Presentation prepared for BIO/CS 471: Algorithms for Bioinformatics. The lecture comprises material from the text Fundamental Concepts of Bioinformatics

Krane, Dan E.

Raymer, Michael L.

English

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Wright State University CORE Scholar Computer Science and Engineering Faculty Publications Computer Science & Engineering 2003 Searching Sequence Databases Dan E. Krane Wright State University - Main Campus, dan.krane@wright.edu Michael L. Raymer Wright State University - Main Campus, michael.raymer@wright.edu Follow this and additional works at: https://corescholar.libraries.wright.edu/cse  Part of the Computer Sciences Commons, and the Engineering Commons Repository Citation Krane, D. E., & Raymer, M. L. (2003). Searching Sequence Databases. . https://corescholar.libraries.wright.edu/cse/383 This Presentation is brought to you for free and open access by Wright State University’s CORE Scholar. It has been accepted for inclusion in Computer Science and Engineering Faculty Publications by an authorized administrator of CORE Scholar. For more information, please contact library-corescholar@wright.edu. Searching Sequence DatabasesBIO/CS 471 – Algorithms for BioinformaticsDatabase Searches 2Database Searching• How can we find a particular short sequence in a database of sequences (or one HUGE sequence)?• Problem is identical to local sequence alignment, but on a much larger scale.• We must also have some idea of the significance of a database hit.• Databases always return some kind of hit, how much attention should be paid to the result?Database Searches 3BLAST• BLAST – Basic Local Alignment Search Tool• An approximation of the Needleman & Wunsch algorithm• Sacrifices some search sensitivity for speedDatabase Searches 4Scoring Matrices• DNA• Identity• Transition/TransversionA R N D C Q E G H I L K M F P S T W Y VA 2R -2 6N 0 0 2D 0 -1 2 4C -2 -4 -4 -5 4Q 0 1 1 2 -5 4E 0 -1 1 3 -5 2 4G 1 -3 0 1 -3 -1 0 5H -1 2 2 1 -3 3 1 -2 6I -1 -2 -2 -2 -2 -2 -2 -3 -2 5L -2 -3 -3 -4 -6 -2 -3 -4 -2 2 6K -1 3 1 0 -5 1 0 -2 0 -2 -3 5M -1 0 -2 -3 -5 -1 -2 -3 -2 2 4 0 6F -4 -4 -4 -6 -4 -5 -5 -5 -2 1 2 -5 0 9P 1 0 -1 -1 -3 0 -1 -1 0 -2 -3 -1 -2 -5 6S 1 0 1 0 0 -1 0 1 -1 -1 -3 0 -2 -3 1 3T 1 -1 0 0 -2 -1 0 0 -1 0 -2 0 -1 -2 0 1 3W -6 2 -4 -7 -8 -5 -7 -7 -3 -5 -2 -3 -4 0 -6 -2 -5 17Y -3 -4 -2 -4 0 -4 -4 -5 0 -1 -1 -4 -2 7 -5 -3 -3 0 10V 0 -2 -2 -2 -2 -2 -2 -1 -2 4 2 -2 2 -1 -1 -1 0 -6 2 4• Proteins• PAM• BLOSUMDatabase Searches 5The BLAST algorithm• Break the search sequence into words• W = 3 for proteins, W = 12 for DNA• Include in the search all words that score above a certain value (T) for any search wordMCGPFILGTYCMCGCGPMCG, CGP, GPF, PFI, FIL, ILG, LGT, GTY, TYCMCG CGPMCT MGP …MCN CTP… …This list can be computed in linear timeDatabase Searches 6The Blast Algorithm (2)• Search for the words in the database• Word locations can be precomputed and indexed• Searching for a short string in a long string Regular expression matching:  FSA• HSP (High Scoring Pair) = A match between a query word and the database• Find a “hit”:  Two non-overlapping HSP’s on a diagonal within distance A• Extend the hit until the score falls below a threshold value, XDatabase Searches 8Results from a BLAST searchDatabase Searches 9Search Significance Scores• A search will always return some hits.• How can we determine how “unusual” a particular alignment score is?• ORF’s AssumptionsDatabase Searches 10Assessing significance requires a distribution• I have an apple of diameter 5”. Is that unusual?Diameter (cm)FrequencyDatabase Searches 11Is a match significant?• Match scores for aligning my sequence with random sequences.• Depends on:• Scoring system• Database• Sequence to search for Length Composition• How do we determine the random sequences?Match scoreFrequencyDatabase Searches 12Generating “random” sequences• Random uniform model:P(G) = P(A) = P(C) = P(T) = 0.25• Doesn’t reflect nature• Use sequences from a database• Might have genuine homology We want unrelated sequences• Random shuffling of sequences• Preserves composition• Removes true homologyDatabase Searches 13What distribution do we expect to see?• The mean of n random (i.i.d.) events tends towards a Gaussian distribution.• Example:  Throw n dice and compute the mean.• Distribution of means:n = 2 n = 1000Database Searches 14The extreme value distribution• This means that if we get the match scores for our sequence with n other sequences, the meanwould follow a Gaussian distribution.• The maximum of n (i.i.d.) random events tends towards the extreme value distribution as ngrows large.Database Searches 15Comparing distributionsµσ( ) βµβµβ−−−−−=xexeexf 1 ( )( )22221 σµπσ−−=xexfExtreme Value: Gaussian:Database Searches 16Determining P-values• If we can estimate β and µ, then we can determine, for a given match score x, the probability that a random match with score x or greater would have occurred in the database.• For sequence matches, a scoring system and database can be parameterized by two parameters, K and λ, related to β and µ.• It would be nice if we could compare hit significance without regard to the database and scoring system used!Database Searches 17Bit Scores• The expected number of hits with score ≥ S is:E = Kmn e −λs• Where m and n are the sequence lengths• Normalize the raw score using:• Obtains a “bit score” S’, with a standard set of units.• The new E-value is:  2lnln KSS −=′ λSmnE ′−= 2Database Searches 18P values and E values• Blast reports E-values• E = 5, E = 10 versus P = 0.993 and P = 0.99995• When E < 0.01 P-values and E-values are nearly identicalDatabase Searches 19BLAST parameters• Lowering the neighborhood word threshold (T)  allows more distantly related sequences to be found, at the expense of increased noise in the results set.• Raising the segment extension cutoff (X) returns longer extensions for each hit.• Changing the minimum E-value changes the threshold for reporting a hit.

Searching Sequence Databases

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Searching Sequence Databases

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