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

6.4K
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...
6.4K
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

250
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...
250
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.5K
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...
7.5K
Ribosome Profiling02:24

Ribosome Profiling

3.7K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.7K
Genomic DNA in Eukaryotes00:58

Genomic DNA in Eukaryotes

49.2K
Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.
49.2K
Eukaryotic Evolution01:24

Eukaryotic Evolution

38.4K
The endosymbiont theory is the most widely accepted theory of eukaryotic evolution; however, its progression is still somewhat debated. According to the nucleus-first hypothesis, the ancestral prokaryote first evolved a membrane to enclose DNA and form the nucleus. Conversely, the mitochondria-first hypothesis suggests that the nucleus was formed after endosymbiosis of mitochondria.
Contrary to the endosymbiont theory, the eukaryote-first hypothesis proposes that the simpler prokaryotic and...
38.4K

You might also read

Related Articles

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

Sort by
Same author

A pan-viral map of host dependency factors from multi-omics integration and machine learning across influenza A, SARS-CoV-2, Zika, and dengue viruses.

Journal of translational medicine·2026
Same author

IntegrateALL: An end-to-end RNA-seq analysis pipeline for multilevel data extraction and interpretable subtype classification in B-precursor ALL.

HemaSphere·2026
Same author

Targeting the cell membrane in established and emerging model organisms.

Development (Cambridge, England)·2026
Same author

Molecular variants, clonal evolution and clinical relevance in pediatric and adult T-cell lymphoblastic neoplasia.

Blood cancer journal·2026
Same author

IGH::FENDRR and specific KRAS mutations define a novel B-ALL molecular subtype with poor chemotherapy response.

Blood·2026
Same author

IDH2 Clonal Hematopoiesis and IKAROS Loss Cooperate in a B-ALL Subtype after Lenalidomide Therapy for Multiple Myeloma.

Blood·2026

Related Experiment Video

Updated: Oct 11, 2025

An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

3.6K

Identifying essential genes across eukaryotes by machine learning.

Thomas Beder1, Olufemi Aromolaran2, Jürgen Dönitz3

  • 1Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany.

NAR Genomics and Bioinformatics
|December 3, 2021
PubMed
Summary
This summary is machine-generated.

Predicting essential genes across species using machine learning is now possible. This approach overcomes limitations of homology and improves essential gene identification in diverse organisms.

More Related Videos

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.1K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.1K

Related Experiment Videos

Last Updated: Oct 11, 2025

An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

3.6K
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.1K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.1K

Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Genome-scale essential gene identification is resource-intensive and limited to a few eukaryotes.
  • Predicting essentiality via gene homology fails for non-conserved genes.
  • Essentiality varies between single-cell and multicellular organisms, and human studies.

Purpose of the Study:

  • To develop a generalizable machine learning model for predicting essential genes across diverse eukaryotes.
  • To overcome limitations of homology-based predictions and divergent essentiality data.
  • To experimentally validate predictions in non-model organisms.

Main Methods:

  • Machine learning applied to 60,381 genes across six model eukaryotes.
  • Utilized 41,635 features including sequence, gene function, and network topology.
  • Employed leave-one-organism-out cross-validation for generalizability assessment.

Main Results:

  • Achieved high generalizability with an average accuracy near 80% in left-out species.
  • Successfully applied and experimentally validated predictions in *Tribolium castaneum* and *Bombyx mori*.
  • Linked essentiality information from model organisms to human cell line screens and population studies.

Conclusions:

  • Machine learning provides a robust and generalizable method for predicting essential genes.
  • This approach enhances essential gene discovery in less-studied organisms.
  • The model bridges essentiality data across different biological contexts, including human studies.