated DNA query sequenceFASTy 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

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.

FASTA Statistical Significance

Z score for a single alignment=

(similarity score - mean score from database)standard deviation from database

Stand. Dev. = scores2 - ( scores)2

Total#ofSequencesTotal#ofSequences

Mean similarity scoresof complete database

Mean similarity scoresof related records

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()

Evaluating the Results of FASTA

BestSCORES Init1: 2847 Initn: 2847 Opt: 2847z-score: 2609.2 E(): 1.4e-138Smith-Waterman score: 2847; 100.0% identity in 413 overlap

GoodSCORES Init1: 719 Initn: 748 Opt: 793z-score: 734.0 E(): 3.8e-34Smith-Waterman score: 796; 41.3% identity in 378 overlap

MediocreSCORES Init1: 249 Initn: 304 Opt: 260z-score: 243.2 E(): 8.3e-07Smith-Waterman score: 270; 35.0% identity in 183 overlap

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.

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

Comparison Matrices

In general, the BLOSUM series is thought to be superior to thePAM series because it is derived from areas of conserved sequences.

It is important to vary the parameters when performing a sequencecomparison. Similarity scores for truly related sequences areusually not sensitive to changes in scoring matrix and gap penalty.

Thus, if your “hits list” holds up after changing these parametersyou can be more sure that you are detecting similar sequences.

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. Theseare used because they run faster than S-W

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.

When to use the correct program

Problem Program Explanation

IdentifyUnknownProtein

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

When to use the correct program (cont. 1)

Problem Program Explanation

Identify new orthologs

TFASTX3;TFASTY3TBLASTN:TBLASTX

Use PAM matrix <=20 or BLOSUM90 to avoid detecting distant relationships. Search EST sequences w/in the same species.

IdentifyESTSequence

FASTX3;FASTY3;BLASTX;TBLASTX

Always attempt to translate your sequence into protein prior to searching.

IdentifyDNASequence

FASTA;BLASTN Nucleotide sequence comparision

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

Filtering Repetitive Sequences

Over 50% of genomic DNA is repetitiveThis is due to: retrotransposons ALU region microsatellites centromeric sequences, telomeric sequences 5’ Untranslated Region of ESTs

Example of ESTs with simple low complexity regions:

T27311GGGTGCAGGAATTCGGCACGAGTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC

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

PSI-BLAST

PSI-position specific iterativea position specific scoring matrix (PSSM) is constructed automatically from multiple HSPs of initial BLAST search. Normal E value is usedThis 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.