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<p>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 in MCSs into a practical tool called <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13015_2025_271_Article_IEq3.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="62" /> </InlineMediaObject> <EquationSource Format="TEX">\(\textsc {McDag}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="normal">M</mi> <mstyle mathsize="0.6em"> <mi mathvariant="normal">C</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">G</mi> </mstyle> </mrow> </math></EquationSource> </InlineEquation>, the first publicly available tool that can index MCSs of real genomic data, and show that its definition can be generalized to multiple strings. We demonstrate that our tool can index pairs of sequences exceeding 10,000 base pairs within minutes, utilizing only 4-7% more than the minimum required nodes. For three or more sequences, we observe experimentally that the minimum index may exhibit a significant increase in the number of nodes.</p>

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\(\textsc {McDag}\): indexing maximal common subsequences for k strings

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月19日

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

摘要

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 in MCSs into a practical tool called \(\textsc {McDag}\) M C D A G , the first publicly available tool that can index MCSs of real genomic data, and show that its definition can be generalized to multiple strings. We demonstrate that our tool can index pairs of sequences exceeding 10,000 base pairs within minutes, utilizing only 4-7% more than the minimum required nodes. For three or more sequences, we observe experimentally that the minimum index may exhibit a significant increase in the number of nodes.