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Related Concept Videos

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...
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 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.
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Gene Families01:57

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Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
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Related Experiment Video

Updated: Jun 23, 2026

Metagenomic Analysis of Silage
08:43

Metagenomic Analysis of Silage

Published on: January 13, 2017

Data structures and compression algorithms for genomic sequence data.

Marty C Brandon1, Douglas C Wallace, Pierre Baldi

  • 1Department of Computer Science, UCI, Irvine, CA 92697, USA.

Bioinformatics (Oxford, England)
|May 19, 2009
PubMed
Summary
This summary is machine-generated.

Researchers developed efficient data structures and algorithms to compress large genome sequences by encoding only differences from a reference sequence. This method achieved significant compression rates, reducing storage needs for genomic data.

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Last Updated: Jun 23, 2026

Metagenomic Analysis of Silage
08:43

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Published on: January 13, 2017

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
12:36

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Published on: May 9, 2011

Ultra-long Read Sequencing for Whole Genomic DNA Analysis
10:34

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Exponential growth of genomic data presents storage, navigation, and privacy challenges.
  • Efficient management of large-scale genomic datasets is crucial for research and application.

Purpose of the Study:

  • To develop novel data structures and algorithms for efficient storage of genomic and sequence data.
  • To facilitate querying and protection of sensitive genomic information.

Main Methods:

  • Encoding only differences between genome sequences and a reference sequence.
  • Utilizing absolute or relative coordinates for variant locations.
  • Employing entropy coding methods (Golomb, Elias, Huffman) for binary string encoding.

Main Results:

  • Demonstrated significant compression rates using human mitochondrial genome sequences.
  • Achieved a 345-fold compression rate (167 KB from 56 MB) using the revised Cambridge Reference Sequence.
  • Further improved compression to 433-fold (133 KB) using a consensus sequence.

Conclusions:

  • The developed approach offers a highly efficient method for genomic data compression.
  • The strategy is extendable to nuclear genomes and high-throughput sequencing data.
  • This facilitates better storage, navigation, and privacy of vast genomic datasets.