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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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RLBWT-Based LCP Computation in Compressed Space for Terabase-Scale Pangenome Analysis.

Ahsan Sanaullah1, Nathaniel K Brown2, Pramesh Shakya1

  • 1Department of Computer Science, University of Central Florida Orlando, Florida, 32816, United States.

Biorxiv : the Preprint Server for Biology
|February 6, 2026
PubMed
Summary
This summary is machine-generated.

We developed new algorithms for constructing compressed full-text indexes using the run-length Burrows-Wheeler transform (RLBWT). These methods significantly reduce memory usage for large biological datasets, making complex analyses more feasible.

Keywords:
Longest Common PrefixMove Data StructureRun Length BWTTerabase Scale Pangenomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Compression

Background:

  • Lossless full-text indexes are crucial for bioinformatics applications.
  • Increasing biological data necessitates efficient indexing of large datasets.
  • Existing run-length Burrows-Wheeler transform (RLBWT) indexes are computationally expensive to construct.

Purpose of the Study:

  • To present algorithms for constructing RLBWT-based compressed full-text indexes and supporting data structures in compressed space.
  • To reduce the computational cost and memory requirements of building these indexes.

Main Methods:

  • Developed algorithms with O(r) space complexity and O(n) time for repetitive datasets.
  • Introduced the first algorithm to compute LCP-related information in O(r) space and optimal time.
  • Utilized r samples of the inverse suffix array at regular intervals.

Main Results:

  • Achieved O(r) space and O(n) time for constructing RLBWT indexes on repetitive datasets.
  • Reduced peak memory usage by 12.6x on the Human Pangenome Reference Consortium Release 2 dataset (from 2,135 GiB to 170 GiB).
  • Provided the first optimal time and O(r) space algorithm for LCP-related information.

Conclusions:

  • The new algorithms offer significant memory reductions for building compressed full-text indexes.
  • These advancements address the computational challenges of indexing large biological datasets.
  • The implementation is publicly available, facilitating broader adoption in bioinformatics research.