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Related Experiment Video

Updated: Aug 19, 2025

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Persistent memory as an effective alternative to random access memory in metagenome assembly.

Jingchao Sun¹, Zhining Qiu¹, Rob Egan²

¹MemVerge Inc, Milpitas, CA, 95035, USA.

BMC Bioinformatics

|November 30, 2022

Summary

Persistent Memory (PMem) can substitute Dynamic Random Access Memory (DRAM) for large metagenome assembly, enabling terabyte-scale datasets. This approach reduces out-of-memory failures, though it may introduce a speed tradeoff.

Keywords:

Metagenome assembly Out-of-memory Persistent memory

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Last Updated: Aug 19, 2025

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

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Published on: August 20, 2021

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Published on: January 13, 2017

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

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Published on: August 29, 2014

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Metagenome assembly is crucial for characterizing microbial communities without cultivation.
This process is memory-intensive and often fails due to out-of-memory errors, especially with large datasets.
Predicting memory requirements for metagenome assembly is challenging due to data-specific patterns.

Purpose of the Study:

To investigate Persistent Memory (PMem) as a cost-effective alternative to DRAM for metagenome assembly.
To assess PMem's ability to reduce out-of-memory errors and enhance scalability for large datasets.
To evaluate the performance impact of PMem on popular metagenome assemblers.

Main Methods:

Evaluated three popular metagenome assemblers (MetaSPAdes, MEGAHIT, MetaHipMer2).
Tested assemblers on datasets up to one terabase using varying ratios of DRAM and PMem.
Measured execution time and memory usage to compare performance.

Main Results:

PMem successfully enabled metagenome assembly on terabyte-sized datasets by partially or fully substituting DRAM.
Assembly speed with PMem was comparable to DRAM in some configurations, with a maximum two-fold slowdown observed.
Different assemblers exhibited unique memory/speed trade-offs when utilizing PMem.

Conclusions:

PMem expands DRAM capacity, facilitating larger metagenome assemblies with a manageable speed tradeoff.
PMem can be integrated without application-specific code changes, suggesting broad applicability to other memory-intensive bioinformatics tasks.
These findings offer a scalable solution for handling massive genomic datasets in computational biology.