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: indexing maximal common subsequences for k strings.

Giovanni Buzzega1, Alessio Conte2, Roberto Grossi2

  • 1Dipartimento di Informatica, Università di Pisa, Largo Pontecorvo 3, 56127, Pisa, Italy. giovanni.buzzega@phd.unipi.it.

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Summary
This summary is machine-generated.

This study introduces a new tool for indexing Maximal Common Subsequences (MCSs) in genomic data. The tool efficiently handles large sequences, offering a practical solution for bioinformatics sequence analysis.

Keywords:
Common subsequencesDirected acyclic graphInclusion maximalityIndex data structure

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Computer Science

Background:

  • Sequence analysis is fundamental in computer science and bioinformatics.
  • Maximal Common Subsequences (MCSs) are inclusion-maximal common subsequences, a generalization of common substrings/subsequences.
  • MCSs have received limited attention despite their fundamental nature.

Purpose of the Study:

  • To engineer recent advancements in MCSs into a practical, publicly available tool.
  • To demonstrate the tool's capability to index MCSs in real genomic data.
  • To generalize MCS indexing to multiple strings.

Main Methods:

  • Development of a practical tool, , for indexing Maximal Common Subsequences.
  • Engineering and simplification of recent MCS algorithms.
  • Experimental evaluation on real genomic data pairs and triplets.

Main Results:

  • The tool successfully indexes pairs of sequences exceeding 10,000 base pairs within minutes.
  • The indexing process utilizes only 4-7% more than the minimum required nodes for pairs.
  • Indexing for three or more sequences shows a significant increase in the number of nodes.

Conclusions:

  • The developed tool is the first to publicly index MCSs of genomic data.
  • The tool provides an efficient solution for analyzing large genomic sequences.
  • Generalizing MCS indexing to multiple strings presents computational challenges regarding index size.