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Related Concept Videos

Genomics02:02

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...
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Evolutionary Relationships through Genome Comparisons02:54

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...
What is Population Genetics?01:25

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.

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Updated: May 21, 2026

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

Exploring massive, genome scale datasets with the GenometriCorr package.

Alexander Favorov1, Loris Mularoni, Leslie M Cope

  • 1Department of Oncology, Division of Biostatistics and Bioinformatics, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America. favorov@sensi.org

Plos Computational Biology
|June 14, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces GenometriCorr, an R package for analyzing genomewide data correlations. It helps explore biological relationships within high-dimensional datasets by calculating spatial associations between genomic intervals.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-dimensional genomic datasets present challenges for biological interpretation.
  • Identifying functional interactions between genomic regions is crucial for understanding biological processes.

Purpose of the Study:

  • To develop a statistically robust tool for correlating genomewide data with other biological features.
  • To guide the biological exploration of complex, high-dimensional datasets.

Main Methods:

  • Implementation of an R package named GenometriCorr.
  • Efficient calculation of spatial correlation between genomic intervals (data and annotated features).
  • Handles diverse data types (pointwise or interval) and computes significance/direction of spatial associations.

Main Results:

  • GenometriCorr provides a metric for functional interaction based on spatial correlation.
  • The software suggests potentially relevant relationships between different genomic datasets.
  • It offers biologically motivated approaches for data analysis.

Conclusions:

  • GenometriCorr serves as a valuable tool for exploring genomewide data correlations.
  • The package facilitates the discovery of functional relationships between genomic features.
  • It aids researchers in navigating and interpreting complex biological datasets.