Sequence Similarity Searches Discussion Work

difficulty arises to align long, highly variable sequences by humans. This was when various computational tools and databases came into the picture with different features.

• Global alignment and local alignment were the two types of approaches done by the computational tools to find the similarities between sequences.

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• Global alignment: In this type, the entire query sequence is screened and identified with the known sequences to find similarities. The Needleman-Wunsch algorithm is used to find the sequence similarities using the dynamic programming method.

• Local alignments: In this type, among the entire query sequence only the matching sequences between the query and the known sequence are identified. Local alignments also work on the dynamic programming method but employ the Smith-Waterman algorithm for alignment.

• In some cases when a sequence upstream part gets overlapped by the downstream of another sequence neither local nor global alignments methods can be used to find the sequence similarities, this is when the method called “glocal” is used. It is a combination of local and global, where the two query sequences are aligned partially to find similarities.

Types of alignments and interpretation:
To find similarities between sequences the query or the unknown sequence should be aligned with the known or unknown sequences. This type of aligning can be done in two types: pairwise alignment and multiple sequence alignment.

i. Pairwise alignment: In this type, similarities between two query sequences are found by either locally or globally aligning them. Not more than two sequences are used at a given time. This type of similarity sequencing is considered to be more efficient since the search of known high sequence similarity containing databases need not be searched. Word method, dot-matrix and dynamic programming are the three methods to do the pairwise alignment.

ii. Multiple sequence alignment: Using this alignment, at a time more than one query sequence is aligned to find the similarities. This type of sequencing is done to find the conserved regions of a sequence with which the phylogenetic relationship between the sequences is found. Motif finding, progressive methods, and iterative methods are the different types to perform multiple sequence alignment.

iii. Organisms that have the same evolutionary ancestors mostly exhibit similar nucleotide sequences. Thus, during interpreting sequences that belong to the common ancestors, point mutations give rise to the mismatches in them. While deletion or insertion of nucleotides gives rise to gaps which are said to be indels.

Bioinformatics tools used in sequence similarity searches:
FAST A: the tool FAST A based on heuristics is used to find similarities between the given sequence by following the local alignment method. Similarities between the whole genome shotgun, proteins, genomes, and nucleotides are found using FAST A.

• Smith-Waterman algorithm (S)-SEARCH: based on the Smith-Waterman algorithm similarities between sequences are locally searched by the SSEARCH tool. Similarities between the whole genome shotgun, proteins, genomes, and nucleotides are found using blast.

• Position-Specific Iterative Basic Local Alignment Search Tool (PSI)-Search: this tool works as a combination of SSEARCH and PSI-BLAST to find amino acid sequence similarities between proteins.

• Global-global (GG)-SEARCH: based on the Needleman-Wunsch algorithm this tool does a global search between the sequences. Similarities between nucleotides and proteins are only predicted.

• Global-local (GL)-SEARCH: In this tool, the database does a local search through the query sequence. Similarities between nucleotides and proteins are only predicted.

• National Centre for Biotechnology Information (NCBI) BLAST: works based on heuristics do local searches on the given alignments. It is the largest and the commonly used sequence similarity search tool. Similarities between nucleotides and proteins are predicted.

• PSI-BLAST: This tool is used only to find the similarity between proteins, where the evolutionary relationship of the query sequence is also found.


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