word, given by the parameter kmer, controls the sensitivity and speed of the program. Increasing the k-mer value decreases number of background hits that are found. From the word hits that are returned the program looks for segments that contain a cluster of nearby hits. It then investigates these segments for a possible match. There are some differences between fastn and fastp relating to the type of sequences used but both use four steps and calculate three scores to describe and format the sequence similarity results. These are: Identify regions of highest density in each sequence comparison. Taking a k-mer to equal 1 or 2. In this step all or a group of the identities between two sequences are found using a look up table. The k-mer value determines how many consecutive identities are required for a match to be declared. Thus the lesser the k-mer value: the more sensitive the search. k-mer=2 is frequently taken by users for protein sequences and kmer=4 or 6 for nucleotide sequences. Short oligonucleotides are usually run with k-mer= 1. The program then finds all similar local regions, represented as diagonals of a certain length in a dot plot, between the two sequences by counting k-mer matches and penalizing for intervening mismatches. This way, local regions of highest density matches in a diagonal are isolated from background hits. For protein sequences BLOSUM50 values are used for scoring k-mer matches. This ensures that groups of identities with high similarity scores contribute more to the local diagonal score than to identities with low similarity scores. Nucleotide sequences use the identity matrix for the same purpose. The best 10 local regions selected from all the diagonals put together are then saved. Rescan the regions taken using the scoring matrices. trimming the ends of the region to include only those contributing to the highest score. Rescan the 10 regions taken. This time use the relevant scoring matrix while rescoring to allow runs of identities shorter than the k-mer value. Also while rescoring conservative replacements that contribute to the similarity score are taken. Though protein sequences use the BLOSUM50 matrix, scoring matrices based on the minimum number of base changes required for a specific replacement, on identities alone, or on an alternative measure of similarity such as PAM, can also be used with the program. For each of the diagonal regions rescanned this way, a subregion with the maximum