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Related Experiment Video

Updated: Aug 17, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Cloud-native distributed genomic pileup operations.

Marek Wiewiórka1, Agnieszka Szmurło1, Paweł Stankiewicz2

  • 1Institute of Computer Science, Warsaw University of Technology, Warsaw, Warsaw 00-661, Poland.

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|December 14, 2022
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Summary

We developed SeQuiLa, a scalable pileup algorithm for bioinformatics. This distributed tool significantly speeds up genomic data analysis and reduces cloud costs, making large-scale population genetics more accessible.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Pileup analysis is crucial for variant calling and genotyping.
  • Sequential implementations create bottlenecks in bioinformatics pipelines.
  • Distributed algorithms face challenges with read-oriented file formats.

Purpose of the Study:

  • To present a scalable, distributed, and efficient pileup algorithm for cloud environments.
  • To optimize pileup analysis for large-scale genomic datasets.
  • To reduce computational costs and execution time for bioinformatics tasks.

Main Methods:

  • Developed a custom data-partitioning algorithm for alignment reads.
  • Implemented a novel approach using MD tags for processing alignment events.
  • Applied source code micro-optimizations and a modular algorithm structure.
  • Designed SeQuiLa as a cloud-native solution deployable on Kubernetes and Hadoop services.

Main Results:

  • SeQuiLa outperforms state-of-the-art distributed tools by up to 6.5x in execution time.
  • Achieved up to 2x reduction in memory usage, leading to significant cloud cost savings.
  • Provides a unified SQL interface for interactive population-scale genomic data analysis.

Conclusions:

  • SeQuiLa offers a highly efficient and scalable solution for pileup analysis in cloud computing.
  • The tool facilitates cost-effective and rapid analysis of large genomic datasets.
  • Enables convenient, interactive population-scale genomic data exploration via a SQL interface.