PRESENTATION MULTIPLE SEQUENCE ALIGNMENT.pptx

PRESENTATION MULTIPLE SEQUENCE ALIGNMENT.pptx

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  SCORING FUNCTION Multiple sequencealignment 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) In calculating the SP scores, each column is scored by summing the scores for all possible pairwise matches, mismatches and gap costs. SEQUENCE 1 G K N SEQUENCE 2 T R N SEQUENCE 3 S H E SUM OF PAIRS -2 +1 +6 = 5
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  The scoring isbased on the BLOSUM62 matrix . The total score for the alignment is 5, which means that the alignment is 25 = 32 times more likely to occur among homologous sequences than by random chance. EXHAUSTIVE ALGORITHMS The exhaustive alignment method involves examining all possible aligned positions simultaneously. Similar to dynamic programming in pairwise alignment, which involves the use of a two-dimensional matrix to search for an optimal alignment, to use dynamic programming for multiple sequence alignment,  extra dimensions are needed to take all possible ways of sequence matching into consideration. DCA (Divide-and-Conquer Alignment, http://bibiserv.techfak.uni- bielefeld.de/ dca/) is a web-based program that is in fact semiexhaustive because certain steps of computation are reduced to heuristics(LEARN NEW SOMETHING )
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  can handle onlydatasets of a very limited number of sequences. HEURISTIC ALGORITHMS • The use of dynamic programming is not feasible for routine multiple sequence alignment, faster and heuristic algorithms have been developed. The heuristic algorithms fall into three categories: • 1. Progressive alignment type • 2. Iterative alignment type, • 3. Block-based alignment type. Progressive Alignment Method: Progressive alignment depends on the stepwise assembly of multiple alignment and is heuristic in nature It first conducts pairwise alignments for each possible pair of sequences using the Needleman–Wunsch global alignment method and records these similarity scores from the pairwise comparisons
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  The scores caneither be percent identity or similarity scores based on a particular substitution matrix Probably the most well-known progressive alignment program is Clustal. Some of its important features are introduced next. Clustal (www.ebi.ac.uk/clustalw/) 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, ClustalW and ClustalX.
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  HEURISTIC ALGORITHMS Clustal doesnot rely on a single substitution matrix. Instead, it applies different scoring matrices when aligning sequences, depending on degrees of similarity For example, for closely related sequences that are aligned in the initial steps, Clustal automatically uses the BLOSUM62 or PAM120 matrix. When more divergent sequences are aligned in later steps of the progressive alignment, the BLOSUM45 or PAM250 matrices may be used instead. Another feature of Clustal is the use of adjustable gap penalties that allow more insertions and deletions in regions that are outside the conserved domains, but fewer in conserved regions. The program also applies a weighting scheme to increase the reliability of aligning divergent sequences (sequences with less than 25% identity) Drawbacks and Solutions
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  T-Coffee (Tree-based ConsistencyObjective Function for alignment Evaluation; www.ch.embnet.org/software/TCoffee.html) performs progressive sequence alignments as in Clustal. Because an optimal initial alignment is chosen from many alternative alignments, T-Coffee avoids the problem of getting stuck in the suboptimal alignment regions, which minimizes errors in the early stages of alignment assembly. Poa (Partial order alignments, www.bioinformatics.ucla.edu/poa/) is a progressive alignment program that does not rely on guide trees. Instead, the multiple alignment is assembled by adding sequences in the order they are given
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  The procedure startsby producing a low-quality alignment and gradually improves it by iterative realignment through well-defined procedures until no more improvements in the alignment scores can be achieved
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  Block-Based Alignment • Theprogressive and iterative alignment strategies are largely global alignment based and may therefore fail to recognize conserved domains and motifs • The strategy identifies a block of ungapped alignment shared by all the sequences, hence, the block-based local alignment strategy. Two block-based alignment programs DIALIGN2 (http://bioweb.pasteur.fr/seqanal/interfaces/dialign2.html) is a webbased program 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, •Match-Box • (www.sciences.fundp.ac.be/biologie/bms/matchbox submit.shtml) is a web-based server
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  • 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; residues between blocks are unaligned. Protein-Coding DNA Sequences 1. The process of achieving maximum sequence similarity at the DNA level, mismatches of genetic codons occur that violate the accepted evolutionary scenario that insertions or deletions occur in units of codons. 2. E.G errors can occur when two sequences are being compared at the protein and DNA levels Editing No matter how good an alignment program seems, the automated alignment often contains misaligned regions This involves introducing or removing gaps to maximize biologically meaningful matches. Sometimes, portions that are ambiguously aligned and deemed to be incorrect have to deleted. In manual editing, empirical evidence or mere experience is needed to make corrections on an alignment.
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  THANK YOU