Gene set enrichment analysis

After the completion of the Human Genome Project, the problem of how to interpret and analyze it remained. In order to seek out genes associated with diseases, DNA microarrays were used to measure the amount of gene expression in different cells. Microarrays on thousands of different genes were carried out, and comparisons of the results of two different cell categories, e.g. normal cells versus cancerous cells. However, this method of comparison is not sensitive enough to detect the subtle differences between the expression of individual genes, because diseases typically involve entire groups of genes. to focus on the changes of expression in groups of a priori defined gene sets. By doing so, this method resolves the problem of the undetectable, small changes in the expression of single genes. == Methods ==

Gene set enrichment analysis uses a priori gene sets that have been grouped together by their involvement in the same biological pathway, or by proximal location on a chromosome. In GSEA, DNA microarrays, or now RNA-Seq, are still performed and compared between two cell categories, but instead of focusing on individual genes in a long list, the focus is put on a gene set. However, GSEA has now also been criticized for the fact that its null distribution is superfluous, and too difficult to be worth calculating, as well as the fact that its Kolmogorov–Smirnov-like statistic is not as sensitive as the original. Spectral Gene Set Enrichment (SGSE) is a proposed, unsupervised test. The method's founders claim that it is a better way to find associations between MSigDB gene sets and microarray data. The general steps include: 1. Calculating the association between principal components and gene sets. 2. Using the weighted Z-method to calculate the association between the gene sets and the spectral structure of the data. == Tools ==

GSEA uses complicated statistics, so it requires a computer program to run the calculations. GSEA has become standard practice, and there are many websites and downloadable programs that will provide the data sets and run the analysis. MOET Multi-Ontology Enrichment Tool (MOET) is a web-based ontology analysis tool that provides functionality for multiple ontologies, including Disease, GO, Pathway, Phenotype, and Chemical entities (ChEBI) for multiple species, including rat, mouse, human, bonobo, squirrel, dog, pig, chinchilla, naked mole-rat and vervet (green monkey). It outputs a downloadable graph and a list of statistically overrepresented terms in the user's list of genes using hypergeometric distribution. MOET also displays the corresponding Bonferroni correction and odds ratio on the results page. It is simple to use, and results are provided with a few clicks in seconds; no software installations or programming skills are required. In addition, MOET is updated weekly, providing the user with the most recent data for analyses. NASQAR NASQAR (Nucleic Acid SeQuence Analysis Resource) is an open source, web-based platform for high-throughput sequencing data analysis and visualization. GSEA can be run using the R-based clusterProfiler package. NASQAR currently supports GO Term and KEGG Pathway enrichment with all organisms supported by an Org.Db database. PlantRegMap The gene ontology (GO) annotation for 165 plant species and GO enrichment analysis is available. MSigDB The Molecular Signatures Database hosts an extensive collection of annotated gene sets that can be used with most GSEA Software. Broad Institute The Broad Institute website is in cooperation with MSigDB and has a downloadable GSEA software, as well a general tutorial. WebGestalt WebGestalt is a web based gene set analysis toolkit. It supports three well-established and complementary methods for enrichment analysis, including Over-Representation Analysis (ORA), Gene Set Enrichment Analysis (GSEA), and Network Topology-based Analysis (NTA). Analysis can be performed against 12 organisms and 321,251 functional categories using 354 gene identifiers from various databases and technology platforms. Enrichr Enrichr is a gene set enrichment analysis tool for mammalian gene sets. It contains background libraries for transcription regulation, pathways and protein interactions, ontologies including GO and the human and mouse phenotype ontologies, signatures from cells treated with drugs, gene sets associated with human diseases, and expression of genes in different cells and tissues. The background libraries are from over 200 resources and contain over 450,000 annotated gene sets. The tool can be accessed through API and provides different ways to visualize the results. GeneSCF GeneSCF is a real-time based functional enrichment tool with support for multiple organisms and is designed to overcome the problems associated with using outdated resources and databases. Advantages of using GeneSCF: real-time analysis, users do not have to depend on enrichment tools to get updated, easy for computational biologists to integrate GeneSCF with their NGS pipeline, it supports multiple organisms, enrichment analysis for multiple gene list using multiple source database in single run, retrieve or download complete GO terms/Pathways/Functions with associated genes as simple table format in a plain text file. DAVID DAVID is the database for annotation, visualization and integrated discovery, a bioinformatics tool that pools together information from most major bioinformatics sources, with the aim of analyzing large gene lists in a high-throughput manner. DAVID goes beyond standard GSEA with additional functions like switching between gene and protein identifiers on the genome-wide scale, which can have a considerable impact on practical interpretation of results. However, A most recent update was performed in 2021 Metascape integrates pathway enrichment analysis, protein complex analysis, and multi-list meta-analysis into one seamless workflow accessible through a significantly simplified user interface. Metascape maintains analysis accuracy by updating its 40 underlying knowledgebases monthly. Metascape presents results using easy-to-interpret graphics, spreadsheets, and publication quality presentations, and is freely available. AmiGO 2 The Gene Ontology (GO) consortium has also developed their own online GO term enrichment tool, allowing species-specific enrichment analysis versus the complete database, coarser-grained GO slims, or custom references. GREAT Genomic region enrichment of annotations tool (GREAT) is a software which takes advantage of regulatory domains to better associate gene ontology terms to genes. Its primary purpose is to identify pathways and processes that are significantly associated with factor regulating activity. This method maps genes with regulatory regions through a hypergeometric test over genes, inferring proximal gene regulatory domains. It does this by using the total fraction of the genome associated with a given ontology term as the expected fraction of input regions associated with the term by chance. Enrichment is calculated by all regulatory regions, and several experiments were performed to validate GREAT, one of which being enrichment analyses done on 8 ChIP-seq datasets. is mainly used for the functional enrichment and network analysis of Omics data. FuncAssociate The FuncAssociate tool enables Gene Ontology and custom enrichment analyses. It allows inputting ordered sets as well as weighted gene space files for background. InterMine Instances of InterMine automatically provide enrichment analysis for uploaded sets of genes and other biological entities. ToppGene suite ToppGene is a one-stop portal for gene list enrichment analysis and candidate gene prioritization based on functional annotations and protein interactions network. Developed and maintained by the Division of Biomedical Informatics at Cincinnati Children's Hospital Medical Center. QuSAGE Quantitative Set Analysis for Gene Expression (QuSAGE) is a computational method for gene set enrichment analysis. QuSAGE improves power by accounting for inter-gene correlations and quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. The applicability of QuSAGE has been extended to longitudinal studies by adding functionality for general linear mixed models. QuSAGE was used by the NIH/NIAID to identify baseline transcriptional signatures that were associated with human influenza vaccination responses. QuSAGE is available as a R/Bioconductor package. Blast2GO Blast2GO is a bioinformatics platform for functional annotation and analysis of genomic datasets. This tool allows to perform gene set enrichment analysis, among other functions. g:Profiler g:Profiler is a toolset for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. g:Profiler supports close to 500 species and strains, including vertebrates, plants, fungi, insects and parasites. == Applications ==

