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Background <p>Precision medicine pipelines typically begin with variant calling to identify disease-related mutations for optimal treatment selection. Reference-free approaches assess variations in the genetic profiles of distinct individuals through the utilization of a <i>De Bruijn</i> graph. However, the timely analysis of large-scale sequencing data may be beyond the capabilities of single workstations, requiring alternative computational approaches.</p> Results <p>We introduce the first-known distributed pipeline for detecting isolated SNPs (Single Nucleotide Polymorphisms), by leveraging the computational resources of multiple machines in parallel. Our pipeline efficiently analyzes large datasets thanks to the usage of a distributed <i>De Bruijn</i> graph representation. Furthermore, we introduce a cluster-driven algorithm to partition the <i>De Bruijn</i> graph across multiple independent machines according to the inner structure of the sequences under analysis, thus further improving the scalability of our pipeline.</p> Conclusions <p>The results of our experiments, conducted on real-world datasets, show the good performance of our pipeline in terms of efficiency, output quality and scalability. Moreover, the reported results also confirm that the adoption of a specialized partitioning algorithm for the distributed representation of the <i>De Bruijn</i> graph leads to a relevant performance speed-up compared to using standard partitioning techniques.</p>

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A scalable distributed pipeline for reference-free variants calling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月3日

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  • Lorenzo Di Rocco,
  • Umberto Ferraro Petrillo

摘要

Background

Precision medicine pipelines typically begin with variant calling to identify disease-related mutations for optimal treatment selection. Reference-free approaches assess variations in the genetic profiles of distinct individuals through the utilization of a De Bruijn graph. However, the timely analysis of large-scale sequencing data may be beyond the capabilities of single workstations, requiring alternative computational approaches.

Results

We introduce the first-known distributed pipeline for detecting isolated SNPs (Single Nucleotide Polymorphisms), by leveraging the computational resources of multiple machines in parallel. Our pipeline efficiently analyzes large datasets thanks to the usage of a distributed De Bruijn graph representation. Furthermore, we introduce a cluster-driven algorithm to partition the De Bruijn graph across multiple independent machines according to the inner structure of the sequences under analysis, thus further improving the scalability of our pipeline.

Conclusions

The results of our experiments, conducted on real-world datasets, show the good performance of our pipeline in terms of efficiency, output quality and scalability. Moreover, the reported results also confirm that the adoption of a specialized partitioning algorithm for the distributed representation of the De Bruijn graph leads to a relevant performance speed-up compared to using standard partitioning techniques.