# Motivating the need for optimal sequence alignments...

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2 Motivating the need for optimal sequence alignments... 2

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4 Note that this actually combines two objectives of optimal sequence alignments: (i) use the score of the alignment o infer homology; (ii) use the alignment itself to study contraints on structure and function. 4

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12 ... No. The existence of indels makes it intractable to consider all possible alignments. 12

13 Note that the term insertion or deletion refers only to the sequences, not to the actual molecular event! 13

14 Number of particles in the universe: on the order of Alignments for two sequences of length 200: ~3 200 =

15 Note: a pair-score does not depend on the context of the aligned pair of amino acids, but only on the two amino acids themselves. Therefore it can be retrieved from a similarity matrix. 15

16 The premise of context independence makes finding an optimal alignment a solvable problem. It is can be shown that alignment problems that are not contextindependent are NP hard, i.e. no algorithm exists that solves such a problem in a number of steps that is proportional to some polynomial of the alignment length. Rather, the number of steps in fully context-sensitive, gapped alignment must be proportional to some number to the power of the alignment length. You can visualize this by considering that context sensitive really means: each local decision (whether to match two characters or insert an indel) is influenced by the state of all characters already in the alignment: all combinations of states are therefore distinct and must be considered separately. This is exactly the procedure which we have considered previously as the brute-force approacht o constructing alignments and found to be intractable. 16

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18 This ironic (!) definition actually defines an infinite recursion - the rule is applied forever. 18

19 Real applications of recursive strategies or algorithms always require a so called Base Case: a situation where the recursion stops and a definite result is generated. More about this at Wikipedia: ( 19

20 Optimal alignment, in the way we have defined the procedure a few slides ago, is simple to write as a recursion. However, implementing the approach as a recursion is very(!) inefficient since it requires looking up many values over and over again. For example if we are to calculate the score for i=9, j=10, we need to consider as one one of the possible extensions the cell i=8, j=9 and x=4 i.e. we need to calculate s 8-4,9-1 -w 4-1 = s 4,8 -w 3. But this is the same value for s we previously had to calculate for the adjacent cell column: i=7, j=9, x=3: s 7-3,9-1 -w 3-1 = s 4,8 -w 2, only with a different w. It is not the w-values that are costly to calculate however, but the s-values themselves, since we need to recurse all the way to the Base Case each time we want to calculate one. So while it is compact to write the alignment in the way given above, in practice we store each intermediate result that is going to be reused. This technique of storing useful intermediate results is called Memoization (not memo r ization) in computer science. (cf. The actual algorithm therefore uses a compact and intuitive way to model the problem: store intermediate values in a matrix where rows and columns correspond to characters in the respective sequences. The highest score in the matrix is the optimal score and the cells that contribute to that score define the optimal alignment. 20

21 An alignment can be represented as a path through a matrix that has a row resp. column for every letter of the two sequences to be aligned. Any alignment can be represented as a path in such a matrix. Only a subset of arrangements correspond to legal paths that represent our normal definition of an alignment. Note that especially in genome/genome comparisons duplications and inversions are common and specialized algorithms are available to perform such alignments (e.g. Shuffle-LAGAN ( )). 21

22 The first step of the Needleman-Wunsch algorithm for global, optimal sequence alignment. This algorithmic strategy is frequently referred to as Dynamic Programming

23 The next scores we need to calculate are the cells in the previous column or row... 23

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27 ... unfortunately, we have no quantitative, mechanistic model for these events. 27

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29 Qian and Goldstein (2001) 1 have shown that a log linear plot of gap probabilities in aligned sequences can be modeled by a sum of four exponential functions. This can be interpreted to mean that several molecular mechanisms could exist for the generation of indels, each with a distinct and characteristic probability of occurrence. However, logarithmic gap penalties do not improve alignments (Cartwright, 2006) 2. Recent developments focus on the inclusion of additional knowledge about the sequences, such as secondary-structure specific gap penalties, or using sequence profiles or multiple alignments, rather than aiming to further improve the gap parameters. The bottom line is: we have no good model for indels, but we have no significantly better model than the simple affine model

