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Gene Duplication and Divergence02:37

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Updated: Feb 28, 2026

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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Adaptive Tracepoints for Pangenome Alignment Compression.

Hasitha Kaushan1, Santiago Marco-Sola2,3, Erik Garrison4

  • 1Department of Electrical Engineering, University of Moratuwa, Bandaranayake Mawatha, Moratuwa 10400, Sri Lanka.

Biorxiv : the Preprint Server for Biology
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

Adaptive tracepoints offer superior compression for genomic sequence alignments by intelligently segmenting data based on complexity. This method significantly improves storage efficiency for large-scale genomic comparisons compared to traditional fixed-size encodings.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Storing large-scale genomic comparison data requires efficient compression.
  • Fixed-size alignment encodings lack adaptability to variable sequence complexity, limiting compression.
  • Conserved regions in genomic sequences present missed compression opportunities.

Purpose of the Study:

  • To develop a complexity-aware alignment encoding method.
  • To improve compression efficiency for sequence alignments.
  • To ensure alignment reconstruction maintains or improves alignment scores.

Main Methods:

  • Introduced adaptive tracepoints, an alignment encoding method.
  • Segmented alignments using configurable complexity metrics like edit distance or diagonal distance.
  • Bounded segments by differences or deviation from the main diagonal to adapt to local characteristics.

Main Results:

  • Diagonal-bounded tracepoints achieved 10.5-13.7x better compression on simulated long sequences.
  • Edit-bounded tracepoints offered tunable trade-offs between compression and reconstruction cost.
  • Real pangenome data showed 23-139x compression with no score degradation and linear reconstruction time.

Conclusions:

  • Adaptive tracepoints provide significant compression gains for genomic sequence alignments.
  • The method adapts to local alignment characteristics, outperforming fixed-size methods.
  • The approach is validated on simulated and real pangenome data, ensuring efficiency and accuracy.