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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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Updated: Mar 3, 2026

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Published on: October 9, 2017

BusyBee Web: metagenomic data analysis by bootstrapped supervised binning and annotation.

Cedric C Laczny¹, Christina Kiefer¹, Valentina Galata¹

¹Chair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.

Nucleic Acids Research

|May 5, 2017

Summary

This summary is machine-generated.

BusyBee Web server automates metagenomic data binning without reference genomes. This tool efficiently recovers microbial genomes from assembled contigs and long reads, aiding biotechnology and life sciences research.

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

Metagenomics enables studying microbial communities without cultivation.
Computational binning is crucial for isolating individual microbial genomes from complex samples.
Current binning tools often require reference genomes or significant computational resources.

Purpose of the Study:

To develop a reference-independent computational tool for automated metagenomic data binning.
To enhance the recovery of population-level genomic sequences from mixed microbial communities.
To provide an accessible web server for metagenomic binning.

Main Methods:

Developed the BusyBee Web server for automated binning of metagenomic data.
Utilized assembled contigs (Illumina) and long reads (PacBio, Oxford Nanopore).
Implemented reversible compression and bootstrapped supervised binning for efficiency.

Main Results:

BusyBee Web server demonstrates high performance in binning assembled contigs and significantly improved performance for long reads.
Achieved median F1 scores ranging from 70.02% to 95.21% in ground truth benchmarks.
Results are visualized in interactive 2D scatterplots with quality estimates, taxonomic, and antibiotic resistance gene annotations.

Conclusions:

BusyBee offers a sensitive, precise, and reference-independent approach for metagenomic binning.
The web server simplifies the analysis of assembled contigs and long reads.
Provides intuitive visualization and annotation of binning results for diverse applications in life sciences and medicine.