Lec 4-multiple sequence alignment.pptx..

Lec 4-multiple sequence alignment.pptx..

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  ZIAUDDIN UNIVERSITY Faculty OfEngineering Science, Technology & Management (ZUFESTM) Department Of Biomedical Engineering Dr. Syeda Bushra Zafar Assistant Professor Bioinformatics Multiple Sequence Alignment
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  Multiple sequence alignment •Multiple sequence alignment is to align multiple related sequences to achieve optimal matching of the sequences. • There are unique advantage of multiple sequence alignment: Reveals biological information Carry out phylogenetic analysis Designing of degenerate polymerase chain reaction primers
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  Scoring Function • Multiplesequence alignment is to arrange sequences in such a way that a maximum number of residues from each sequence are matched up according to a particular scoring function. • The scoring function for multiple sequence alignment is based on the concept of sum of pairs (SP).
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  Approaches used inmultiple sequence alignment • Exhaustive algorithm • Heuristic algorithm
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  Exhaustive algorithm • Theexhaustive alignment method involves examining all possible aligned positions simultaneously • This approach is similar to dynamic programming, but dynamic programming is limited to small datasets of less than ten short sequences. • A program called, divide and conquer alignment (DCA) which uses some exhaustive components is used. • Divide and conquer alignment: • It is a semi exhaustive approach • It works by breaking sequences into small fragments • This algorithm provides an option of using a more heuristic procedure (fastDCA) to choose optimal cutting points so it can more rapidly handle a greater number of sequences. • The resulting short alignments are joined together head to tail • When the length of sequences reach a predefined threshold then dynamic programming is applied for alignment. • It performs global alignment and requires the input sequences to be of similar lengths and domain structures.
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  Heuristic algorithm • Dynamicprogramming is not feasible for routine multiple sequence alignment, a faster heuristic algorithm has been developed. • Categories of heuristic algorithm: 1. Progressive alignment type 2. Iterative alignment type 3. Block-based alignment type
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  Progressive Alignment Method •It first conducts pairwise alignments for each possible pair of sequences and generate score • The scores are then converted into evolutionary distances to generate a distance matrix for all the sequences involved • A phylogenetic tree is generated using the neighbor-joining method • According to the guide tree, the two most closely related sequences are first re-aligned • Two already aligned sequences are converted to a consensus sequence with gap positions fixed. The consensus is then treated as a single sequence in the subsequent step. • The next closest sequence based on the guide tree is aligned with the consensus sequence using dynamic programming.
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  Clustal • Probably themost well-known progressive alignment program is Clustal • Clustal is a progressive multiple alignment program available either as a stand- alone or on-line program. • The stand-alone program, which runs on UNIX and Macintosh, has two variants, Clustal W and Clustal X. • One of the most important features of this program is the flexibility of using substitution matrices. • For closely related sequences BLOSUM62 or PAM120 matrix are used, whereas, for more divergent sequences BLOSUM45 or PAM250 matrices may be used. • Use of adjustable gap penalties that allow more insertions and deletions in regions that are outside the conserved domains, but fewer in conserved regions
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  • The programalso applies a weighting scheme to increase the reliability of aligning divergent sequences Drawbacks and Solutions It is unsuitable for comparing sequences of different lengths. Accuracy is compromised. • Another major limitation is the “greedy” nature of the algorithm. The final alignment could be far from optimal
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  T-Coffee (Tree-based ConsistencyObjective Function for alignment Evaluation) • T-Coffee performs both global and local pairwise alignment for all possible pairs involved The global pairwise alignment is performed using the Clustal program. The local pairwise alignment is generated by the Lalign program, from which the top ten scored alignments are selected Both global and local alignments are pooled Consistency of alignment is evaluated and a score is generated. Each pairwise alignment is further aligned with a possible third sequence. The result is used to refine the original pairwise alignment based on a consistency criterion in a process known as library extension. Distance matrix is built to derive a guide tree Multiple sequence alignment performed using progressive approach.
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  • T-Coffee avoidsthe problem of getting stuck in the suboptimal alignment regions, because an optimal initial alignment is chosen from many alternative alignments • T-Coffee indeed outperform Clustal however, it is slower than Clustal because of the time required for calculation of consistency score. • T-Coffee provides a graphical output of the alignment results.
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  Iterative alignment • Theiterative approach is based on the idea that an optimal solution can be found by repeatedly modifying existing suboptimal solutions. • The order of the sequences used for alignment is different in each iteration • This method may alleviate the “greedy” problem of the progressive strategy, because order is change everytime. • This method does not have guarantees for finding the optimal alignment as it is also heuristic in nature
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  PRRN • Uses adouble nested iterative strategy for multiple alignment. It performs multiple alignment through two sets of iterations: inner iteration and outer iteration • In the outer iteration, an initial random alignment is generated that is used to derive a UPGMA tree • In the inner iteration, first the sequences are divided randomly into two groups • The two groups, each treated as a single sequence, are then aligned to each other using global dynamic programming • The process is repeated through many cycles until the total SP score no longer increases • Tree is generated • New weights are applied to optimize alignment scores. The newly optimized alignment is subject to further realignment in the inner iteration. • This process is repeated over many cycles until there is no further improvement in the overall alignment scores
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  Block-Based Alignment • Itis a local alignment based strategy that identifies a block of ungapped alignment shared by all the sequences. DIALIGIN 2 • The method breaks each of the sequences down to smaller segments and performs all possible pairwise alignments between the segments. • High-scoring segments, called blocks, among different sequences are then compiled in a progressive manner to assemble a full multiple alignment • It places emphasis on block-to-block comparison rather than residue-to-residue comparison
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  Match-Box • The programcompares segments of every nine residues of all possible pairwise alignments. • If the similarity of particular segments is above a certain threshold across all sequences, they are used as an anchor to assemble multiple alignments