FastA - Steps

• Hashing: FastA locates regions of the query sequence and matching regions in the database sequences that have high densities of exact word matches. (without gaps) The length of the matched word is called the ktup parameter.
• Scoring: The ten highest scoring regions are rescored using a scoring matrix. The score for such a pair of regions is saved as the init₁ score.
• Introduction of Gaps: FastA determines if any of the initial regions from different diagonals may be joined together to form an approximate alignment with gaps. Only non-overlapping regions may be joined. The score for the joined regions is the sum of the scores of the initial regions minus a joining penalty for each gap. The score of the highest scoring region, at the end of this step, is saved as the init_n score.
  
• Alignment: After computing the initial scores, FastA determines the best segment of similarity between the query sequence and the search set sequence, using a variation of the Smith-Waterman algorithm. The score for this alignment is the opt score.
• Random Sequence Simulation: In order to evaluate the significance of such alignment FastA empirically estimates the score distribution from the alignment of many random pairs of sequences. More precisely, the characters of the query sequences are reshuffled (to maintain bias due to length and character composition) and searched against a random subset of the database. This empirical distribution is extrapolated, assuming it is an extreme value distribution, and each alignment to the real query is assigned a Z-score and an E-score.

  The Fasta program can be used through an interface in WWW2.EBI.ac.uk/fasta3.[11]