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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: Apr 23, 2026

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

Binning metagenomic contigs by coverage and composition.

Johannes Alneberg¹, Brynjar Smári Bjarnason¹, Ino de Bruijn²

¹1] KTH Royal Institute of Technology, Science for Life Laboratory, School of Biotechnology, Division of Gene Technology, Stockholm, Sweden. [2].

|September 15, 2014

Summary

This summary is machine-generated.

CONCOCT is a new algorithm that automatically clusters genome fragments from shotgun sequencing data. This method improves the reconstruction of microbial genomes from complex communities using sequence composition and coverage.

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Last Updated: Apr 23, 2026

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Area of Science:

Genomics
Bioinformatics
Microbial Ecology

Background:

Shotgun sequencing is crucial for reconstructing genomes from microbial communities.
Genome assembly from complex metagenomic data often results in fragmented genomes.
Binning genome fragments into complete or near-complete genomes is essential for downstream analysis.

Purpose of the Study:

To introduce CONCOCT, a novel algorithm for automated genome binning.
To improve the reconstruction of microbial genomes from complex metagenomic datasets.
To provide a precise and efficient tool for clustering genomic contigs.

Main Methods:

CONCOCT algorithm development.
Integration of sequence composition and cross-sample coverage data.
Clustering of contigs into distinct genome bins.
Evaluation on artificial and real human gut metagenome datasets.

Main Results:

CONCOCT demonstrates high recall and precision in genome binning.
Successful clustering of contigs into complete genomes.
Effective application on complex human gut metagenome data.

Conclusions:

CONCOCT provides an effective automated solution for genome binning.
The algorithm enhances the reconstruction of microbial genomes from metagenomic data.
CONCOCT is a valuable tool for microbial community genome analysis.