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

36.6K
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...
36.6K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.7K
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...
13.7K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.9K
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...
5.9K

You might also read

Related Articles

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

Sort by
Same author

Cross-species consensus atlas of the primate basal ganglia.

bioRxiv : the preprint server for biology·2025
Same author

Author Correction: Conservation and alteration of mammalian striatal interneurons.

Nature·2025
Same author

Fast Optimization of Robust Transcriptomics Embeddings using Probabilistic Inference Autoencoder Networks for multi-Omics.

bioRxiv : the preprint server for biology·2025
Same author

Conservation and alteration of mammalian striatal interneurons.

Nature·2025
Same author

Conservation, alteration, and redistribution of mammalian striatal interneurons.

bioRxiv : the preprint server for biology·2024
Same author

The NELF pausing checkpoint mediates the functional divergence of Cdk9.

Nature communications·2023

Related Experiment Video

Updated: Jul 29, 2025

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

2.2K

BRGenomics for analyzing high-resolution genomics data in R.

Michael DeBerardine1

  • 1Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, United States.

Bioinformatics (Oxford, England)
|May 19, 2023
PubMed
Summary
This summary is machine-generated.

BRGenomics is a new R package offering efficient genomics data analysis. It provides fast, flexible methods for processing and analyzing high-resolution sequencing data in R.

More Related Videos

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

19.0K
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.3K

Related Experiment Videos

Last Updated: Jul 29, 2025

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

2.2K
Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

19.0K
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.3K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-resolution genomics data analysis requires efficient and flexible computational tools.
  • Existing methods may lack the speed or adaptability needed for complex datasets.
  • The Bioconductor ecosystem provides a robust framework for bioinformatics analysis.

Purpose of the Study:

  • Introduce the BRGenomics R/Bioconductor package.
  • Provide fast and flexible methods for post-alignment processing and analysis of high-resolution genomics data.
  • Facilitate the analysis of various sequencing data types within an interactive R environment.

Main Methods:

  • Utilizes core Bioconductor packages like GenomicRanges.
  • Implements methods for data importation, processing, and read counting.
  • Includes functions for normalization, re-sampling, and data cleaning.
  • Supports parallel processing and efficient data storage strategies.

Main Results:

  • BRGenomics offers optimized methods for handling multiple datasets simultaneously.
  • The package supports quantitative single-base data and run-length encoded coverage.
  • Successfully applied to analyze ATAC-seq, ChIP-seq, PRO-seq, and RNA-seq data.
  • Built for unobtrusive integration and compatibility within the Bioconductor ecosystem.

Conclusions:

  • BRGenomics provides a comprehensive solution for high-resolution genomics data analysis.
  • The package enhances efficiency and flexibility for researchers in R.
  • Extensive documentation and examples ensure ease of use and adoption.