What is protein co-expression?

What is protein co-expression?

The co-expression of proteins that form a complex. Often the seperate components of a complex are not soluble mostly due to hydrophobic patches that are exposed to the solvent. These patches are usually involved in and protected by the binding to the other component(s) of the complex.

What is co-expression network?

A co-expression network identifies which genes have a tendency to show a coordinated expression pattern across a group of samples. This co-expression network can be represented as a gene–gene similarity matrix, which can be used in downstream analyses (Figure 1).

What is PPI network?

A protein-protein interaction (PPI) network represents a platform by which we have this chance to systematically identify disease-related genes from the relations between genes with similar functions.

What is gene co-expression analysis?

GCNs are a potent approach to gather biologically relevant information, e.g., for the identification of genes not yet associated with explicit biological questions, and for accelerating the interpretation of molecular mechanisms at the root of significant biological processes.

What is an Eigengene?

eigengene (plural eigengenes) (genetics) (mathematics) One of a set of right singular vectors of a genes x samples matrix that tabulates, e.g., the mRNA or gene expression of the genes across the samples.

In what 2 ways can genes be co expressed?

Pearson’s correlation coefficient, Mutual Information, Spearman’s rank correlation coefficient and Euclidean distance are the four mostly used co-expression measures for constructing gene co-expression networks.

Is PPI a network?

Protein-protein interactions (PPIs) are essential to almost every process in a cell, so understanding PPIs is crucial for understanding cell physiology in normal and disease states. Protein-protein interaction networks (PPIN) are mathematical representations of the physical contacts between proteins in the cell.

What are the applications of PPI network?

The promising applications of PPI networks to disease datasets are concentrated on four major areas: (i) the identification of genes and proteins associated with diseases, (ii) the study of network properties and their relation to disease states, (iii) the identification of disease-related subnetworks, and (iv) network …

What are Eigengenes?

What is WGCNA RNA-seq?

WGCNA uses a series of correlations to identify sets of genes that are expressed together in your data set. This is a fairly intuitive approach to gene network analysis which can aid in interpretation of microarray & RNA-seq data.

What is gene module?

A gene module is defined as a set of coexpressed genes to which the same set of transcription factors binds.

How are PPI and co-expression networks complementary?

On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis.

How are protein interactions related to gene expression?

New methodologies have also been developed to increase knowledge about protein-protein interactions (PPIs) [ 6 ]. As a result, the PPI networks combined with protein and with gene expression levels are today widespread to investigate biological systems [ 7 – 10 ].

Why is PPI used to describe physical contact between proteins?

For this reason, the term PPI has not only been exclusively used to indicate a physical contact between proteins, but also proteins connected by functional links. It is important to bear in mind that proteins participate to physical-chemical connection depending on the biological context where they are [ 30 ].

How are genomic and proteomic data related to protein interactions?

Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules.