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Updated: Feb 19, 2026

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K-mer clustering algorithm using a MapReduce framework: application to the parallelization of the Inchworm module of

Chang Sik Kim1,2, Martyn D Winn3, Vipin Sachdeva4,5

  • 1The Hartree Centre and Scientific Computing Department, STFC Daresbury Laboratory, Warrington, WA4 4AD, UK.

BMC Bioinformatics
|November 5, 2017
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We developed a novel k-mer clustering method for de novo transcriptome assembly. This approach reduces computational demands, enabling large dataset analysis on standard compute clusters without specialized hardware.

Keywords:
De novo sequence assemblyMapReduceRNA-SeqTrinity

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • De novo transcriptome assembly is crucial for studying gene expression in non-model organisms.
  • Current de novo assemblers require significant in-memory resources due to whole-dataset k-mer analysis.
  • This limits the analysis of large RNA sequence datasets on standard hardware.

Purpose of the Study:

  • To introduce a novel, scalable approach for de novo transcriptome assembly.
  • To reduce the memory and runtime requirements of existing assembly pipelines.
  • To enable the analysis of large RNA sequencing datasets using distributed computing.

Main Methods:

  • Implemented a k-mer clustering approach as a preliminary step in transcriptome assembly.
  • Utilized the MapReduce framework for parallel processing of large datasets.
  • Integrated the clustering method into the Trinity pipeline using the MPI protocol for distributed computation.

Main Results:

  • Achieved significant reductions in runtime and per-node memory usage.
  • Demonstrated the effectiveness of the clustering approach on real RNA sequencing datasets.
  • Validated the method within the context of the Trinity de novo assembly pipeline.

Conclusions:

  • MapReduce-based k-mer clustering offers a scalable solution for complex sequencing challenges.
  • This method facilitates the distribution of computational tasks without compromising accuracy.
  • The clustering approach is a valuable initial step for various de novo assembly pipelines.