Abstract
Following advances in DNA and protein sequencing, the application of computational approaches in analysing biological data has become a very important aspect of biology. Evaluating similarities between biological sequences is crucial to our understanding of evolutionary biology, and this can be achieved by basic local alignment search tool (BLAST) and fast alignment (FASTA). BLAST and FASTA have become fundamental tools of biology and it is essential to know how they operate, the task they can accomplish and how to accurately interpret their output. This paper provides an analysis of BLAST and FASTA in sequence analysis. Both BLAST and FASTA algorithms are appropriate for determining highly similar sequences. However, BLAST appears to be faster and also more accurate than FASTA. Both BLAST and FASTA are limited in sensitivity and may not be able to capture highly divergent sequences in some cases. Consequently, evolutionarily diverse members of a family of proteins may be missed out in a BLAST or FASTA search. Key words: Bioinformatics, basic local alignment search tool (BLAST), fast alignment (FASTA), sequence alignment, prokaryotes.
Highlights
The term bioinformatics was coined by Paulien Hogeweg of Utrecht University in 1979 for the study of informatic processes in biotic systems, but the field of bioinformatics did not become recognized until the 1990s (Hogeweg, 1978; Luscombe et al, 2001)
This paper provides an analysis of the use of basic local alignment search tool (BLAST) and fast alignment (FASTA) in sequence analysis, and it is targeted at beginners in the field of bioinformatics
FASTP was used for protein similarity searching, its improvement in FASTA empowered it to execute DNA:DNA searches, translated protein:DNA
Summary
The term bioinformatics was coined by Paulien Hogeweg of Utrecht University in 1979 for the study of informatic processes in biotic systems, but the field of bioinformatics did not become recognized until the 1990s (Hogeweg, 1978; Luscombe et al, 2001). Through a BLAST search, one can compare a query sequence with a database of sequences, and thereby identify library sequences that share resemblance with the query sequence above a certain threshold Based on such comparison, BLAST can be used to achieve several objectives including species identification, locating domains, DNA mapping and annotation (Altschul et al., 1990). BLAST identifies homologous sequences by locating short matches between the two sequences being compared This process is referred to as seeding, and it is after this initial match that BLAST begins to make local alignments. The E value is equivalent to the number of sequences occurring in the database, that is expected to match a given query sequence at least as well as the listed sequence does, if the relationship between the sequences was random
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