Genome-wide association studies Single-nucleotide polymorphisms, or SNPs, are single base mutations that may be associated with diseases. One base change has the potential to affect the protein that results from that gene being expressed; however, it also has the potential to have no effect at all. Genome-wide association studies (GWAS) are comparisons between healthy and disease genotypes to try to find SNPs that are overrepresented in the disease genomes, and might be associated with that condition. Before GSEA, the accuracy of genome-wide SNP association studies was severely limited by a high number of false positives. The theory that the SNPs contributing to a disease tend to be grouped in a set of genes that are all involved in the same biological pathway, is what the GSEA-SNP method is based on. This application of GSEA does not only aid in the discovery of disease-associated SNPs, but helps illuminate the corresponding pathways and mechanisms of the diseases. Exome sequences from women who had experienced SPTB were compared to those from females from the 1000 Genome Project, using a tool that scored possible disease-causing variants. Genes with higher scores were then run through different programs to group them into gene sets based on pathways and ontology groups. This study found that the variants were significantly clustered in sets related to several pathways, all suspects in SPTB. This analysis showed significant changes of expression in genes involved in pathways that have not been previously associated with the progression of renal cancer. From this study, GSEA has provided potential new targets for renal cell carcinoma therapy. Schizophrenia GSEA can be used to help understand the molecular mechanisms of complex disorders. Schizophrenia is a largely heritable disorder, but is also very complex, and the onset of the disease involves many genes interacting within multiple pathways, as well the interaction of those genes with environmental factors. For instance, epigenetic changes, like DNA methylation, are affected by the environment, but are also inherently dependent on the DNA itself. DNA methylation is the most well-studied epigenetic change, and was recently analyzed using GSEA in relation to schizophrenia-related intermediate phenotypes. Researchers ranked genes for their correlation between methylation patterns and each of the phenotypes. They then used GSEA to look for an enrichment of genes that are predicted to be targeted by microRNAs in the progression of the disease. Genetic and molecular evidence was sought to support this. Researchers took blood samples from sufferers of depression, and used genome-wide expression data, along with GSEA to find expression differences in gene sets related to inflammatory pathways. This study found that those people who rated with the most severe depression symptoms also had significant expression differences in those gene sets, and this result supports the association hypothesis. and microbe set enrichment analysis (MSEA). Instead of analyzing gene sets, these approaches tests for enrichment of predefined sets of microbial species or genera enabling interpretation of microbial community shifts in terms of higher-level taxonomy or functional roles. == See also ==