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Updated: Apr 1, 2026

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Sequence Factorization with Multiple References.

Sebastian Wandelt1, Ulf Leser1

  • 1Knowledge Management in Bioinformatics, Humboldt-University of Berlin, Rudower Chaussee 25, 12489 Berlin, Germany.

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|October 1, 2015
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Summary
This summary is machine-generated.

Multi-reference factorization significantly improves data compression for large sequencing projects, offering better ratios than single-reference methods. This technique optimizes storage and analysis of genomic data by factoring sequence differences against multiple references.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomic Data Analysis

Background:

  • High-throughput sequencing generates massive datasets, posing storage and analysis challenges.
  • Referential compression, storing sequence differences against a reference, offers significant compression ratios for similar sequences like human genomes.
  • Multi-reference compression shows potential for even higher compression ratios but lacks detailed analysis.

Purpose of the Study:

  • To analyze the key technique of sequence factorization for multi-reference compression.
  • To propose optimization heuristics and identify parameters influencing factorization size, time, and memory usage.
  • To evaluate multi-reference factorization performance across different species and configurations.

Main Methods:

  • Developed and analyzed sequence factorization techniques for multi-reference compression.
  • Proposed optimization heuristics and identified critical parameter settings.
  • Evaluated 30 setups with varying numbers of references on data from three species.

Main Results:

  • Factorization sizes ranged from optimal to 300% overhead.
  • Factorization speed varied widely, from 0.01 MB/s to over 600 MB/s.
  • Main memory usage spanned from tens of MB to tens of GB.

Conclusions:

  • Multi-reference factorization offers substantial improvements over single-reference methods.
  • Identified optimal configurations for common use cases based on performance evaluations.
  • Factorization parameters critically impact compression efficiency, speed, and memory requirements.