multiple sequence alignment
13 likes2,057 views
The document discusses multiple sequence alignment (MSA), a method used in bioinformatics to arrange DNA, RNA, or protein sequences to identify functional, structural, or evolutionary relationships. It outlines various approaches for MSA, including progressive, iterative, and block-based methods, emphasizing the advantages of MSA over pairwise alignment in revealing biological information. The document also describes scoring functions and computational challenges associated with MSA, as well as its applications in phylogenetic tree construction and protein structure prediction.
multiple sequence alignment
- 1. MULTIPLE SEQUENCE ALINGMENT HARSHITA AGARWAL KUMAR SHASHANK SHAURYA NANCY
- 2. BACKGROUND In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA or protein to identify regions of similarity that may be a consequence of functional, structural or evolutionary relationships between the sequences. One could align these sequences either pair wise against each other or against whole database, or by performing multiple sequence alignment where one align 3-many sequences at a time. Multiple sequence alignment (MSA) helps us to find conserved domain among a whole set of sequences that is difficult to find if pair wise alignment is done. Like pair wise alignment MSA could also be done locally or globally. Global alignment includes gaps while local alignment could avoid these gaps. One could Take MSA an extension of pair wise alignment, as primary step of MSA is pair wise alignment between all the possible pairs followed by formation of a guided tree ultimately providing the final MSA report. There are various ways of doing MSA: Progressive, Iterative, and Block based method. These methods are explained in following text. There are various statistical model of MSA which provides us a table or file containing information regarding probability of amino acid or nucleotide at each position of MSA file. These models help us to correlate aligned sequences and study insertions and deletions more easily and efficiently. In our present study we have explained various ways of performing MSA and some statistical model of MSA (PSSM, Profile, and PSI- BLAST).
- 3. Multiple Sequence Alignment A natural extension of pairwise alignment is multiple sequence alignment, which is to align multiple related sequences to achieve optimal matching of the sequences. There is a unique advantage of multiple sequence alignment because it reveals more biological information than many pairwise alignments can. SCORING FUNCTION The scoring function for multiple sequence alignment is based on the concept of sum of pairs (SP). As the name suggests, it is the sum of the scores of all possible pairs of sequences in a multiple alignment based on a particular scoring matrix. In calculating the SP scores, each column is scored by summing the scores for all possible pairwise matches, mismatches and gap costs. The score of the entire alignment is the sum of all of the column scores . Multiple sequence alignment heurestic progressive itertative block based method exhaustive
- 4. 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. This means to establish a multidimensional search matrix. For aligning N sequences, an N-dimensional matrix is needed to be filled with alignment scores. As the amount of computational time and memory space required increases exponentially with the number of sequences, it makes the method computationally prohibitive to use for a large data set. For this reason, full dynamic programming is limited to small datasets of less than ten short sequences. HEURISTIC ALGORITHMS Because 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: progressive alignment type iterative alignment type and block-based alignment type. Progressive Alignment Method Progressive alignment depends on the stepwise assembly of multiple alignment and is heuristic in nature . it does not seprate the process of scoring an alignment from the optimization algorithm. It is fast and
- 5. efficient and does not directly optimize any global scoring function of alignment correctness. Steps: 1. perform pairwise alignment for all sequences 2. use the alignment score, that gives a phylogenetic tree using neighbor-joining(NJ) method. 3. The sequences are aligned using the phylogenetic relationships indicated by the tree. CLUSTALW produces the best match for the sequences and arranges them so that the similarities and differences can be seen. It works on the hypothesis that sequence in an alignment will reflect their evolutionary history.
- 6. Iterative Alignment The iterative approach is based on the idea that an optimal solution can be found by repeatedly modifying existing suboptimal solutions. The procedure starts by producing a low-quality alignment and gradually improves it by iterative realignment through well-defined procedures until no more improvements in the alignment scores. Steps remain similar to progressive alignment, only pre-alignment is done multiple times. Software used: T-coffee Block-Based Alignment The progressive and iterative alignment strategies are largely global alignment based and may therefore fail to recognize conserved domains and motifs among highly divergent sequences of varying lengths. For such divergent sequences that share only regional similarities, a local alignment based approach has to be used. The strategy identifies a block of ungapped alignment shared by all the sequences, hence, the block- based local alignment strategy is used.
- 7. APPLICATION OF M.S.A phylogenetic tree construction illumination of functionally important regions prediction of structure of proteins
- 8. REFERENCES S.C. Rastogi and N. Mendiratla and P.Rastogi. Bioinformatics methods and applicationsGenomics, Proteomics and Drug Discovery. Prentice Hall India, 2004. Multiple sequence alignment Introduction to Computational Biology Teresa Przytycka, PhD, https://www.ncbi.nlm.nih.gov https://www.ebi.ac.uk/Tools/msa/clustal http://www.iiserpune.ac.in/~farhat/wordpress/wpcontent/uploads/2 011/06/lecture_MSA.pdf