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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...
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...
Genome Annotation and Assembly03:36

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.
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.
Genomic DNA in Eukaryotes00:58

Genomic DNA in Eukaryotes

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.
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...

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Related Experiment Video

Updated: Jun 13, 2026

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
09:10

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

The Genomedata format for storing large-scale functional genomics data.

Michael M Hoffman1, Orion J Buske, William Stafford Noble

  • 1Department of Genome Sciences, University of Washington, PO Box 355065, Seattle, WA 98195-5065, USA.

Bioinformatics (Oxford, England)
|May 4, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient genome data storage format for rapid access to large datasets. The new format significantly speeds up data retrieval compared to traditional methods.

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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Related Experiment Videos

Last Updated: Jun 13, 2026

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
09:10

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
09:06

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Managing large genomic datasets is challenging.
  • Efficient storage and retrieval are crucial for genomic data analysis.

Purpose of the Study:

  • To present a novel format for efficient storage of genomic numeric data.
  • To enable fast random access to large genomic datasets.

Main Methods:

  • Developed a new data storage format for genomic data.
  • Created utilities for loading data into the new format.
  • Implemented reference components in Python and C.

Main Results:

  • The format allows fast random access to hundreds of gigabytes of data.
  • The format has a small disk space footprint.
  • Data retrieval is over 2900 times faster than using wiggle files.

Conclusions:

  • The presented format offers efficient storage and rapid access for genomic data.
  • This approach significantly improves upon existing methods for handling large genomic datasets.