Gene expression can be determined by several methods including in broad terms protein expression, and mRNA expression.
mRNA Expression Analysis
mRNA expression analysis or expression of mRNA levels can be assayed by multiple techniques at a large-scale level these include:
- cDNA microarray analysis
- EST or expressed cDNA sequence tags
- SAGE (Serial Analysis of Gene Expression)
- Massively Parallel Signature Sequencing (MPSS)
- multiplexed in-situ hybridization
At a smaller scale or to confirm results of a larger-scale one can use the following mRNA expression techniques:
- Real-Time PCR
- Ribonuclease protection assay (RPA)
However, bioinformatic analysis can be used. Bioinformatic analysis allows the bypassing of biological measurement bias and noise of the specific techniques and uses statistical tools to separate significant change in gene expression from noise in large-scale or high-throughput gene expression studies.
Such studies can be employed to determine the genes implicated in a disorder: one might compare microarray data from cancerous epithelial cells to data from non-cancerous cells to determine the transcripts that are up-regulated and down-regulated in a particular population of cancer cells.
Affymetrix GeneChip Technology Analysis
Affymetrix GeneChip Technology is used frequently to analyse mRNA expression patterns using a microarray approach.
Raw data generated from GeneChip probe arrays can be analyzed using the Affymetrix (MAS)4.0 and MAS5.0 (Microarray Analysis Suite) software.
Microarray Analysis Suite software is able to calculate a variety many measurements using the hybridization intensities measured by the scanner from the probe arrays.
Some Affymetrix analysis algorithms
Raw Data Analysis
The intensity from the entire probe array is used for Background and Noise calculations in raw data analysis.
Other metrics compare the intensities of the sequence-specific Perfect Match (PM) probe cells with their control Mismatch (MM) probe cells for each probe set to determine if a transcript is Present (P), Marginal (M), or Absent (A). Next, the numbers of Positive and Negative probe pairs are determined for every probe set. The numbers of Positive and Negative probe sets are then used to derive metrics that describe the performance of each probe set, the Positive Fraction and the Pos/Neg Ratio. Raw data analysis also results in Absence and Presence Calls, Average difference, and Expression Levels.
Comparison analysis gives Normalization and Scaling, Fold Change Calculation and Correlation Coefficients.
Functional Relationships can be assayed by using several methods including:
- Listing genes by changes in their expression including fold-Increase or Decrease
- Phylogenetic Trees
- Self-Organizing Maps
- Relevance Networks
Protein Expression Analysis
See protein expression analysis for more information on this topic.
Gene Expression Bioinformatics Forum
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