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  "path": "/abs/2602.05186v1",
  "publishedAt": "2026-02-06T01:00:00.000Z",
  "site": "https://arxiv.org",
  "tags": [
    "Xingfu Li"
  ],
  "textContent": "**Authors:** Xingfu Li\n\nSequence alignment is a cornerstone technique in computational biology for assessing similarities and differences among biological sequences. A key variant, sequence-to-graph alignment, plays a crucial role in effectively capturing genetic variations. In this work, we introduce two novel formulations within this framework: the Gap-Sensitive Co-Linear Chaining (Gap-CLC) problem and the Co-Linear Chaining with Errors based on Edit Distance (Edit-CLC) problem, and we investigate their computational complexity. We show that solving the Gap-CLC problem in sub-quadratic time is highly unlikely unless the Strong Exponential Time Hypothesis (SETH) fails -- even when restricted to binary alphabets. Furthermore, we establish that the Edit-CLC problem is NP-hard in the presence of errors within the graph. These findings emphasize that incorporating co-linear structures into sequence-to-graph alignment models fails to reduce computational complexity, highlighting that these models remain at least as computationally challenging to solve as those lacking such prior information.",
  "title": "Challenges in Solving Sequence-to-Graph Alignment with Co-Linear Structure"
}