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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.
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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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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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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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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
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Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization).

Elina Tjioe1, Michael W Berry, Ramin Homayouni

  • 1Department of Electrical Engineering and Computer Science and Graduate School of Genome Science and Techonology, University of Tennessee, Knoxville, TN 37996, USA.

BMC Bioinformatics
|October 16, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces FAUN, a bioinformatics tool that uses Nonnegative Matrix Factorization (NMF) to discover novel gene relationships from biomedical literature. FAUN aids researchers in interpreting complex genomic data and validating gene associations.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Extracting novel functional gene relationships from vast biomedical literature is a significant bioinformatics challenge.
  • Existing tools often struggle to identify implied or novel gene relationships crucial for discovery-oriented experiments.

Purpose of the Study:

  • To develop a web-based bioinformatics software environment, FAUN (Feature Annotation Using Nonnegative Matrix Factorization), for discovering and classifying gene functional relationships.
  • To address the computational complexity and parameterization of Nonnegative Matrix Factorization (NMF) for gene set processing.

Main Methods:

  • Development of FAUN, a web-based bioinformatics software environment.
  • Application of Nonnegative Matrix Factorization (NMF) for analyzing gene document collections.
  • Testing FAUN on manually curated gene document sets and a gene set associated with Autism.

Main Results:

  • FAUN facilitates the discovery and classification of functional gene relationships.
  • The study discusses the computational aspects and parameterization of NMF for gene set analysis.
  • FAUN demonstrated utility and performance as a knowledge discovery tool, particularly for Autism-related genes.

Conclusions:

  • FAUN enhances the efficiency of biomedical literature utilization for researchers.
  • The software provides valuable tools for knowledge discovery and validation of functional gene associations.
  • FAUN is beneficial for analyzing gene subsets identified in high-throughput experiments.