Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
Next-generation Sequencing03:00

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.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Deep learning models for cell cycle phase prediction from single-cell RNA sequencing data.

Briefings in bioinformatics·2026
Same author

Dynamic effects of radioactive iodine therapy on gut microbiota and metabolites in patients with papillary thyroid cancer.

Microbiology spectrum·2026
Same author

The mitochondrial protease, LonP1, is a potential cardioprotective target for attenuating doxorubicin-induced cardiomyocyte death.

Journal of translational medicine·2026
Same author

Structural basis for the ion selectivity of potassium-chloride cotransporter KCC4 revealed by cryo-EM titration.

Biophysics reports·2026
Same author

Text-dominant decision-making by large multimodal models in dermatology clinical challenges: Comment on "AI-assisted dermatologic diagnosis using a large language model".

Journal of the American Academy of Dermatology·2026
Same author

Genomic landscape and tumor immune microenvironment of osteosarcoma: Bridging mechanistic insights to precision therapeutics.

Bone reports·2026

Related Experiment Video

Updated: May 10, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Gene prediction in metagenomic fragments based on the SVM algorithm.

Yongchu Liu1, Jiangtao Guo, Gangqing Hu

  • 1State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China.

BMC Bioinformatics
|June 6, 2013
PubMed
Summary

MetaGUN, a novel gene prediction method, accurately identifies genes in metagenomic fragments using a machine learning approach. It outperforms existing methods, especially for novel gene discovery in complex environments like the human gut microbiome.

More Related Videos

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

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

Published on: September 25, 2021

Related Experiment Videos

Last Updated: May 10, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

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

Published on: September 25, 2021

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Metagenomic sequencing enables exploration of microbial communities without cultivation.
  • Accurate gene identification from metagenomic fragments is a critical challenge.

Purpose of the Study:

  • To introduce MetaGUN, a novel machine learning-based gene prediction method for metagenomic fragments.
  • To enhance the accuracy and reliability of gene prediction in complex microbial datasets.

Main Methods:

  • A three-stage strategy involving phylogenetic classification, SVM-based protein-coding sequence identification, and TIS adjustment.
  • Utilizes k-mer based sequence binning, entropy density profiles (EDP), translation initiation site (TIS) scores, and open reading frame (ORF) length.
  • Employs both universal and novel modules for comprehensive gene identification, including conserved domains.

Main Results:

  • MetaGUN demonstrates superior performance in predicting both 3' and 5' ends of genes across various fragment lengths compared to existing methods.
  • Achieves highly reliable gene predictions, outperforming current metagenomic gene finders.
  • Identified thousands of additional genes with significant evidence in human gut microbiome samples.

Conclusions:

  • MetaGUN offers more reliable and comprehensive gene prediction for metagenomic data.
  • It shows a tendency to identify more potential novel genes than other available methods.
  • Provides a valuable tool for exploring microbial genomes and discovering new functional elements.