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Genome Annotation and Assembly

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Related Experiment Video

Updated: Feb 8, 2026

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

Versatile genome assembly evaluation with QUAST-LG.

Alla Mikheenko¹, Andrey Prjibelski¹, Vladislav Saveliev¹

¹Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.

Bioinformatics (Oxford, England)

|June 29, 2018

Summary

This summary is machine-generated.

Long-read sequencing advances genome assembly. QUAST-LG evaluates assembly quality and establishes theoretical limits, showing current assemblies approach optimal but still differ from finished references.

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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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Area of Science:

Genomics
Bioinformatics

Background:

High-throughput sequencing revolutionized genomics in the early 2000s.
Long-read sequencing represents the next major advancement in the field.
These technologies, coupled with computational methods, enable near-complete genome assembly.

Purpose of the Study:

To evaluate the performance of state-of-the-art genome assembly software on diverse eukaryotic datasets.
To introduce QUAST-LG, a tool for assessing large de novo genome assemblies against reference sequences.
To define and compute theoretical upper bounds for genome assembly correctness and completeness.

Main Methods:

Benchmarking genome assembly software using six eukaryotic datasets from various sequencing technologies.
Developing and utilizing QUAST-LG for quality metric computation on large-scale assemblies.
Introducing the concept of upper bound assembly to establish theoretical limits for reconstruction.

Main Results:

Demonstrated the performance of current genome assembly software on different sequencing data.
QUAST-LG effectively compares de novo assemblies to reference genomes and calculates quality metrics.
Quantified the gap between current assembly quality and theoretical optimum, and between optimum and finished genomes.

Conclusions:

Current genome assembly methods are approaching theoretical optima for large genomes.
QUAST-LG provides a robust framework for evaluating large genome assemblies.
Further improvements are needed to bridge the gap between optimal assemblies and fully finished reference genomes.