Basic Local Alignment Search Tool (BLAST)

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Information about Basic Local Alignment Search Tool (BLAST)

Published on June 27, 2014

Author: wijesingheasirisuranga


Basic Local Alignment Search Tool (BLAST) by Stephen F. Altschul et al J. Mol. Bio 1990 W.O.K.A.S Wijesinghe, S. D. L Gunawardena, A. A. M Athukorala

CONTENTS ConclusionResults MethodsIntroduction » » » » » Questions & Answers »



• Function of a newly sequenced gene or a protein can be predicted by discovering it’s homology to a known gene or a protein. • A major task of Bioinformatics is to find homologous sequence in a database of sequences • Databases of DNA and amino acid sequences continue to grow in size. • There are number of software tools based on many algorithms. Introduction


Needleman-Wunsch Algorithm • Dynamic programming algorithm • Assign scores to insertions ,deletions and replacements. • Compute an alignment of two sequences with least mutations. • Measurement of Similarity • Because of computational requirements impractical for searching a large database without a supercomputer Introduction

Smith Waterman • Rapid Heuristic Algorithm • Allows large databases to be searched on common computers FastP Algorithm • David J. Lipman and William R. Pearson in 1985 • First find locally similar regions between 2 sequences based on identities but not gaps. • Then rescores those regions using a similarity matrix. • Quite popular Introduction


BLAST(Basic Local Alignment Search Tool) Algorithm • A new approach to rapid sequence comparison. • It directly approximate alignments that optimize a measure of local similarity called the Maximal Segment Pair(MSP) score. • Recent mathematical results on MSP scores allows performance analysis of this method • Generated alignment has a statistical significance. • Simple & a robust algorithm Introduction

Can be applied to various contexts • DNA sequences • Protein sequences • Motif searches • Gene identification searches • Analysis of multiple similarity regions in long DNA sequences Characteristic features • Flexibility & Tractability. • Very much faster than existing sequence comparison tools of comparable sensitivity. Introduction This research paper describes all the methods & implementations of BLAST algorithm.


THE MSP MEASURE Discussion on Maximal Segment Pairs METHODS

TWO TYPES OF SIMILARITY MEASURES Methods Global • Optimize the overall alignment of two sequences. • Includes large stretches of low similarity Local • Seek only relatively conserved subsequences • Single comparison may yield several distinct subsequence alignments

• Local similarity measure are preferred for database searches where cDNAs can be compared with partially sequence genes. • Many similarity measures including the one they describe begins with a matrix of similarity score for all possible pairs of residues B L A S T Methods

Scoring Alignments • Scoring matrix: 4 x 4 matrix (DNA) or 20 x 20 matrix (protein) • Identities & conservative replacements >>> Positive Scores • Unlikely Replacements >> Negative scores • Amino acid sequences : “PAM” matrix BLOSUM • DNA sequences : match = +5 mismatch = -4 • Sequence Segment – Contiguous stretch of residues of any length • Similarity score for segments is the sum of similarity values for each pair of aligned residues Methods

Maximal Segment Pair (MSP) • Given these rules they have defined a Maximal Segment Pair (MSP) which is the highest scoring pair of identical length segments chosen from 2 sequences. • The similarity score of an MSP is called the MSP score which is calculated by BLAST. • With long sequences the search for the MSP score becomes computationally demanding. • Therefore BLAST searches for locally maximal segment pairs – Score cannot be improved by either extending or shortening segments. Methods


• Goal is to report those database sequences that have MSP score above some cutoff score S. • Statistically the highest MSP score S can be estimated at which “chance similarities” are likely to appear. • BLAST minimizes time spent on database sequences whose similarity with the query has little chance of exceeding this score. Methods

• Let a word pair be a segment pair with a fixed length w. • Main strategy: seek only segment pairs (one from database, one query) that contain a word pair with score at least T. • Such hit will be extended until it exceeds the cutoff score S and those hits will be the final output of BLAST. • Lower T => Fewer false negatives • Lower T => More pairs to analyze Methods


• BLAST finds locally maximal segment pairs that exceeds a particular cutoff. • Detailed annotation of Three Algorithmic steps. • Compile a list of high-scoring words. • Scanning the DB for hits. • Extending hits that meet certain s coring criteria (Extend only word pairs with a score of at least T to determine if it has a segment pair of score at least S). METHODS B L A S T

COMPILING OF HIGH SCORING WORDS • Obtain the list of words in the target sequence (k-mers), that give a score of T or higher when aligned with the query sequence. • Assume that the query sequence is P Q G E F G • We have the following four 3-mers (recall, the number of k-mers is always N-k+1): P Q G Q G E G E F E F G METHODS Each of these 3-mers are then scored against each and every one of the k-mers in each of the target sequence. For long sequences, this could well include all 8000 possible k-mers.

