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Updated: Jun 13, 2026

Metagenomic Analysis of Silage

Metagenomic Analysis of Silage

Published on: January 13, 2017

Ab initio gene identification in metagenomic sequences.

Wenhan Zhu¹, Alexandre Lomsadze, Mark Borodovsky

¹School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Nucleic Acids Research

|April 21, 2010

Summary

This study refines a gene identification algorithm for microbial DNA sequences using evolutionary dependencies. The enhanced method improves accuracy and identifies thousands of new genes in gut metagenomes.

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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Accurate ab initio gene prediction from short DNA sequences is challenging due to uncertain model parameters.
Machine learning approaches can address this, particularly by leveraging evolutionary relationships between oligonucleotide frequencies and genome composition.

Purpose of the Study:

To describe a refined algorithm for gene identification in microbial DNA sequences.
To enhance the accuracy of gene prediction by improving model parameter estimation.

Main Methods:

Utilizing dependencies between oligonucleotide frequencies in protein-coding regions and genome nucleotide composition.
Employing direct polynomial and logistic approximations for oligonucleotide frequencies.
Developing separate models for bacteria and archaea.

Main Results:

The refined method significantly increases the accuracy of model reconstruction and gene prediction.
Applied to prokaryotic genomes, the algorithm demonstrates high accuracy on short sequences.
Thousands of previously unidentified genes were discovered in human and mouse gut metagenomes.

Conclusions:

The enhanced gene identification algorithm offers improved accuracy for microbial DNA.
This advancement facilitates the discovery of novel genes, particularly in complex metagenomic datasets.