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Merging of multi-string BWTs with applications.

James Holt1, Leonard McMillan1

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We developed a new algorithm to merge multiple compressed genomic datasets using the Burrows-Wheeler Transform (BWT). This method enhances data compression and enables efficient searching and reconstruction of sequence data for bioinformatics.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Genomic sequencing throughput outpaces analysis capabilities, creating large raw data archives.
  • Raw sequence data requires efficient compression and retrieval methods.
  • The multi-string Burrows-Wheeler Transform (BWT) offers compression and search functionalities for genomic datasets.

Purpose of the Study:

  • To propose a novel method for merging multiple multi-string BWT datasets.
  • To achieve improved data compression ratios.
  • To facilitate downstream bioinformatics analyses on combined datasets.

Main Methods:

  • Development of a novel algorithm for merging multi-string BWTs.
  • Analysis of the time and space complexity of the merging algorithm.
  • Evaluation of the compressibility of the merged BWT.

Main Results:

  • A new algorithm merges F multi-string BWTs in O(N + LCS) time and O(N * F) bits.
  • The merged multi-string BWT demonstrates superior compressibility compared to individual BWTs.
  • Exploration of the utility of the merged BWT for bioinformatics applications.

Conclusions:

  • The proposed merging algorithm offers an efficient solution for managing large genomic datasets.
  • Merging BWTs improves compression and facilitates data accessibility for analysis.
  • This approach supports the growing need for scalable genomic data management and analysis.