pairwise sequence alignment

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Pairwise sequence alignment. Based on presentation by Irit Gat-Viks, which is based on presentation by Amir Mitchel, Introduction to bioinformatics course, Bioinformatics unit, Tel Aviv University. and of Benny shomer, Bar-Ilan university. Where we are in the course?. - PowerPoint PPT Presentation

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  • Pairwise sequence alignment

    Based on presentation by Irit Gat-Viks,which is based on presentation by Amir Mitchel,Introduction to bioinformatics course,Bioinformatics unit, Tel Aviv University.and of Benny shomer, Bar-Ilan university

  • Where we are in the course?Ways to interrogate biobanks:By identifier-based search (GenBank etc.)By genome location (genome browsers)By mining annotation files with scripsNow: searching by sequence similarity

  • What is it good for?Function inference if we know something about A and A is similar to B, we can say something about B guilt by associationConservation arguments if we know that A and B do something similar, by looking at the conserved segments we can infer which parts of A and B are important for their functionLooking for repeats etc.Identifying the position of an mRNA/any transcript in the genomeResequencingEtc.

  • Issues with sequence similarityThings were afterA score: how well do two sequences fit?Statistics: is this score significant or expected at random?Regions: which parts of the query and the target sequence are actually similar/different?Next timeHow to efficiently search a large sequence database

  • Topics to be CoveredIntroductionComparison methods global/local alignmentAlignment parametersAlignment scoring matrices proteinsAlignment scoring matrices DNAEvaluationComparison programs

  • Start from simple: Dot plotsThe most intuitive method to compare two sequences.Each dot represents a identity of two characters.No real score/significance, but very easy to assess visually

  • To Reduce Random Noise in Dot MatrixSpecify a window size, wTake w residues from each of the two sequencesAmong the w pairs of residues, count how many pairs are matchesSpecify a stringency

  • Simple Dot Matrix, Window Size 1

    PVILEPMMKVTIEMPP111V11I11L1E11P111I11M111RV11E11V11T1T1P111

  • Window Size is 3

    PVILEPMMKVTIEMPP31111V311I31111L3111E12111P1112111I11111M121R111111V111111E1121V112T1111T11221P11111113

  • Window Size is 3; Stringency is 2

    PVILEPMMKVTIEMPP3V3I3L3E2P2IM2RVE2V2TT22P3

  • Protein Sequencessingle residue identity6 out of 23 identical

  • Insertion/Deletion, Inversion

  • ABCDEFGEFGHIJKLMNOtandem duplicationcompared to no duplicationtandem duplicationcompared to self

  • What Is This?5 GGCGG 3 Palindrome (Intrastrand)

  • Compare a sequence with itselfIdentifies low complexity/repeat regions

  • Dotlet examplehttp://myhits.isb-sib.ch/cgi-bin/dotlethttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=full_report&list_uids=672http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=full_report&list_uids=353120

  • DefinitionAlignment: A matching of two sequences. A good alignment will match many identical (similar) characters in the two sequences VLSPAD-TNVK-AWAKVGAHAAGHG||| | | |||| | ||||VLSEAEWQ-VLHVWAKVEA--AGHG

  • How similar are two sequences?The common measure of sequence similarity is their alignment scoreSimpler measures, e.g., % identity are also commonThese require algorithm that compute the optimal alignment between sequences

  • How to present the alignment?| - character-wise identity: - very similar amino acids. less similar amino acids- gap in out of the sequences

  • Pairwise Alignment - ScoringThe final score of the alignment is the sum of the positive scores and penalty scores:

    + Number of Identities+ Number if Similarities- Number of Dissimilarities- Number of Gap openings- Number of Gap extensionsAlignment score

  • Comparison methodsGlobal alignment Finds the best alignment across the whole two sequences. Local alignment Finds regions of similarity in parts of the sequences. Global Local _____ _______ __ ____ __ ____ ____ __ ____

  • Global AlignmentAlgorithm of Needleman and Wunsch (1970) Finds the alignment of two complete sequences: ADLGAVFALCDRYFQ|||| |||| |ADLGRTQN-CDRYYQ

    Semi-global alignment allows free endsGFHKKKADLGAVFALCDRYFQ|||| |||| |ADLGRTQN-CDRYYQJKLLKJ

  • Local AlignmentAlgorithm of Smith and Waterman (1981)Makes an optimal alignment of the best segment of similarity between two sequences.ADLGCDRYFQ|||| |||| |ADLGCDRYYQ

    Can return a number of well aligned segments.

  • Finding an optimal alignmentPairwise alignment algorithms identify the highest scoring alignment from all possible alignments.Different scoring systems can produce (very) different best alignments!!! Unfortunately the number of possible alignments if pretty hugeDynamic programming to the rescue

  • Intuition of Dynamic Programming Lets say we want to align XYZ and ABC If we already computed the optimal way to: Align XY and AB Opt1 Align XY and ABC Opt2 Align XYZ and AB Opt3 We now need to test three possible alignments Opt1Z or Opt2Z orOpt3-Opt1COpt2- Opt3C(where - indicates a gap).

