Understanding the underlying biological procedures and methods related to gene expression and regulation is the goal of RNA-Seq analysis. Biological systems can be depicted as networks of pairwise relationships between biological entities at various levels of the organization, from molecules to biospheres. Biomolecules can interact directly through physical contact or indirectly through causal chains or simple correlations. Networks between DNA–RNA, DNA–Protein, RNA–RNA, RNA–Protein, and Protein-Protein are among the most studied interactomes. Since some components are shared by both, such as common gene, transcript, or protein identifiers, any network of words can theoretically be combined with these interactions. Instead of looking at the components of a cell or an organism as isolated events, the systems biology approach looks at the overall structure and function of the cell or organism. Gene expression in an organism or interaction is viewed as a sum of individual genes, sets of genes, and other compounding variables in the systems biology approach. When analyzing a biological problem as a system rather than an individual problem, gene regulatory networks (GRNs) and co-expression analyses are popular.
There are considerable possibilities to discover networks within and between their available datasets, directing them toward important insights, potential validation experiments, and more holistic comprehension of their studies, given the growing avalanche of RNA-Seq data and the wealth of network analysis (NA) programs. NA of RNA-Seq data can reveal interconnections and functional associations between a variety of elements, including regulators/co-regulators, upstream/downstream sequences, and genic features; differentially expressed subnetworks; and global connectivity among genes and gene networks. Semantic networks, which encompass the relationships between categories of biological meaning, most commonly ontological, that have been delegated to the biomolecules, can offer a more abstracted view of biological systems, which is often merged with the biomolecular interactions. Mathematical and statistical models were used in traditional systems biology. Modern systems biology, on the other hand, relies on computer models that simulate an organism's whole biological system by taking into account all of its components.
RNA-seq analysis does not end with the creation of lists of differential expression (DE) genes. Examining the expression changes of groups of genes can provide additional biological insight into an experimental system. The concept behind this process, known as system biology, is that the whole is greater than the sum of its parts. Two essential parts are pathway analysis and co-expression network analysis.
1. GSEA is a genome-wide expression profiling method based on knowledge.
2. GSVA is an unsupervised, non-parametric method for estimating variation in gene set enrichment using samples from an expression data set.
3. SeqGSEA combines differential expression and splicing to provide methods for gene set enrichment analysis.
4. GAGE is a test of the most cutting-edge large-scale genome assembly algorithms.
5. SPIA identifies the disease's most important pathways.
6. TAPPA is a java-based device for phenotype-associated genetic pathway recognition.
7. DEAP uses differential expression data to identify important regulatory patterns.
8. GSAASeqSP can find pathways or gene sets that are strongly linked to a disease or phenotype.
9. By utilizing massive amounts of publicly available gene expression data, GSCA assists researchers in making discoveries.
1. DICER uses a novel probabilistic score for differential correlation to detect differentially co-expressed gene sets.
2. WGCNA is a useful tool for identifying co-expressed gene groups in microarray or RNA-seq data.