Contents
How do you represent gene expression data?
A common method of visualising gene expression data is to display it as a heatmap (Figure 12). The heatmap may also be combined with clustering methods which group genes and/or samples together based on the similarity of their gene expression pattern.
What technique is used for gene expression?
Most of these techniques, including microarray analysis and reverse transcription polymerase chain reaction (RT-PCR), work by measuring mRNA levels. However, researchers can also analyze gene expression by directly measuring protein levels with a technique known as a Western blot.
What is gene network analysis?
Gene network inference is the task of reconstructing regulatory networks among genes from high-throughput (in particular transcriptomic) data. These synthetic data are then analyzed with basic gene network inference methods based on information theory and correlation analysis.
What is gene expression and what are the two stages?
The process of gene expression involves two main stages: Transcription. Translation. Transcription. The production of messenger RNA (mRNA) by the enzyme RNA polymerase, and the processing of the resulting mRNA molecule.
Why is gene expression studied?
The expression studies are directed to detect and quantify messenger RNA (mRNA) levels of a specific gene. Today, many of the techniques for quantification of RNA are deprecated because other new techniques provide more information.
How are gene expression data used in network inference?
Beside gene expression data, the network inference using available heterogeneous -omics data, like transcriptomics, proteomics, interactomics, and metabolomics data, becoming more flexible. Integration of these data and using a priori knowledge can contribute to achieve more reliable comprehension of the regulatory relationships.
How to infer regulatory networks from expression data?
The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed.
How are co expression networks used in RNA sequencing?
With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants.
How is co-expression analysis used to identify genes?
Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease.