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Genome Annotation and Assembly

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Related Experiment Video

Updated: May 21, 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

Sequential co-assembly reduces computational resources and errors in metagenome-assembled genomes.

Hannah M Lynn¹, Jeffrey I Gordon¹

¹Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA; Newman Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110, USA.

Cell Reports Methods

|March 18, 2025

Summary

This study introduces a sequential co-assembly method for DNA shotgun sequencing data. This approach reduces computational demands, saving time and memory while improving genome assembly accuracy, especially in resource-limited environments.

Keywords:

Bowtie CP: Microbiology CP: Systems biology MEGAHIT assembly time memory requirements metagenome-assembled genomes misassembly errors sequential co-assembly shotgun DNA sequencing reads single-node computing tools

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Last Updated: May 21, 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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Metagenomic Analysis of Silage

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Published on: January 13, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Metagenome-assembled genome generation from DNA shotgun sequencing is computationally intensive.
Traditional co-assembly methods struggle with large datasets and high memory requirements.

Purpose of the Study:

To present a novel sequential co-assembly method to reduce computational resource demands for generating metagenome-assembled genomes.
To demonstrate the efficiency and accuracy of this method compared to traditional approaches.

Main Methods:

A sequential co-assembly strategy using single-node computing tools for read assembly and mapping.
Application to simulated mouse microbiome data, a longitudinal study of human gut microbiomes, and a large-scale mouse microbiome dataset.

Main Results:

The sequential co-assembly method significantly reduced assembly time and memory usage.
Fewer assembly errors were observed compared to traditional co-assembly.
Successfully processed a 2.3-terabyte dataset that was too large for conventional methods.

Conclusions:

The sequential co-assembly method offers a computationally efficient and accurate alternative for metagenome-assembled genome generation.
This approach is particularly beneficial for resource-constrained settings and large datasets.
Facilitates microbiome research in diverse settings, including low- and middle-income countries.