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Updated: Apr 28, 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
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AlignGraph: algorithm for secondary de novo genome assembly guided by closely related references
Ergude Bao1, Tao Jiang1, Thomas Girke1
1Department of Computer Science and Engineering and Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.
Bioinformatics (Oxford, England)
|June 17, 2014
Summary
AlignGraph improves genome assemblies by using related species genomes to extend and join fragmented contigs. This novel algorithm enhances genome assembly quality and completeness, overcoming challenges in next-generation sequencing.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- De novo genome assembly is challenging due to repeats and sequencing errors, often resulting in fragmented contigs.
- Improving genome assembly quality is crucial for understanding genomic structures and functions.
Purpose of the Study:
- To introduce AlignGraph, a novel algorithm for enhancing de novo genome assemblies.
- To leverage closely related reference genomes for improving contig and scaffold quality.
Main Methods:
- AlignGraph aligns paired-end reads and preassembled contigs/scaffolds to a reference genome.
- It constructs a PE multipositional de Bruijn graph incorporating positional information.
- This graph facilitates guided extension and joining of assembly fragments.
Main Results:
- AlignGraph significantly improved contigs and scaffolds from various assemblers.
- Extended 28.7-62.3% of contigs in Arabidopsis thaliana and human assemblies.
- Increased N50 values for extendable contigs by 89.9-165.8% and scaffolds by 86.6%.
Conclusions:
- AlignGraph effectively improves genome assembly quality by utilizing related reference genomes.
- The algorithm overcomes limitations of traditional de novo assembly methods.
- AlignGraph provides a valuable tool for enhancing genomic data analysis.
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