    Thus, if we construct small alignments first, we are able to extend then by testing only 3 scenarios.

  • Formally: solving global alignmentGlobal Alignment Problem:Input: Two sequences S=s1sn, T=t1.tm (n~m)Goal: Find an optimal alignment according to the alignment quality (or scoring).

    Notation: Let (a,b) be the score (weight) of the alignment of character a with character b.Let V(i,j) be the optimal score of the alignment of S=s1si and T=t1tj (0 i n, 0 j m)

  • V(k,l) is computed as follows:Base conditions: V(i,0) = k=0..i(sk,-)V(0,j) = k=0..j(-,tk)Recurrence relation:V(i-1,j-1) + (si,tj)1in, 1jm: V(i,j) = max V(i-1,j) + (si,-)V(i,j-1) + (-,tj)Alignment with 0 elements spacingS=s1...si-1 with T=t1...tj-1 si with tj.S=s1...si with T=t1...tj-1and - with tj.V(i,j) := optimal score of the alignment of S=s1si and T=t1tj (0 i n, 0 j m)

  • Optimal Alignment - Tabular ComputationUse dynamic programming to compute V(i,j) for all possible i,j values:for i=1 to n dobegin For j=1 to m do begin Calculate V(i,j) using V(i-1,j-1), V(i,j-1), V(i-1,j) endend

  • Optimal Alignment - Tabular ComputationAdd back pointer(s) from cell (i,j) to father cell(s) realizing V(i,j).Trace back the pointers from (m,n) to (0,0) Needleman-Wunsch, 70Backtracking the alignment

  • Solving Local Alignment

    Algorithm of Smith and Waterman (1981).V(i,j) : the value of optimal local alignment between S[1..i] and T[1..j]Assume the weights fulfill the following condition:(x,y) = 0if x,y match 0o/w (mismatch or indel)

  • Computing Local Alignment (2)A scheme of the algorithm:Find maximum similarity between suffixes of S=s1...si and T=t1...tjDiscard the prefixes S=s1...si, and T=t1...tj whose similarity is 0 (and therefore decrease the overall similarity)Find the indices i*, j* of S and T respectively after which the similarity only decreases.

  • As usual the pointers are created while filling the values in the table, The alignments are found by tracking the pointers from cell (i*, j*) until reaching an entry (i, j) that has value 0.

  • Computational complexityComputing the table requires O(n2) operations for both global and local alignmentSaving the pointers for traceback - O(n2)But what if we are only interested in the optimal alignment score?Only need to remember the last row O(n) space

  • OutlineWe now figured outWhat an alignment isWhat alignment score consists ofHow to efficiently compute an optimal alignmentStill left to figure outWhere do we obtain good (i,j) valuesWhen do we use global/local alignmentHow to use alignment to search large databases

  • Scoring amino acid similarityIdentity: Count the number of identical matches, divide by length of aligned region. The homology rule: above 25% for amino acids, above 75% for nucleotides.Similarity: A less well defined measure A problematic idea: Give positive score for aligning amino acids from the same groupCan we find a better definition for similarity?

  • Scoring System based on evolutionSome substitutions are more frequent than other substitutionsChemically similar amino acids can be replaced without severely effecting the proteins function and structureOrthologous proteins: proteins derived from the same common ancestorBy comparing reasonably close orthologous proteins we can compute the relative frequencies of different amino acid changesAmino acid substitution matrices: Families of matrices that list the probability of change from one amino acid to another during evolution (i.e., defining identity and similarity relationships between amino acids).The two most popular matrices are the PAM and the BLOSUM matrix

  • PAM matrixPAM units measure evolutionary distance.1 PAM unit indicates the probability of 1 point mutation per 100 residues.Multiplying PAM1 by itself gives higher PAMs matrices that are suitable for larger evolutionary distance.JTT matrices are a newer generation of PAMs

  • PAM 1

  • PAM 250

  • Log Odds matricesThe score might arise from bias in amino acid frequency -> We use the log odds of the PAM matrix.

    (120 PAM)

  • Rules of thumbThe most widely used PAM250 is good for about 20% identity between the proteins40% --> PAM12050% --> PAM8060% --> PAM60

  • PAM vs. BLUSOMChoosing nDifferent BLOSUM matrices are derived from blocks with different identity percentage. (e.g., blosum62 is derived from an alignment of sequences that share at least 62% identity.) Larger n smaller evolutionary distance.Single PAM was constructed from at least 85% identity dataset. Different PAM matrices were computationally derived from it. Larger n larger evolutionary distance

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