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Related Experiment Video

Updated: Feb 5, 2026

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Sequence Alignment on Directed Graphs.

Vaddadi Naga Sai Kavya1, Kshitij Tayal1, Rajgopal Srinivasan1

  • 1TCS Research, Hyderabad, India.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|September 12, 2018
PubMed
Summary

V-ALIGN aligns sequences directly on genome variation graphs, avoiding costly graph transformations. This novel dynamic programming approach improves efficiency for sequence alignment on complex genomic data.

Keywords:
V-ALIGNgenome variation graphspangenomesequence alignment

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Genomic variations are represented as genome variation graphs, which are directed graphs that may contain cycles.
  • Existing sequence alignment algorithms on graphs rely on partial order alignment (POA) techniques that require converting graphs to directed acyclic graphs (DAGs).
  • This conversion process (DAGification) involves expensive loop unrolling and can lead to significant graph size inflation.

Purpose of the Study:

  • To introduce a novel algorithm, V-ALIGN, for direct sequence alignment on genome variation graphs.
  • To bypass the computationally expensive DAGification step required by existing methods.
  • To enable efficient gapped alignment directly on cyclic graphs.

Main Methods:

  • Developed V-ALIGN, a novel dynamic programming (DP) formulation for gapped sequence alignment directly on genome variation graphs.
  • The algorithm supports both affine and linear gap penalties.
  • Refinements were proposed to improve practical performance, achieving linear time complexity with respect to sequence size, graph size, and feedback vertex set size.

Main Results:

  • V-ALIGN successfully aligns sequences directly on input graphs, eliminating the need for DAGification.
  • Experimental comparisons showed competitive performance against existing POA-based techniques.
  • Performance of V-ALIGN on filtered subgraphs for short sequence alignment depends on subgraph size.

Conclusions:

  • V-ALIGN offers a more efficient alternative for sequence alignment on genome variation graphs.
  • The algorithm's direct approach on cyclic graphs avoids the overhead and potential blow-up associated with DAGification.
  • V-ALIGN provides a valuable tool for analyzing genomic variations represented as complex graphs.