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Related Experiment Video

Updated: Aug 29, 2025

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications
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A fast and efficient path elimination algorithm for large-scale multiple common longest sequence problems.

Changyong Yu1, Pengxi Lin1, Yuhai Zhao2

  • 1College of Computer Science and Engineering, Northeastern University, Shenyang, China.

BMC Bioinformatics
|September 7, 2022
PubMed
Summary

Solving the Longest Common Subsequence (LCS) of Multiple sequences (MLCS) is challenging for large datasets. A new mini Directed Acyclic Graph (DAG) model and Path Elimination Algorithm efficiently handle large-scale MLCS problems, outperforming existing methods.

Keywords:
Mini-MLCSMultiple longest common subsequences (MLCS)The branch and bound

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Sequence Analysis

Background:

  • The Longest Common Subsequence (LCS) of Multiple sequences (MLCS) problem is computationally intensive.
  • Existing algorithms for MLCS require constructing large Directed Acyclic Graphs (DAGs), leading to significant memory and time consumption.
  • These limitations make current methods impractical for lengthy and large-scale sequence datasets.

Purpose of the Study:

  • To develop an efficient method for solving the MLCS problem, particularly for large-scale sequence data.
  • To overcome the memory and time bottlenecks associated with traditional DAG construction in MLCS algorithms.

Main Methods:

  • Introduction of a mini Directed Acyclic Graph (mini-DAG) model.
  • Development of a novel Path Elimination Algorithm integrated with the mini-DAG approach.
  • Utilizing a branch and bound strategy during DAG construction to prune unnecessary paths.

Main Results:

  • The proposed mini-DAG model significantly reduces the size of the graph compared to traditional DAGs.
  • The Path Elimination Algorithm, combined with mini-DAG, enhances computational efficiency by saving memory and search time.
  • The approach effectively addresses the challenges of large-scale MLCS problems.

Conclusions:

  • The novel mini-DAG model and Path Elimination Algorithm provide an efficient solution for large-scale MLCS problems.
  • Empirical results on DNA sequence benchmarks demonstrate superior performance compared to leading algorithms.
  • This advancement is particularly impactful for applications involving extensive biological sequence analysis.