Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Evolutionary Relationships through Genome Comparisons

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...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

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.

You might also read

Related Articles

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

Sort by

Same author

The Vertebrate Genomes Project Phase I: A global reference genome resource.

bioRxiv : the preprint server for biology·2026

Same author

Population-scale Y chromosome assemblies reveal recurrent remodeling within constrained architectures.

bioRxiv : the preprint server for biology·2026

Same author

A complete human pancreatic cancer genome.

bioRxiv : the preprint server for biology·2026

Same author

Clinical outcome of biomarker-guided therapies in adult neuro-oncology patients: An update from the Tübingen molecular tumor board cohort.

Neuro-oncology advances·2026

Same author

Resistance Training Reshapes the Gut Microbiome in a Longitudinal 8-Week Intervention in Sedentary Adults.

Sports medicine - open·2026

Same author

Adaptive Tracepoints for Pangenome Alignment Compression.

bioRxiv : the preprint server for biology·2026

Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026

Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026

Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 22, 2025

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

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

Published on: December 7, 2021

Pangenome graph layout by Path-Guided Stochastic Gradient Descent.

Simon Heumos^1,2,3,4, Andrea Guarracino^5,6, Jan-Niklas M Schmelzle^7,8

¹Quantitative Biology Center (QBiC), University of Tübingen, 72076 Tübingen, Germany.

Bioinformatics (Oxford, England)

|July 3, 2024

Summary

This summary is machine-generated.

Visualizing large pangenome graphs is challenging. We developed Path-Guided Stochastic Gradient Descent (PG-SGD), an efficient algorithm to create low-dimensional layouts, revealing genomic diversity and features.

More Related Videos

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Related Experiment Videos

Last Updated: Jun 22, 2025

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

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

Published on: December 7, 2021

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

The increasing availability of complete genomes necessitates advanced models for studying population-level genomic variability.
Pangenome graphs are crucial for representing genomic similarity and diversity across multiple genomes.
Effective visualization of large pangenome graphs is essential for biological interpretation but poses a significant challenge due to their scale.

Purpose of the Study:

To address the challenge of visualizing large-scale pangenome graphs.
To develop a novel and efficient graph layout algorithm for pangenome visualization.
To enable the exploration of genomic diversity and biological features within populations.

Main Methods:

Introduction of Path-Guided Stochastic Gradient Descent (PG-SGD), a novel graph layout algorithm.
Utilizing genomes as paths within the pangenome graph to define an embedded positional system.
Sampling genomic distances between node pairs efficiently, avoiding quadratic costs associated with traditional Stochastic Gradient Descent (SGD) graph drawing methods.

Main Results:

PG-SGD efficiently computes low-dimensional layouts for gigabase-scale pangenome graphs.
The algorithm effectively visualizes complex genomic structures and facilitates the discovery of biological features.
Demonstrated scalability and efficiency in generating human-readable graph embeddings.

Conclusions:

PG-SGD offers an efficient solution for visualizing large pangenome graphs.
The algorithm aids in understanding genomic variability and diversity within populations.
The developed method enhances the interpretability of complex genomic data.