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Updated: Jan 7, 2026

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deMEM: a novel divide-and-conquer framework based on de Bruijn graph for scalable multiple sequence alignment.

Yanming Wei1,2, Zhaoyang Huang1, Pinglu Zhang2,3

  • 1School of Computer Science and Technology, Xidian University, No. 266, Xinglong Section of Xifeng Road, Chang'an Zone, Xi'an, 710126, China.

Gigascience
|January 5, 2026
PubMed
Summary
This summary is machine-generated.

deMEM is a new framework that helps align extremely large DNA sequences, overcoming limitations in comparative genomics. This advance allows tools like MAFFT to process massive datasets, improving genomic analysis accuracy.

Keywords:
de Bruijn graphmaximum exact matchmultiple sequence alignmentparallel algorithm design

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Area of Science:

  • Comparative genomics
  • Bioinformatics
  • Computational biology

Background:

  • Multiple sequence alignment (MSA) is critical for comparative genomics.
  • Large-scale sequence alignment presents significant computational challenges.
  • Alignment quality directly impacts downstream genomic analyses.

Purpose of the Study:

  • To introduce deMEM, a novel framework for DNA multiple sequence alignment.
  • To enable existing MSA tools to handle extremely large and numerous sequences.
  • To improve the scalability and efficiency of large-scale genomic data analysis.

Main Methods:

  • deMEM utilizes a three-stage alignment process.
  • Maximum Exact Matches are represented using de Bruijn graphs and clustered.
  • A divide-and-conquer strategy and profile-profile alignment are employed.

Main Results:

  • deMEM effectively enables existing MSA methods like MAFFT to align extremely large datasets.
  • The framework successfully aligned a dataset of a thousand monkeypox virus genomes.
  • Long sequences previously unalignable are now manageable.

Conclusions:

  • deMEM significantly enhances the capability of current MSA tools for large-scale genomic challenges.
  • The framework facilitates the analysis of massive sequence datasets, including viral genomes.
  • deMEM is freely available, promoting wider adoption in bioinformatics research.