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Genetic Lingo

Genetic Lingo

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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.

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Gene Families

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Updated: Jan 11, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

FGeneBERT: function-driven pre-trained gene language model for metagenomics.

Chenrui Duan^1,2, Zelin Zang³, Yongjie Xu^1,2

¹College of Computer Science and Technology, Zhejiang University, No. 866, Yuhangtang Road, 310058 Zhejiang, P. R. China.

Briefings in Bioinformatics

|November 10, 2025

Summary

This summary is machine-generated.

FGeneBERT, a new metagenomic model, uses protein-based gene context to improve understanding of gene relationships and functions. This approach enhances analysis across gene, functional, bacterial, and environmental levels in complex biological data.

Keywords:

DNA metagenomics pre-trained language model transformer

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Metagenomic data, crucial for understanding diverse environments and human health, presents challenges due to mixed genomes.
Current K-mer based methods limit capturing structural and functional gene contexts and struggle with complex gene relationships.
Existing approaches fail to effectively encode biologically meaningful genes and address inherent one-to-many/many-to-one relationships in metagenomic data.

Purpose of the Study:

To introduce FGeneBERT, a novel pre-trained model designed to overcome limitations in current metagenomic data analysis.
To enhance the understanding of inter-gene contextual relationships and gene sequence-function associations.
To improve the representation and analysis of biologically meaningful genes within complex metagenomic datasets.

Main Methods:

Developed FGeneBERT, a metagenomic pre-trained model utilizing a protein-based gene representation as a tokenizer.
Implemented masked gene modeling to improve comprehension of inter-gene contextual relationships.
Employed triplet enhanced metagenomic contrastive learning to elucidate gene sequence-function relationships.

Main Results:

FGeneBERT demonstrates superior performance across metagenomic datasets at gene, functional, bacterial, and environmental levels.
The model effectively handles varying input sequence sizes, from 1k to 213k.
Case studies on ATP synthase and gene operons showcase FGeneBERT's accuracy in functional recognition and biological relevance.

Conclusions:

FGeneBERT offers a significant advancement in metagenomic data analysis by providing context-aware and structure-relevant gene representations.
The protein-based approach and advanced learning strategies enhance the biological interpretability of metagenomic findings.
FGeneBERT's capabilities are vital for future research in environmental and human health-related metagenomics.