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We developed a novel indexing framework using tag arrays to efficiently query complex pangenome graphs. This method enables lossless, haplotype-aware searches across large genomes, improving genomic analysis tools.

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Area of Science:

  • Bioinformatics
  • Computational Genomics
  • Data Structures

Background:

  • Pangenome graphs are essential for representing genomic variation across multiple haplotypes.
  • Efficient and lossless indexing of large-scale pangenomic data remains a significant computational challenge.

Purpose of the Study:

  • To present a practical and scalable indexing framework for pangenome graphs.
  • To enable efficient and lossless retrieval of query patterns within complex pangenomic structures.

Main Methods:

  • Developed a tag array indexing framework extending the FM-index with graph coordinates.
  • Introduced a novel construction algorithm combining k-mers, graph extensions, and haplotype traversal.
  • Utilized multi-string Burrows-Wheeler Transform (BWT) and r-index for memory-efficient processing of large genomes.

Main Results:

  • The tag array structure demonstrates effective compression and scalability with increasing haplotypes.
  • Accurate mapping information is preserved across diverse genomic regions.
  • The method enables lossless and haplotype-aware querying in complex pangenomes.

Conclusions:

  • The proposed indexing framework provides a practical solution for querying complex pangenomes.
  • This approach facilitates the development of scalable aligners and graph-based bioinformatics tools.