Feb. 19, 2014, 6:27 p.m. by Rosalind Team
Reads will form a collection of strings Patterns that we wish to match against a reference genome Text. For each string in Patterns, we will first find all its exact matches as a substring of Text (or conclude that it does not appear in Text). When hunting for the cause of a genetic disorder, we can immediately eliminate from consideration areas of the reference genome where exact matches occur. We will later generalize this problem to find approximate matches, where single nucleotide substitutions in reads separate the individual from the reference genome (or represent errors in reads).
Multiple Pattern Matching Problem: Find all occurrences of a collection of patterns in a text.
Input: A string Text and a collection Patterns containing (shorter) strings.
Output: All starting positions in Text where a string from Patterns appears as a substring.
To solve this problem, we will consolidate Patterns into a directed tree called a trie (pronounced “try”), which is written Trie(Patterns) and has the following properties.
The most obvious way to construct Trie(Patterns) is by iteratively adding each string from Patterns to the growing trie, as implemented by the following algorithm.
Construct a trie on a collection of patterns.
Given: A collection of strings Patterns.
Return: The adjacency list corresponding to Trie(Patterns), in the following format. If Trie(Patterns) has n nodes, first label the root with 1 and then label the remaining nodes with the integers 2 through n in any order you like. Each edge of the adjacency list of Trie(Patterns) will be encoded by a triple: the first two members of the triple must be the integers labeling the initial and terminal nodes of the edge, respectively; the third member of the triple must be the symbol labeling the edge.
ATAGA ATC GAT
0->1:A 1->2:T 2->3:A 3->4:G 4->5:A 2->6:C 0->7:G 7->8:A 8->9:T