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Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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The technique helps...
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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.
Reporter Genes02:11

Reporter Genes

Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
Commonly used reporter...
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
What is Gene Expression?01:42

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...

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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
08:09

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Integrating phenotype and gene expression data for predicting gene function.

Brandon M Malone1, Andy D Perkins, Susan M Bridges

  • 1Department of Computer Science and Engineering, Box 9637, Mississippi State University, Mississippi State, MS 39762, USA. bm542@msstate.edu

BMC Bioinformatics
|October 9, 2009
PubMed
Summary
This summary is machine-generated.

Integrating gene expression and textual phenotype data improves gene function prediction. This enhanced approach yields more precise functional annotations for genes, outperforming methods using single data types.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Predicting gene function is crucial for understanding biological systems.
  • Existing methods often rely on single data types, limiting prediction accuracy.
  • Integrating diverse datasets offers a promising avenue for improved gene annotation.

Purpose of the Study:

  • To develop and evaluate a framework for integrating gene expression and textual phenotype data for gene function prediction.
  • To assess the performance of an integrated similarity graph against methods using individual data sources.

Main Methods:

  • Constructed an integrated similarity graph by combining gene expression and textual phenotype data.
  • Utilized the graph to predict gene function and assign annotations from the Gene Ontology.
  • Compared the integrated approach with methods using only gene expression or phenotype data.

Main Results:

  • The integrated similarity graph demonstrated superior precision in gene function prediction compared to individual data types.
  • The integrated approach achieved comparable annotation numbers to gene expression alone.
  • Significantly more correct gene function assignments were generated using the integrated method.

Conclusions:

  • Augmenting gene expression data with textual phenotype data enhances the precision of functional annotation predictions.
  • The integrated approach effectively mitigates the limitations of using textual phenotype data alone.
  • This framework offers a robust strategy for more accurate gene function prediction.