Sequence-To-Graph Alignment (Sequence-to-Graph Alignment)

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Revision as of 10:25, 15 February 2023 by Admin (talk | contribs) (Created page with "{{DISPLAYTITLE:Sequence-To-Graph Alignment (Sequence-to-Graph Alignment)}} == Description == This is pattern matching where you are given a pattern and a hypertext graph. The hypertext model is that the text forms a graph of $N$ nodes and $E$ edges, where a string is stored inside each node, and the edges indicate alternative texts that may follow the current node. The pattern is still a simple string of length $m$. It is also customary to transform this graph into a o...")
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Description

This is pattern matching where you are given a pattern and a hypertext graph. The hypertext model is that the text forms a graph of $N$ nodes and $E$ edges, where a string is stored inside each node, and the edges indicate alternative texts that may follow the current node. The pattern is still a simple string of length $m$. It is also customary to transform this graph into a one-character hypertext, i.e. one where there is exactly one character per node (by converting each node containing a text of length $l$ into a chain of $l$ nodes). This graph has $n$ nodes and $e$ edges (note that $n$ is the text size and $e = n − N + E$).

Additonal notes: (changes are allowed in the query sequence alone) Linear gap penalty?

Parameters

N: number of vertices in original hypertext graph
E: number of edges in original hypertext graph
m: length of pattern
n: number of vertices in converted graph (total text size)
e: number of edges in converted graph

Table of Algorithms

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