FASTA algorithm

Pearson, W. R. (1991). Searching protein sequence libraries comparison of the sensitivity and the selectivity of the Smith-Waterman and FASTA algorithms. Genomics 11,635-50. 

The FASTA algorithm was originally described by Lipman and Pearson (1985) as a method of locating similar sequences by first identifying short words common to the compared sequences. The words are any short DNA or protein sequence (n-mer or k-tuple) k-tuple sizes of 1 or 2 are used for protein sequences, and up to 6 bases for DNA searches. In the first similarity search, the test sequence (or query) and comparison sequence are lined up, and under each position is recorded the number of elements that the comparison sequence must be moved to the right (+) or left (-) to achieve a match. If the two sequences are very similar in a particular region, then a particular displacement will occur frequently if not, all possible displacements will be found (Wilbur and Lipman 1983). 

All of the three IC centers also provide facilities for sequence similarity search and alignment. The widely used database search algorithms are FASTA (Lipman and Pearson, 1985) at http //www.nbrf.georgetown.edu/pirwww/search/fasta.html and BLAST (Altschul et al., 1990) at http //www.ncbi.nlm.nih.gov/BLAST/. For BLAST... 

The 3D-ID compatibility algorithm (Ito et ah, 1997) is applied to predict the secondary structures by threading at SSThread of DDBJ (http //www.ddbj.nig.ac.jp/ E-mail/ssthread/www service.html). Paste the query sequence (fasta format) into the sequence box, enter your e-mail address, and click the Send button. The e-mail returns the threading result reporting the amino acid sequence with the predicted secondary structures (H for a. helix, E for / strand, and C for coil or other). 

This chapter presents a brief summary of the essentials of statistics that are particularly appropriate for handling biochemical data. This is followed by a section on the quantitative analysis of experimental results which deals chiefly with binding processes and enzyme kinetics. The chapter concludes with a brief discussion of methods of sequence analysis and databases, including a description of the FASTA and Needleman and Wunsch algorithms which form the basis of most of the sequence alignment methods currently in use. 

Many algorithms have been employed for sequence analysis. They can be used for either protein or nucleic add sequences, and many of these were not developed specifically for molecular biological use, but for linguistic applications. It is beyond the scope of this book to discuss these algorithms in any detail the essentials of two of the most important methods, FASTA and Needleman-Wunsch will serve as examples of how algorithms are structured. 

Several implementations of this procedure are available, most prominently the SSEARCH program from the FASTA package [53], There exist implementations of the Smith-Waterman algorithm that are tuned for speed like one using special processor instructions [54] and, among others, one by Barton [55], Depending on implementation, computer, and database size, a search with such a program will take on the order of one minute. 

Most algorithms require sets of sequences in FASTA format. Proteins are usually easily extracted directly from the available sequence databases. Genomic DNA is more problematic, because annotation of genes, promoters, transcriptional start sites, introns, exons, and other important features is not always reliable. Several web servers available to aid in extracting the relevant sequences for discovering regulatory elements in genomic DNA are shown in Table 1. 

The impredict algorithm uses a two-layer, feed-forward neural network to assign the predicted type for each residue (Kneller et al., 1990). In making the predictions, the server uses a FASTA format file with the sequence in either one-letter or three-letter code, as well as the folding class of the protein (a, j8, or a//8). Residues are classified... 

Built on the concept of local alignments, FASTA (Lipman and Pearson, 1985) and BLAST (Altschul et al, 1990) provide increasingly rapid sequence alignments strategies and both can be executed via a personal computer. BLAST uses a heuristic approximation algorithm that gains speed at the expense of accuracy of result. The tool finds short... 

Similarity Algorithm. Available http //fasta.bioch.vitginia.edu/fasta www/lalign.htm. 

Algorithms. Although there are many mechanisms to search biodatabases, the most critical and extensively used is sequence similarity searching. Needleman-Wunsch, Smith-Waterman, FASTA, and BLAST represent the major similarity algorithms. They differ in algorithmic mechanism and computational speed, with Needleman-Wunsch being the most accurate but also the most computationally intense. At the time of its publication in 1970, it took days to return results. BLAST is the least accurate of... 

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