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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
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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.
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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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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
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The KM-Algorithm Identifies Regulated Genes in Time Series Expression Data.

Martina Bremer1, R W Doerge

  • 1Department of Mathematics, San Jose State University, One Washington Square, San Jose, CA 95192, USA.

Advances in Bioinformatics
|December 4, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a statistical method to rank genes in time-series expression data, aiding in the identification of regulated genes and the construction of gene regulatory networks.

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Gene expression time series experiments provide dynamic insights into biological processes.
  • Identifying key regulatory genes is crucial for understanding complex biological networks.
  • Existing methods may not fully capture the dynamic regulation of gene expression.

Purpose of the Study:

  • To develop a statistical method for ranking genes based on their regulatory activity in time-series expression data.
  • To enable focused analysis of specific genes or subsets for gene regulatory network construction.
  • To identify biologically meaningful conclusions from dynamic gene expression data.

Main Methods:

  • Utilized a state space model incorporating hidden regulators of gene expression.
  • Applied Kalman (K) smoothing and maximum (M) likelihood estimation for parameter optimization.
  • Developed a regulation criterion based on derived model parameters.

Main Results:

  • The proposed statistical method effectively ranks genes by their degree of regulation.
  • Demonstrated the ability to identify regulated genes in time-dependent gene expression datasets.
  • Showcased the utility of focusing on strong regulation over large expression values for biological insights.

Conclusions:

  • Meaningful biological conclusions can be derived from gene expression time series experiments.
  • The developed statistical approach enhances the identification of key regulatory genes.
  • Prioritizing strong gene regulation aids in building robust gene regulatory networks.