Related Experiment Video
Updated: Feb 15, 2026
12:08
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
5.9K
PAQman: reference-free ensemble evaluation of long-read genome assemblies.
Samuel O'Donnell1, Ningxiao Li2,3, Jacob L Steenwyk4
1Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI 53706, United States.
G3 (Bethesda, Md.)
|February 14, 2026
Summary
Evaluating genome assembly quality is difficult. The Post-Assembly Quality manager (PAQman) tool simplifies this by assessing seven key features without needing a reference genome, streamlining the process for researchers.
Area of Science:
- Genomics
- Bioinformatics
Background:
- Long-read sequencing advances facilitate genome assembly.
- Current genome assembly quality assessment tools are limited, often requiring reference genomes or focusing on few metrics.
- The proliferation of evaluation tools complicates pipeline development.
Purpose of the Study:
- To develop a comprehensive, reference-free tool for genome assembly quality assessment.
- To integrate multiple quality metrics into a single, user-friendly framework.
- To reduce the complexity of evaluating genome assembly quality.
Main Methods:
- Developed the Post-Assembly Quality manager (PAQman) tool.
- PAQman measures seven reference-free genome quality features: Contiguity, Gene content, Completeness, Accuracy, Correctness, Coverage, and Telomerality.
- Integrated existing tools and custom scripts into a unified pipeline.
Main Results:
- PAQman provides a streamlined and consistent framework for genome assembly quality assessment.
- The tool requires only the query genome assembly and underlying long-read data.
- Enables evaluation of multiple quality features within a single system.
Conclusions:
- PAQman lowers the barrier to entry for comprehensive genome assembly quality assessment.
- Offers a standardized approach to evaluating genome quality across diverse datasets.
- Facilitates more robust and reliable genome assembly evaluations.
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