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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Published on: December 7, 2021

Big Data in metagenomics: Apache Spark vs MPI.

José M Abuín^1,2, Nuno Lopes¹, Luís Ferreira¹

¹2Ai-School of Technology, IPCA, Barcelos, Portugal.

|October 6, 2020

Summary

This summary is machine-generated.

MetaCache-MPI, a new Message Passing Interface (MPI) tool, offers faster and more memory-efficient analysis of metagenomic DNA sequencing data compared to Apache Spark solutions.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing generates massive datasets for biology and medicine.
Big Data technologies like Apache Spark are used for processing large-scale biological data.
High Performance Computing (HPC) with Message Passing Interface (MPI) offers potential performance gains.

Purpose of the Study:

To introduce MetaCache-MPI, an MPI-based software for metagenomic data analysis.
To evaluate the performance and memory efficiency of MetaCache-MPI against existing solutions.
To provide a faster and more memory-efficient alternative for building and querying metagenomic databases.

Main Methods:

Developed MetaCache-MPI using Message Passing Interface (MPI).
Compared MetaCache-MPI with the single CPU MetaCache and Apache Spark-based MetaCacheSpark.
Evaluated performance and RAM consumption for database building and querying using 32 processes.

Main Results:

MetaCache-MPI is 1.65x faster than MetaCacheSpark for database building (32 processes).
MetaCache-MPI uses 48.12% less RAM than MetaCacheSpark for database building.
MetaCache-MPI is 3.11x faster and uses 55.56% less RAM for database querying compared to MetaCacheSpark.

Conclusions:

MetaCache-MPI demonstrates superior speed and memory efficiency for metagenomic database construction and querying.
The MPI-based approach offers significant advantages over Spark for large-scale metagenomic data analysis.
MetaCache-MPI maintains the accuracy of the original MetaCache implementation while enhancing performance.