Sequence-To-Graph Alignment

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 NN nodes and EE 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 mm. 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 ll into a chain of ll nodes). This graph has nn nodes and ee edges (note that nn is the text size and e=nN+Ee = n − N + E). Additonal notes: (changes are allowed in the query sequence alone) Linear gap penalty

Parameters

  • NN: number of vertices in original hypertext graph
  • EE: number of edges in original hypertext graph
  • mm: length of pattern
  • nn: number of vertices in converted graph (total text size)
  • ee: number of edges in converted graph

Filters

Computational Model

Randomization

Approximation

Algorithms Table

Displaying 6 of 6 algorithms

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Jain, Chang2019O(V+mE)O(V + mE)O(V)O(V)
V-ALIGN2018O(mVE)O(mVE)O(mV)O(mV)
Rautiainen and Marschall2017O(V+mE)O(V + mE)O(Vm)O(V\sqrt{m})
HybridSpades2015O(m(Vlog(mV)+E))O(m(V \log(mV) + E))O(m(V+E))O(m(V+E))
Navarro2000O(m(V+E))O(m(V + E))O(V)O(V)
Amir et al.1997O(m(nlogm+E))O(m(n \log m + E))O(mn)O(mn)

Reductions Table

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Other relevant algorithms

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