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Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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Related Experiment Video

Updated: Jul 12, 2025

Combining Analysis of DNA in a Crude Virion Extraction with the Analysis of RNA from Infected Leaves to Discover New Virus Genomes

Combining Analysis of DNA in a Crude Virion Extraction with the Analysis of RNA from Infected Leaves to Discover New Virus Genomes

Published on: July 27, 2018

plASgraph2: using graph neural networks to detect plasmid contigs from an assembly graph.

Janik Sielemann¹, Katharina Sielemann², Broňa Brejová³

¹Computational Biology, Faculty of Biology, Center for Biotechnology & Graduate School Digital Infrastructures for the Life Sciences (DILS), Bielefeld Institute for Bioinformatics Infrastructure, Bielefeld University, Bielefeld, Germany.

Frontiers in Microbiology

|October 23, 2023

Summary

This study introduces plASgraph2, a novel tool using graph neural networks (GNNs) to accurately identify plasmid DNA sequences in fragmented bacterial genomes, aiding antimicrobial resistance research.

Keywords:

assembly graph bioinformatics classification machine learning (ML)plasmids

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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

Published on: August 20, 2021

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Last Updated: Jul 12, 2025

Combining Analysis of DNA in a Crude Virion Extraction with the Analysis of RNA from Infected Leaves to Discover New Virus Genomes

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Published on: July 27, 2018

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Published on: November 25, 2015

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

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Plasmid identification from sequencing data is crucial for understanding antimicrobial resistance (AMR) spread and One-Health issues.
Fragmented genome assemblies from short-read data pose challenges for accurate plasmid contig identification.
Existing methods often struggle with classifying short contigs based on sequence features or database searches alone.

Purpose of the Study:

To develop a new computational architecture for improved identification of plasmid contigs in fragmented bacterial genome assemblies.
To leverage graph neural networks (GNNs) and assembly graphs for enhanced contig classification accuracy.
To provide an accurate, user-friendly tool for plasmid identification in bacterial isolates.

Main Methods:

Developed a novel architecture employing graph neural networks (GNNs) integrated with assembly graphs.
Utilized information propagation from neighboring nodes within the assembly graph for classification.
Trained the plASgraph2 model on a dataset comprising samples from the ESKAPEE group of pathogens.

Main Results:

plASgraph2 demonstrates superior or comparable performance against state-of-the-art methods on independent test sets.
The tool shows enhanced accuracy in classifying short contigs, which are typically difficult to identify.
Achieved high performance on both ESKAPEE pathogen samples and related bacterial pathogens.

Conclusions:

plASgraph2 offers a new, accurate, and accessible tool for plasmid contig classification in bacterial genomics.
The study validates the utility of graph neural networks (GNNs) as a powerful approach in genomic data analysis.
The findings contribute to better surveillance of antimicrobial resistance and related One-Health challenges.