Schmidt [ 5] used weighted grid digraphs for finding all non-overlapping pairs of substrings (not necessarily consecutive) with the highest scores in a given string of length n.
Basically an n-gram profile is constructed by counting the occurrences of sequences of n characters in a given string (e. g. the string "This_is" yields the following bigram (2-gram) profile: "Th":1, "hi": 1, "is":2, "s_": 1, "_i":1).
The String Barcoding (SBC) problem, introduced by Rash and Gusfield (RECOMB, 2002), consists in finding a minimum set of substrings that can be used to distinguish between all members of a set of given strings.
We propose a simple algorithm for computing the longest common subsequence of two given strings X and Y in O kl+min{p1,p2}) time, where p1 and p2 denote the numbers of elements in the bottom and right boundaries of the matched blocks, respectively.
Given a set of n strings of length L and a radius d, the closest string problem (CSP for short) asks for a string tsol that is within a Hamming distance of d to each of the given strings.
In case users do not have any prior information such as the sequence of interest or CIDs, the keyword tool allows a search of all potential chickpea IDs that contain a given input string of text in their annotation.
Faldum and Pommerening have further shown that in a given PID string, up to two errors can be detected and one erroneous character as well as the transposition of two adjacent characters can be corrected.
The expected approximation ratio of our algorithm is 1 - 2 log n n - 1 for n ≥ 6, where n is the total number of H's in a given HP string.
Our algorithm is polynomial and the expected approximation ratio is 1 - 2 log n n - 1 for n ≥ 6 where n is total number of H in a given HP string.
For our algorithm we do a probabilistic analysis and deduce that the expected approximation ratio of our algorithm is 1 - 2 log n n - 1 for n ≥ 6, where n is the total number of H in a given HP string.
