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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.

Genome Copying Errors

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their survival. Therefore, the copying errors are checked and repaired at three levels.

Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

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Related Experiment Video

Updated: Nov 27, 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

High-Quality Genome-Scale Models From Error-Prone, Long-Read Assemblies.

Jared T Broddrick¹, Richard Szubin², Charles J Norsigian²

¹Exobiology Branch, Space Science and Astrobiology Division, NASA Ames Research Center, Moffett Field, CA, United States.

Frontiers in Microbiology

|December 7, 2020

Summary

This summary is machine-generated.

This study presents a workflow for creating genome-scale metabolic models (GEMs) from nanopore sequencing data, enabling rapid characterization of microbial metabolism and antimicrobial resistance (AMR) in pathogens.

Keywords:

MinION long-read sequencing MinION nanopore device®antimicrobial resistance (AMR)constraint-based model metabolic model reconstruction nanopore sequencing

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Last Updated: Nov 27, 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

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

Microbiology
Systems Biology
Bioinformatics

Background:

Nanopore sequencing offers rapid, point-of-care genomic analysis.
Genome-scale models (GEMs) are crucial for understanding bacterial phenotypes, especially antimicrobial resistance (AMR).
Nanopore sequencing errors can compromise GEM quality.

Purpose of the Study:

To develop and validate a workflow for constructing GEMs from nanopore sequencing data.
To assess the utility of this workflow for characterizing pathogenic bacteria, including AMR strains.
To evaluate the impact of sequencing depth and metagenomic approaches on GEM quality.

Main Methods:

A novel computational workflow was developed for GEM reconstruction from nanopore-derived assemblies.
The pipeline was benchmarked using *Escherichia coli* K-12 reference data.
The workflow was applied to clinical isolates of pathogenic bacteria and mock metagenomic samples.

Main Results:

Nanopore-derived GEMs achieved >99% completeness, even at low sequencing depths (<10× coverage).
The workflow successfully generated strain-specific GEMs for clinical isolates, identifying AMR genes and enabling growth simulations.
Metagenomic processing did not degrade the quality of derived GEMs.

Conclusions:

The developed workflow enables rapid, *in situ* construction of high-quality GEMs from nanopore sequencing data.
This approach facilitates the characterization of microbial metabolism and AMR in pathogenic bacteria at the point of care.
Combining nanopore sequencing with GEMs offers a powerful strategy for combating AMR pathogens.