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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

Gene Families

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

Gene Families

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...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Pedigree Analysis01:35

Pedigree Analysis

Overview
Pedigree Analysis01:35

Pedigree Analysis

Overview

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Related Experiment Videos

GIFtS: annotation landscape analysis with GeneCards.

Arye Harel1, Aron Inger, Gil Stelzer

  • 1Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. arik.harel@weizmann.ac.il

BMC Bioinformatics
|October 27, 2009
PubMed
Summary
This summary is machine-generated.

The GeneCards Inferred Functionality Score (GIFtS) quantifies gene annotation status using diverse data. This bioinformatics tool aids in analyzing gene sets and identifying uncharacterized genes for novel discoveries.

Related Experiment Videos

Area of Science:

  • Computational genomics
  • Bioinformatics
  • Gene annotation

Background:

  • Gene annotation is crucial for understanding gene function, expression, and sequence.
  • GeneCards is a comprehensive resource for human gene information, integrating data from 68 sources.
  • Quantitative measures of gene annotation are valuable bioinformatics tools.

Purpose of the Study:

  • To introduce the GeneCards Inferred Functionality Score (GIFtS) for quantitative assessment of gene annotation.
  • To leverage the extensive data within GeneCards for a novel gene annotation metric.
  • To provide a tool for exploring and classifying genes based on their annotation status.

Main Methods:

  • Developed the GIFtS algorithm utilizing GeneCards' diverse data sources.
  • Implemented GIFtS for browsing the human genome and retrieving genes by annotation level.
  • Utilized cluster analysis of GIFtS annotation vectors for gene classification.
  • Evaluated data sources based on their contribution to gene annotation.

Main Results:

  • The GIFtS distribution reveals two main gene groups: highly annotated protein-coding genes and broadly categorized genes.
  • Cluster analysis classified gene groups based on their annotation profiles.
  • An inverse correlation was observed between source gene count and average GIFtS.
  • GIFtS correlates with publication count and HGNC database entry seniority for highly annotated genes.

Conclusions:

  • GIFtS is a valuable tool for analyzing large gene sets in computational and wet-lab research.
  • GIFtS can help identify uncharacterized gene groups for exploring novel functions and the human genome.
  • The tool facilitates the identification of poorly characterized genes for further investigation.