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SplitMEM: a graphical algorithm for pan-genome analysis with suffix skips.

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  • 1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA and Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA.

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A new algorithm, splitMEM, efficiently constructs compressed de Bruijn graphs for population genomics. This enables detailed analysis of bacterial pan-genomes, revealing core-genome properties.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomics is shifting towards pan-genome analysis, representing multiple individuals.
  • Compressed de Bruijn graphs are advanced data structures for population genomics.
  • These graphs encode genomic variations beyond linear sequence limitations.

Purpose of the Study:

  • To explore topological relationships between suffix trees and compressed de Bruijn graphs.
  • To introduce an efficient algorithm for constructing compressed de Bruijn graphs.
  • To demonstrate the algorithm's utility in analyzing bacterial pan-genomes.

Main Methods:

  • Developed the splitMEM algorithm for direct compressed de Bruijn graph construction.
  • Utilized suffix skips for efficient traversal of suffix links.
  • Decomposed maximal exact matches into graph nodes.

Main Results:

  • splitMEM constructs compressed de Bruijn graphs in linear time and space relative to genome count.
  • Successfully analyzed the nine-strain Bacillus anthracis pan-genome.
  • Analyzed up to 62 strains of Escherichia coli, revealing core-genome properties.

Conclusions:

  • splitMEM provides an efficient method for pan-genome representation.
  • The algorithm facilitates the discovery of core-genome properties in bacterial populations.
  • This approach advances the capabilities of population genomics analysis.