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McDag: Indexing Maximal Common Subsequences in Practice

Authors Giovanni Buzzega , Alessio Conte , Roberto Grossi , Giulia Punzi

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Author Details

Giovanni Buzzega
  • University of Pisa, Italy
Alessio Conte
  • University of Pisa, Italy
Roberto Grossi
  • University of Pisa, Italy
Giulia Punzi
  • University of Pisa, Italy

Funding

Giovanni Buzzega, Roberto Grossi, Giulia Punzi: Received funding from the European Union’s Horizon 2020 Research and Innovation Staff Exchange programme under the Marie Skłodowska-Curie grant agreement No. 872539. Giovanni Buzzega, Alessio Conte, Roberto Grossi: Supported, in part, by MUR of Italy, under PRIN Project n. 2022TS4Y3N - EXPAND: scalable algorithms for EXPloratory Analyses of heterogeneous and dynamic Networked Data.
  • Punzi, Giulia: Supported by the Italian Ministry of Research, under the complementary 36 actions to the NRRP "Fit4MedRob - Fit for Medical Robotics" Grant (#PNC0000007).

Acknowledgements

The authors would like to thank the anonymous reviewers for their insightful comments and suggestions.

Cite As Get BibTex

Giovanni Buzzega, Alessio Conte, Roberto Grossi, and Giulia Punzi. McDag: Indexing Maximal Common Subsequences in Practice. In 24th International Workshop on Algorithms in Bioinformatics (WABI 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 312, pp. 21:1-21:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.WABI.2024.21

Abstract

Analyzing and comparing sequences of symbols is among the most fundamental problems in computer science, possibly even more so in bioinformatics. Maximal Common Subsequences (MCSs), i.e., inclusion-maximal sequences of non-contiguous symbols common to two or more strings, have only recently received attention in this area, despite being a basic notion and a natural generalization of more common tools like Longest Common Substrings/Subsequences. In this paper we simplify and engineer recent advancements on MCSs into a practical tool called McDag, the first publicly available tool that can index MCSs of real genomic data. We demonstrate that our tool can index sequences exceeding 10,000 base pairs within minutes, utilizing only 4-7% more than the minimum required nodes, while also extracting relevant insights.

Subject Classification

ACM Subject Classification
  • Theory of computation → Pattern matching
  • Applied computing → Molecular sequence analysis
  • Applied computing → Computational genomics
Keywords
  • Index data structure
  • DAG
  • Common subsequence
  • Inclusion-wise maximality
  • LCS

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References

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