• So, of all pairwise scorings for P Q G (using the BLOSUM- 62 matrix), we can find the following high scoring ones: • P Q G (of course, this is a perfect match) score of 7+5+6 = 18 • P E G score of 7+2+6 = 15 • P Q A score of 7+5+0 = 12 METHODS

SCANNING THE DB FOR HITS • Scan the database for hits with the compiled list of words obtained in previous step. • How efficiently search a long sequence for multiple occurrences of short sequences. • BLAST has two approaches • Indexing approach • Finite state machine METHODS

INDEXING APPROACH – EXAMPLE 1 • Build a lookup table of size |Σ|w for all w-length words in DB. METHODS

INDEXING APPROACH – EXAMPLE 2 • Let w=3. For amino acids, the number of words is 203. • Map a word to an integer between 1 and 203. • Thus a word has an index into an array. • Each index points to a list of matches of the word in the query sequence. • As we scan the database, each database word immediately leads to the hits in the query sequence. METHODS

EXTENDING HITS • Once the hits are located both in the query and the target sequence, extend the hits to form high scoring segment pairs. • Find the highest scoring segment (the maximal segment pair) or those whose score exceeds (another user set) threshold S. • When manage to find a hit (a match between a “word” and a database entry), extend the hit in either direction. • Keep track of the score (use a scoring matrix). METHODS

EXTENDING HITS – EXAMPLE 1 • Extend each seed on either side until the aggregate alignment score falls below a threshold. • Un-gapped: Extend by only either matches or mismatches. • Gapped: Extend by matches, mismatches or a limited number of insertion/deletion gaps. METHODS



Evaluating Statistical Significance RESULTS

• Finally, evaluate the statistical significance of the alignments / scores that exceed the threshold. • BLAST statistical significance of MSP scores can be evaluated by following factors. • Performance of BLAST with random sequences • Performance of BLAST with homologous sequences • Performance comparing long DNA sequences RESULTS B L A S T

Performance of BLAST with random sequences RESULTS

• When two random sequences of length m and n compared, the probability of finding at least one HSP “by chance” is: • Hence, the probability of finding exactly x HSPs with a score ≥ S is given by: RESULTS E eXPXPXP   1)0(1)1(1)1( ! )( x E exXP x E  B L A S T

• Where E can be defined by according to the Karlin-Altschul equation. • E(HSPs with score) ≥ S, also called the E-value: RESULTS

• The probability of finding c or more distinct segment pairs, all with a score of at least S, is given by the formula: • Utilizing this formula can be detected two sequences that share distinct regions of similarity as significantly related. RESULTS      1 0 ! 1)(1)( c i i E i E ecXPcXP B L A S T

The choice of word length & threshold parameters Necessary & Sufficient Adjustments to be Done in Terms of Word Length & Threshold Value RESULTS

Time required to execute BLAST • To Compile List of Words. • To Scan the Database for Hits (MSP>T). • To Extend All Hits to Seek Segment Pairs with Scores Exceeding the Cutoff (HSP>S). • All these three steps depend on W & T Can we make the process more optimal? • Decrease the time spent on step 3, by increasing the W. But there are complementary problems created by larger W. • For Proteins – 20W possible word, Therefor when W increases number of words generated by query grows exponentially. (But number of words increases linearly with the length of the query) • It increases time spent on step 1 & also the amount of memory required. Results B L A S T

Optimal T and W values • For protein sequences W=4, T=17. • For DNA sequences W=11. How W and T affect the performance of BLAST Results B L A S T T Execution Time Accuracy Speed W Execution Time Accuracy Speed Computational Complexity ?

Performance of BLAST with homologous sequences Things to be Noted When Query Sequence BLAST Against Set of Homologous Sequences RESULTS

What is homologous? • Related by common ancestor. Researchers Example 1 • Search for wooly monkey sequence. • When W=4 & T=17. • Found 178 MSPs with scores (50-80). • Random model suggest that BLAST should miss 24 of MSPs • But actual miss 43. • Therefor error = 44.2% Researchers Example 2 • Search for mouse sequences. • Same W & T as previous. • Found 33 MSPs with scores (45-65). • Random model suggest that BLAST should miss 8 of MSPs • But actual miss 2. RESULTS B L A S T

• Failure to detect significant similarity does only shows our inability to detect homology, it does not prove that the sequences are not homologous. • The overall performance of BLAST depends on the distribution of MSP scores. Strengths of BLAST • Great utility is for identify high scoring MSPs quickly. • Takes lower amount of time for the alignment process. Further improvements can be done • Novel approaches like Position Specific Iterated BLAST (PSI BLAST) RESULTS B L A S T

Comparison of two long DNA sequences Does Adjustments of W Make the Process Faster? RESULTS

Main Classes of Locally Similar Regions • Genes. • Long interspersed Repeats. • Anticipated Weaker Similarities. Example (Human Gene VS Rabbit Gene) Step -1 • Match Score = 5, Mismatch Score = -4 & W=12. • 93 Alignments scoring over 200, 57 Alignments scoring over 350 with 1301 highest score. Step -2 • W=8 • Only additional 32 alignments are found score over 200. • Use of Smaller W does not provides new essential information always. Results B L A S T

Results B L A S T The Time & Sensitivity of BLAST on DNA Sequences as a Function of W


Underlying Concept of BLAST • Simple & Robust. • Can be implemented in many ways. • Can be used in variety of contexts. Researchers Implementation • Used a shared memory version of BLAST. • Why shared memory? • Loads compressed DNA file into memory once & allow subsequent steps to skip that step. • BLAST approach permits construction of extremely fast programs for database searching, Which provides additional advantage on mathematical advantage To Whom This Tool Would Help • Molecular Biologists • Doctors & etc… Conclusion B L A S T


Q&A Q&A • What are the disadvantages of Blast with compared to other heuristic algorithms? – by Pubudu • What is the criteria of selecting threshold S and T values using in implementation part of this research paper? – by Parinda • What are the chances that the Maximal Segment Pair score for two unrelated sequences would be greater than or equal to S value? – by Parinda • Among PAM and BLOSUM matrices what is the most suitable matrix when scoring segments in amino acid sequences ? Why? – by Shashika


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