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Related Experiment Video

Updated: Jan 3, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
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Counting Kmers for Biological Sequences at Large Scale.

Jianqiu Ge1, Jintao Meng1, Ning Guo1

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, 518055, China.

Interdisciplinary Sciences, Computational Life Sciences
|November 18, 2019
PubMed
Summary
This summary is machine-generated.

SWAPCounter is a new distributed approach for efficiently counting k-mers in massive biological sequence data. It achieves high scalability and parallel efficiency, outperforming existing tools on large datasets.

Keywords:
Biological sequenceCounting bloom filterKmer countingScalability

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • K-mer abundance counting is crucial for bioinformatics tasks like genome assembly.
  • Sequencing data is growing exponentially, exceeding single-node processing capabilities.
  • Efficient processing of large-scale sequence data on high-performance computing clusters is a challenge.

Purpose of the Study:

  • To develop a highly scalable distributed approach for k-mer counting.
  • To address the computational challenges of processing Petabyte-scale sequence data.
  • To improve memory and communication efficiency in k-mer abundance analysis.

Main Methods:

  • Introduced SWAPCounter, a distributed k-mer counting method.
  • Integrated an MPI streaming I/O module for high-speed data loading.
  • Utilized a counting bloom filter for memory and communication efficiency.
  • Overlapped counting steps to enhance scalability and parallel efficiency.

Main Results:

  • SWAPCounter demonstrates competitive performance against KMC2 and MSPKmerCounter in shared memory environments.
  • Achieved the highest scalability in strong scaling experiments.
  • Scaled to 32,768 cores with 79% parallel efficiency on 4 TB of 1000 Genomes data.

Conclusions:

  • SWAPCounter offers a highly scalable and efficient solution for large-scale k-mer counting.
  • The approach effectively handles massive biological sequence datasets.
  • SWAPCounter provides a valuable tool for modern genomic data analysis.