Functional analysis/biological pathways analysis means to dissect and analyze a collection of genes to determine and analyze the genes that are involved in the regulation of specialized biological pathways.
The standard bioinformatics analysis usually result in the lists of variants, transcripts, or genes and some statistic data, e.g., a gene name, fold change, and multiple-testing-corrected P-value for a gene expression study; or a SNP, odds ratio, and P-value for an association to a disease phenotype from a genetic association study.
Mostly, these “gene lists” are derived from tests that examine a single genetic variant or over/under-expression of a single gene at a time between two conditions, i.e., case vs. control, wild type vs mutant.
However, it has been observed that complex phenotypes are not the result of a single gene but reflect abnormalities in the entire cellular network linking the tissues and organ systems. A better understanding of the working of genetic variants, gene expression, DNA binding, and DNA methylation at multiple loci throughout the genome together, in order to influence the presentation of a complex phenotype that may lead to discovery and characterization of unknown biological processes.
Putting the lists of genes into biological context is the basis of “Functional Analysis” or “functional annotation”.
Types of Functional Analysis
Functional Analysis or pathway analysis can be of following types:
It can be defined as the process of gathering information about a gene and describing its biological activity - its various aliases, molecular functions, biological roles, subcellular locations and its expression domains within the organism. Functional annotations can be retrieved from the following databases:
It is a statistical, also known as “enrichment analysis”, that determines whether the genes from pre-defined sets are present, i.e., those belonging to a specific GO term or KEGG pathway, are present more than would be expected, i.e., “over-represented”, in your dataset.
Ingenuity Pathway Analysis
Ingenuity Pathway Analysis (IPA) is an “all-in-one” web-based software application enabling the analysis, integration, and understanding of data from gene expression, miRNA, SNP microarrays and metabolomics, proteomics and RNAseq experiments as well. It includes the analysis of the following pathways:
Canonical pathways - the study of biological pathways generalized or idealized to the pathways that represent general properties or characteristics of a particular signaling module or pathway. These pathways also define the “specific pathways” as those specific tissues, cell lines, etc.
Biological networks - the convenient representation of patterns of interactions between appropriate biological elements, are known as ‘Biological networks’. These include biochemical networks, neural networks and ecological networks.
Upstream transcription factors influencing your gene’s expression.
KEGG stands for Kyoto Encyclopedia of Genes and Genomes (https://www.genome.jp/kegg/). It assigns functional meaning to genes and genomes both at molecular and higher levels. Molecular-level functions are stored in the KEGG Orthology (KO) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. Initially, KEGG was developed for defining nodes of molecular networks but now it has extended the contents and improved the quality irrespective of whether or not the KOs in the three molecular network databases.
Moreover, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving forward towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.
The Gene Ontology (GO) knowledgebase is the world's largest source of information on the functions of genes. This knowledge is both human-readable and machine-readable, and is the foundation for computational analysis of large-scale molecular biology and genetic experiments in biomedical research. The aim of the GO Consortium is to develop a comprehensive, computational model of biological systems, ranging from the molecular to the organism level, across the multiplicity of species in the tree of life.
Gene Ontologies & Annotations
Gene ontologies (GO) involve the study of the network of Biological classes describing the current best representation of the "universe" of biology. The molecular functions, cellular locations, and processes gene products may carry out. GO annotations involve the study of statements, based on specific, traceable scientific evidence, asserting that a specific gene product is a real model of a particular GO CLASS.
Biological Network Analysis
Biological networks are a convenient way of representing complex patterns of interactions between various biological elements. These biological network can be of following types:
Biochemical networks - they represent the molecular-level patterns of interaction and mechanisms of control in the biological cell. The principal types of these networks are metabolic networks, protein-protein networks, and genetic regulatory networks.
Neural networks - these can be represented as a set of vertices, the neurons, connected by two types of directed edges, one for excitatory inputs and one for inhibiting inputs. In practice, neurons are not all the same. This variation can be encoded in our network representation by different types of vertices.
Ecological networks - these are the networks of ecological interactions between species. Species in an ecosystem can interact in different ways: they can eat one another, they can parasitize one another, or they can have any of a variety of mutually advantageous interactions, such as pollination or seed dispersal.
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