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Related Concept Videos

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.
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.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
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.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
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When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...

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Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
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Published on: November 12, 2012

Defining functional distances over gene ontology.

Angela del Pozo1, Florencio Pazos, Alfonso Valencia

  • 1Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernandez Almagro, 3, E-28029 Madrid, Spain. adelpozo@cnio.es

BMC Bioinformatics
|January 29, 2008
PubMed
Summary

We introduce a novel method to quantify protein function similarity using GO term co-occurrence in Interpro entries. This approach generates a

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Quantifying functional similarity between proteins is challenging, despite existing ontologies like Gene Ontology (GO).
  • Traditional methods rely on GO structure, which can be limited by ontology 'richness' variations.
  • Functional metrics are needed to improve protein function comparison and prediction accuracy.

Purpose of the Study:

  • To develop a new method for quantifying functional distances between GO terms.
  • To overcome limitations of ontology-based similarity measures.
  • To establish a robust approach for protein function analysis.

Main Methods:

  • Propose a novel method based on the simultaneous occurrence of GO terms within Interpro entries.
  • Derive 'functional distances' (Df) independent of GO hierarchical structure.
  • Represent functional distances as a hierarchical 'Functional Tree'.

Main Results:

  • The proposed method defines functional distances based on shared Interpro entries.
  • This approach reveals natural biological links between GO functions.
  • The resulting 'Functional Tree' organizes GO terms into biologically meaningful groups.

Conclusions:

  • The new distance metric effectively quantifies GO term similarity.
  • The 'Functional Tree' enhances protein function comparison and prediction.
  • This method has potential applications in function-based database searches and gene cluster analysis.