Rationale for searching sequence databases

June 22, 2005Writing Topics due todayWriting projects due July 8 Learning objectives- Review of Smith-Waterman Program FASTA and BLAST programs. Psi-Blast

Workshop-Use of Psi-BLAST to determine sequence similarities. Use BLASTx to gain information on gene structure.

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”. 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

initn 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.

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 of 10 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

E = K•m•n•e-λS

Where K is 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 exponentiallyIf m•n increases, the “search space” increases and there is a

greater chance for a random “hit”, E increases. Larger database will increase E.

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 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.