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32 In the example above, the ankyrin domain repeats of the yeast transcription factor Mbp1 are shown as a red box in this graphic of domains in sequence families, compiled in the CDART database 1. This domain is found in many other proteins, but some of them do not share the other sequence elements found in Mbp1 - they are only partially related. Attempting a global sequence alignment with such sequences would attempt to align sequences that are actually not homologous, leading to inappropriately low scores and the danger of spurios results. Temple Smith and Michael Waterman 2 have slightly modified the Needleman- Wunsch algorithm, 11 years after its publication, to find the highest scoring local alignment: this is the highest match in the matrix, tracked back to the point where the pathscore drops below zero. The rest of the algorithm works in exactly the same way. There is only one detail that needs to be considered: the substitution matrix must yield a negative expectation value for random alignments. If this were not the case, random pairs could extend the locally high-scoring alignment unreasonably

33 In this example, Smith-Waterman local sequence alignment detects only the highscoring similarity between Mbp1 and Swi4s APSES domains. The lower scoring, more highly diverged ankyrin repeats are missed by the algorithm. The Needleman- Wunsch alignment finds both sets of sequences, albeit there are segments in between that don't align well at all. In this case, one could do a local alignment, remove the matching segments from the input sequences and then redo the alignment to see if any other significantly similar segments are found. 33

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35 Of course the algorithms will optimally align anything you feed them, but for anything but homologous sequence the alignment will be meaningless. Aligning non-homologous sequences is a nice example of cargo-cult bioinformatics. Therefore: if you already know that your proteins are multi-domain, separate out the domains before aligning. If you don't know, critically look at the results, generate a hypothesis about the domain structure and rerun your alignment on the domains separately. The exception, of course: is if you know (or believe) your two proteins comprise homologous domains in the same order. Amino acid sequences are much more highly conserved then genomic sequence and even if you have nucleotide sequences to start from, you should always translate them before aligning. In general, many more matches are required to make nucleotide sequence matches significant, since the alphabet is much smaller. Also, there is no good notion of "similarity" or "conservative mutation" at the nucleotide level 1. The only reasons to align nucleotides are: if you are actually interested in the number and type of nucleotide exchanges, such as in gene assembly and EST clustering, studies of SNPs, in comparative genomics, phylogenetic studies of closely related genes, or defining primer binding sites; if you are aligning untranslated sequences; in particular if it is the nucleotide sequence itself that is conserved, such as in DNA binding sites or splice sites; or if you are studying RNA genes, such as trna or rrna. A corollary is that you should not try to align sequences in highly gapped regions. These residues have evolved in a non-comparable context, they cannot have been conserved by evolution for that reason and applying our scoring matrices cannot compare such residues in a meaningful way. 1 However, transitions (conserving pyrimidines or purines) are more frequent than transversions. See en.wikipedia.org/wiki/models_of_dna_evolution for how this is modelled. 35

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37 Note that reporting %-identity is an objective metric, but it still depends on the exact alignment that has been produced and it does not capture the quality of gaps. 37

38 Identities of 20 to 25% are also called the ''twilight zone'' - in which homology is likely but can't be confidently inferred from sequence similarity alone. These thresholds are based on sequence similarity after optimal alignment. Additional supporting evidence for homology can be contributed from: simlar length; similar functional sequence patterns (e.g. cys/his clusters); similar number of transmembrane helices; similar conservation patterns or conserved motifs; similar amino acid frequencies or bias (eg. polyglutamine, polyproline); similar patterns of disordered sequence; similar structure; similar function; similar genomic context; similar interactors; similar subcellular localization; [...] 38

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### ... and searches for related sequences probably make up the vast bulk of bioinformatics activities.

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