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

Genomics02:02

Genomics

35.8K
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...
35.8K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
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...
5.7K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

12.4K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
12.4K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.0K
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...
7.0K
Genomic DNA in Eukaryotes00:58

Genomic DNA in Eukaryotes

46.6K
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.
46.6K
Next-generation Sequencing03:00

Next-generation Sequencing

87.3K
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....
87.3K

You might also read

Related Articles

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

Sort by
Same author

Long-read sequencing and the evolving landscape of facioscapulohumeral muscular dystrophy diagnosis.

Brain : a journal of neurology·2026
Same author

Expanding Genetic and Clinical Spectra of Inherited Retinal Dystrophies: Identification of Three Novel <i>PRPH2</i> Variants.

Biomedicines·2025
Same author

<i>BRCA</i> Screening and Identification of a Common Haplotype in the Jewish Community of Rome Reveal a Founder Effect for the c.7007G>C, p. (Arg2336Pro) <i>BRCA2</i> Variant.

Cancers·2025
Same author

Sample Tracking Tool: A Comprehensive Approach Based on OpenArray Technology and R Scripting for Genomic Sample Monitoring.

Diagnostics (Basel, Switzerland)·2025
Same author

AI-Powered Neurogenetics: Supporting Patient's Evaluation with Chatbot.

Genes·2025
Same author

Deciphering the Complexity of FSHD: A Multimodal Approach as a Model for Rare Disorders.

International journal of molecular sciences·2024

Related Experiment Video

Updated: Jun 3, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
10:41

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

9.2K

Federated Learning: Breaking Down Barriers in Global Genomic Research.

Giulia Calvino1,2, Cristina Peconi1, Claudia Strafella1

  • 1Genomic Medicine Laboratory UILDM, IRCCS Santa Lucia Foundation, 00179 Rome, Italy.

Genes
|January 8, 2025
PubMed
Summary

Federated Learning (FL) enables secure, decentralized genomic data analysis, overcoming data silos and privacy issues. This approach supports collaborative research and advances precision medicine while complying with regulations like GDPR.

Keywords:
NGS sequencingartificial intelligencefederated learninggenomic data privacymachine learningprecision medicine

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.1K
Capturing Chromosome Conformation Across Length Scales
10:15

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

3.4K

Related Experiment Videos

Last Updated: Jun 3, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
10:41

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

9.2K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.1K
Capturing Chromosome Conformation Across Length Scales
10:15

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

3.4K

Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Next-Generation Sequencing (NGS) has advanced genomic research, enabling personalized medicine and population genetics.
  • Data silos, privacy concerns, and regulatory hurdles impede large-scale genomic data integration and collaboration.
  • Federated Learning (FL) offers a decentralized approach to data analysis, preserving privacy and adhering to regulations like GDPR.

Purpose of the Study:

  • To explore the application and potential of Federated Learning (FL) in the field of genomics.
  • To detail the methodology of FL for genomic data analysis, including privacy-preserving techniques.
  • To examine the challenges and regulatory considerations, such as GDPR, for implementing FL in genomics.

Main Methods:

  • Review of Federated Learning methodologies: local model training, secure aggregation, and iterative refinement.
  • Analysis of FL's suitability for decentralized genomic data analysis.
  • Examination of challenges: heterogeneous data integration and cybersecurity risks.

Main Results:

  • FL enables decentralized analysis of genomic data, addressing privacy and regulatory concerns (e.g., GDPR).
  • Successful implementations in global and national initiatives demonstrate FL's scalability and collaborative potential.
  • FL facilitates secure data sharing and model building across multiple institutions without centralizing sensitive data.

Conclusions:

  • Federated Learning is a viable and scalable solution for collaborative genomic research, enhancing precision medicine.
  • Addressing challenges like data heterogeneity and cybersecurity is crucial for widespread FL adoption in genomics.
  • Future directions include AI integration and enhanced education to maximize FL's impact on global health initiatives.