What is gene regulatory network inference?
Interactions between genes are typically represented as a gene regulatory network (GRN) whose nodes correspond to different genes, and a directed edge denotes a direct causal effect of some gene on another gene. The usual research aim is to infer the network topology from given gene expression data.
What are transcription regulatory networks?
Transcriptional regulatory networks (TRNs) encode instructions for animal development and physiological responses. Recent advances in genomic technologies and computational modeling have revolutionized our ability to construct models of TRNs.
What is regulatory network in biology?
Definition. In biology, regulatory networks are sets of macromolecules, mostly proteins and RNAs, that interact to control the level of expression of various genes in a given genome.
What is gene network analysis?
Weighted gene co-expression network analysis (WGCNA) is a bioinformatics application for exploring the relationships between different gene sets (modules), or between gene sets and clinical features (Langfelder and Horvath, 2008).
Why is functional genomics important?
The goal of functional genomics is to determine how the individual components of a biological system work together to produce a particular phenotype. Functional genomics focuses on the dynamic expression of gene products in a specific context, for example, at a specific developmental stage or during a disease.
What is a hub gene?
Hub genes were defined as the genes with connectivity (degree) greater than 10 in the genetic interaction network and, incidentally, are the top 10% genes of highest connectivity (see Supplementary Fig. S3 and Supplementary Table S3 for the distribution of connectivities of genes in the genetic interaction network).
How do gene networks work?
A gene (or genetic) regulatory network (GRN) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins which, in turn, determine the function of the cell.
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 differential network analysis?
(2018), a differential network analysis method for scRNA-seq data is proposed that first determines a sample size corrected gene-gene correlation matrix for each cellular state and then identifies differential gene-gene pairs across the states.
What is the difference between genomics and functional genomics?
Structural genomics involves the physical nature of genomes and includes the sequencing and mapping of genomes. Functional genomics involves studying the expression and function of the genome. Genomics can also involve the investigation of interactions between genes and between genes and the environment.
What techniques are used in functional genomics?
Different techniques that are widely used to understand the gene/protein function include RNA interference (RNAi), mutagenesis, mass spectrometry, genome annotation, and so on.
Where is hub genes WGCNA?
Easiest way: simply use the chooseTopHubInEachModule function from WGCNA… Another way: Convert the WGCNA object to an igraph object directly with wgcna2igraph, and then generate hub centrality scores via Kleinberg’s metric, part of the igraph package.
How do you identify hub genes in cytoscape?
In a co-expression network, Maximal Clique Centrality (MCC) algorithm was reported to be the most effective method of finding hub nodes (25). The MCC of each node was calculated by CytoHubba, a plugin in Cytoscape (25). In this study, the genes with the top 10 MCC values were considered as hub genes.
What is the control region of a gene?
Locus control region, a long-range cis-regulatory element that enhances expression of linked genes at ectopic chromatin sites. Internal control region, a sequence of DNA located with the coding region of eukaryotic genes that binds regulatory elements such as activators or repressors.
What is WGCNA used for?
What is hub gene in WGCNA?
By utilizing the WGCNA algorithm, genes with similar co-expression patterns are classified into a set of modules, in which the most central genes could be further identified as hub genes.
What is a gene network analysis?
What is network based analysis?
Network-based analysis allows researchers to go beyond individual genetic alterations to predict sets of genes that may be contributing to certain cancers.
Which are examples of sequence motifs?
A sequence motif is a nucleotide or amino-acid sequence pattern that is widespread and has, or is conjectured to have, a biological significance….Structural motif examples
- Zinc finger motif. Zinc fingers are a common motif in DNA-binding proteins.
- Four-helix bundle motif.
- Greek motif.
What is Wgcna used for?
What is the MCC from cytoscape?
How do you identify a hub gene?