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Sarand: exploring antimicrobial resistance gene neighbourhoods in complex metagenomic assembly graphs.

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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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Movi 2: Fast and Space-Efficient Queries on Pangenomes.

Mohsen Zakeri1, Nathaniel K Brown1, Travis Gagie2

  • 1Department of Computer Science, Johns Hopkins University.

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|November 24, 2025
PubMed
Summary

Movi 2 significantly reduces the size and memory footprint of compressed indexes for pangenomics. This advancement improves efficiency for large-scale genomic data analysis, outperforming previous methods.

Keywords:
Burrows-Wheeler Transformindexingmove structurepangenomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Compressed indexing is vital for pangenomics to manage large datasets and minimize reference bias.
  • The original Movi index offered speed and locality but suffered from a high memory footprint.

Purpose of the Study:

  • To introduce Movi 2, a novel compressed index with substantially reduced size and memory requirements.
  • To explore sampling strategies for balancing query speed and space efficiency in Movi 2.

Main Methods:

  • Development of new methods to decrease the size and memory usage of move-structure indexes.
  • Implementation of sampling approaches to allow for adjustable query and space efficiency.

Main Results:

  • The most compressed Movi 2 version achieved over a fivefold reduction in space compared to the original Movi index.
  • Movi 2 demonstrated superior performance in both time and memory footprint against existing methods, including r-index and the previous Movi.
  • Effective application to large pangenome collections, such as the Human Pangenome Reference Consortium (HPRC) releases.

Conclusions:

  • Movi 2 offers significant improvements in space efficiency for compressed indexes in pangenomics.
  • The new methods provide flexible trade-offs between query performance and memory usage.
  • Movi 2 represents a state-of-the-art solution for analyzing large pangenome datasets.