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

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Circulating imidazole propionate and coronary heart disease risk: interplay between histidine intake, fiber, and gut microbiome.

BMC medicine·2026

Same author

Itacitinib plus calcineurin inhibitor-based therapy for prophylaxis of graft-versus-host disease: GRAVITAS-119 results.

Research square·2026

Same author

Item recognition is associated with gut microbiota composition in healthy humans.

Learning & memory (Cold Spring Harbor, N.Y.)·2026

Same author

Shotgun Metagenomic Profiling of the Gut Virome in Prodromal and Confirmed Parkinson's Disease.

Annals of neurology·2026

Same author

Long-lasting gut microbiome and fecal metabolome alterations after colorectal adenoma removal and their relationship to colorectal cancer.

Cell host & microbe·2026

Same author

Dietary sulfur amino acids enhance anti-tumor immunity in colon cancer via an NKT cell-XCL1-cDC1 circuit.

Immunity·2026

Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·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

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 5, 2026

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

Published on: June 10, 2017

The Sleipnir library for computational functional genomics.

Curtis Huttenhower¹, Mark Schroeder, Maria D Chikina

¹Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, NJ 08540, USA.

Bioinformatics (Oxford, England)

|May 24, 2008

Summary

This summary is machine-generated.

The Sleipnir C++ library offers efficient machine learning and data manipulation for large biological datasets. It enables integrated analysis of heterogeneous data, making complex biological insights more accessible.

More Related Videos

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

Related Experiment Videos

Last Updated: Jul 5, 2026

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma

Published on: June 10, 2017

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Rapid acceleration in biological data generation, leading to massive whole-genome datasets.
Significant computational challenges in managing and analyzing large-scale biological data collections.
Need for efficient methods to handle storage, memory, and processing resources for big data.

Purpose of the Study:

To develop computational methods for efficient analysis of large biological data.
To address the challenge of managing and mining vast, heterogeneous biological datasets.
To create tools for integrated analysis of diverse biological data types.

Main Methods:

Implementation of a C++ library (Sleipnir) with machine learning and data manipulation algorithms.
Focus on heterogeneous data integration and computational efficiency for large datasets.
Development of multithreaded tools for parallel processing in various computing environments.

Main Results:

Sleipnir library facilitates microarray processing, functional ontology mining, clustering, Bayesian learning, and support vector machine tasks.
Enables analysis of heterogeneous data at unprecedented scales.
Prebuilt tools and multithreaded capabilities allow common tasks to be performed rapidly on standard hardware.

Conclusions:

Sleipnir provides an efficient and scalable solution for analyzing large, heterogeneous biological data.
The library and tools can be integrated into existing computational systems.
Accelerates the extraction of biological insights from massive genomic datasets.