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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...
Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
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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.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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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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Related Experiment Video

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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
09:27

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Published on: March 15, 2011

Use of genomic DNA control features and predicted operon structure in microarray data analysis: ArrayLeaRNA - a

Carmen Pin1, Mark Reuter

  • 1Institute of Food Research, Norwich, NR4 7UA, UK. carmen.pin@bbsrc.ac.uk

BMC Bioinformatics
|November 21, 2007
PubMed
Summary

A new tool, ArrayLeaRNA, offers improved gene expression analysis using a novel Bayesian model. This method enhances data normalization and statistical significance determination for microarrays, aiding in identifying differentially expressed genes.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarrays are essential for gene expression studies.
  • Determining the statistical significance of observed expression differences remains a challenge.

Purpose of the Study:

  • To develop and evaluate a novel Bayesian approach for robust gene expression analysis using microarrays.
  • To improve data normalization and the accurate identification of differentially expressed genes.

Main Methods:

  • Developed ArrayLeaRNA, a Bayesian tool utilizing Gumbel distribution and genomic DNA control features for normalization and parameter estimation.
  • Incorporated prior knowledge from predicted operon structures.
  • Compared ArrayLeaRNA against LOWESS normalization and the OpWise Bayesian method using experimental datasets.

Main Results:

  • ArrayLeaRNA demonstrated superior data normalization by using equally transcribed genes as a reference.
  • Statistical significance was determined based on the variability of equally transcribed genes.
  • Operon information improved the classification of genes with low-confidence measurements.
  • ArrayLeaRNA outperformed LOWESS and OpWise in normalization, variability estimation, and distinguishing differentially expressed genes.

Conclusions:

  • ArrayLeaRNA provides a robust Bayesian method for analyzing microarray expression profiles.
  • The tool significantly enhances normalization accuracy, experimental variability estimation, and the identification of significant gene expression changes.
  • ArrayLeaRNA is versatile, applicable to various hybridization types and datasets with predominant differential gene regulation.