Sequence-To-Graph Alignment: Difference between revisions

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== Parameters ==  
== Parameters ==  


N: number of vertices in original hypertext graph
$N$: number of vertices in original hypertext graph


E: number of edges in original hypertext graph
$E$: number of edges in original hypertext graph


m: length of pattern
$m$: length of pattern


n: number of vertices in converted graph (total text size)
$n$: number of vertices in converted graph (total text size)


e: number of edges in converted graph
$e$: number of edges in converted graph


== Table of Algorithms ==  
== Table of Algorithms ==  


Currently no algorithms in our database for the given problem.
Currently no algorithms in our database for the given problem.

Latest revision as of 09:24, 10 April 2023

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

Currently no algorithms in our database for the given problem.