Gene Set Analysis Tools Research Articles

Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets.

While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets.

Induction of the CtsR regulon improves Xylanase production in Bacillus subtilis

BackgroundThe bacterium Bacillus subtilis is extensively used for the commercial production of enzymes due to its efficient protein secretion capacity. However, the efficiency of secretion varies greatly between enzymes, and despite many years of research, optimization of enzyme production is still largely a matter of trial-and-error. Genome-wide transcriptome analysis seems a useful tool to identify relevant secretion bottlenecks, yet to this day, only a limited number of transcriptome studies have been published that focus on enzyme secretion in B. subtilis. Here, we examined the effect of high-level expression of the commercially important enzyme endo-1,4-β-xylanase XynA on the B. subtilis transcriptome using RNA-seq.ResultsUsing the novel gene-set analysis tool GINtool, we found a reduced activity of the CtsR regulon when XynA was overproduced. This regulon comprises several protein chaperone genes, including clpC, clpE and clpX, and is controlled by transcriptional repression. CtsR levels are directly controlled by regulated proteolysis, involving ClpC and its cognate protease ClpP. When we abolished this negative feedback, by inactivating the repressor CtsR, the XynA production increased by 25%.ConclusionsOverproduction of enzymes can reduce the pool of Clp protein chaperones in B. subtilis, presumably due to negative feedback regulation. Breaking this feedback can improve enzyme production yields. Considering the conserved nature of Clp chaperones and their regulation, this method might benefit high-yield enzyme production in other organisms.

GSA Central-A web platform to perform, learn, and discuss gene set analysis.

Gene Set Analysis (GSA) is one of the most commonly used strategies to analyze omics data. Hundreds of GSA-related papers have been published, giving birth to a GSA field in Bioinformatics studies. However, as the field grows, it is becoming more difficult to obtain a clear view of all available methods, resources, and their quality. In this paper, we introduce a web platform called “GSA Central” which, as its name indicates, acts as a focal point to centralize GSA information and tools useful to beginners, average users, and experts in the GSA field. “GSA Central” contains five different resources: A Galaxy instance containing GSA tools (“Galaxy-GSA”), a portal to educational material (“GSA Classroom”), a comprehensive database of articles (“GSARefDB”), a set of benchmarking tools (“GSA BenchmarKING”), and a blog (“GSA Blog”). We expect that “GSA Central” will become a useful resource for users looking for introductory learning, state-of-the-art updates, method/tool selection guidelines and insights, tool usage, tool integration under a Galaxy environment, tool design, and tool validation/benchmarking. Moreover, we expect this kind of platform to become an example of a “thematic platform” containing all the resources that people in the field might need, an approach that could be extended to other bioinformatics topics or scientific fields.

Bioinformatics analysis of BIRC5 in human cancers.

BackgroundAn inhibitor of apoptosis (IAP) family member, baculoviral IAP repeat containing five (BIRC5) plays an important role in the occurrence and development of tumors. However, the underlying mechanism in human cancers remains unclear.MethodsIn this study, we investigated BIRC5 expression and explored the prognostic value of BIRC5 in different human cancers via bioinformatics analysis, including the databases of Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, GEPIA, DriverDBv3, GeneMANIA, WEB-based Gene Set Analysis Tool (WebGestalt) and TIMER.ResultsIn most human cancers, BIRC5 usually had higher expression compared to normal human tissues. High expression of BIRC5 could increase the mortality of patients with adrenocortical carcinoma (ACC), kidney renal clear cell carcinoma (KIRC), low-grade glioma (LGG), liver hepatocellular carcinoma (LIHC), and lung adenocarcinoma (LUAD) (P<0.05). Cox analysis demonstrated that high BIRC5 expression was an independent factor for poor overall survival (OS) [hazard ratio, (HR) >1, P<0.05]. There were differences in BIRC5 expression in the case of TP53 mutation, different tumor grades, and stages. Interactive genes for BIRC5 mainly participated in apoptosis, cell division, cell cycle, and cancer pathways, strongly suggesting its oncogenic role in promoting cancer cell proliferation and cancer development. In addition, BIRC5 expression exhibited a close correlation with immune infiltration, which was related to the cumulative survival rate, especially in LGG. The elevated expression of BIRC5 could be regulated through TP53 mutation, tumor stage, and tumor grade (P<0.05).ConclusionsAs a result of our findings, BIRC5 appears to be an independent unfavourable prognostic biomarker in human cancers. BIRC5 may become a potential clinical target in the future for the treatment of cancers.

Incorporating regulatory interactions into gene-set analyses for GWAS data: A controlled analysis with the MAGMA tool.

To date, genome-wide association studies have identified thousands of statistically-significant associations between genetic variants, and phenotypes related to a myriad of traits and diseases. A key goal for human-genetics research is to translate these associations into functional mechanisms. Popular gene-set analysis tools, like MAGMA, map variants to genes they might affect, and then integrate genome-wide association study data (that is, variant-level associations for a phenotype) to score genes for association with a phenotype. Gene scores are subsequently used in competitive gene-set analyses to identify biological processes that are enriched for phenotype association. By default, variants are mapped to genes in their proximity. However, many variants that affect phenotypes are thought to act at regulatory elements, which can be hundreds of kilobases away from their target genes. Thus, we explored the idea of augmenting a proximity-based mapping scheme with publicly-available datasets of regulatory interactions. We used MAGMA to analyze genome-wide association study data for ten different phenotypes, and evaluated the effects of augmentation by comparing numbers, and identities, of genes and gene sets detected as statistically significant between mappings. We detected several pitfalls and confounders of such "augmented analyses", and introduced ways to control for them. Using these controls, we demonstrated that augmentation with datasets of regulatory interactions only occasionally strengthened the enrichment for phenotype association amongst (biologically-relevant) gene sets for different phenotypes. Still, in such cases, genes and regulatory elements responsible for the improvement could be pinpointed. For instance, using brain regulatory-interactions for augmentation, we were able to implicate two acetylcholine receptor subunits involved in post-synaptic chemical transmission, namely CHRNB2 and CHRNE, in schizophrenia. Collectively, our study presents a critical approach for integrating regulatory interactions into gene-set analyses for genome-wide association study data, by introducing various controls to distinguish genuine results from spurious discoveries.

GeneSetCluster: a tool for summarizing and integrating gene-set analysis results

BackgroundGene-set analysis tools, which make use of curated sets of molecules grouped based on their shared functions, aim to identify which gene-sets are over-represented in the set of features that have been associated with a given trait of interest. Such tools are frequently used in gene-centric approaches derived from RNA-sequencing or microarrays such as Ingenuity or GSEA, but they have also been adapted for interval-based analysis derived from DNA methylation or ChIP/ATAC-sequencing. Gene-set analysis tools return, as a result, a list of significant gene-sets. However, while these results are useful for the researcher in the identification of major biological insights, they may be complex to interpret because many gene-sets have largely overlapping gene contents. Additionally, in many cases the result of gene-set analysis consists of a large number of gene-sets making it complicated to identify the major biological insights.ResultsWe present GeneSetCluster, a novel approach which allows clustering of identified gene-sets, from one or multiple experiments and/or tools, based on shared genes. GeneSetCluster calculates a distance score based on overlapping gene content, which is then used to cluster them together and as a result, GeneSetCluster identifies groups of gene-sets with similar gene-set definitions (i.e. gene content). These groups of gene-sets can aid the researcher to focus on such groups for biological interpretations.ConclusionsGeneSetCluster is a novel approach for grouping together post gene-set analysis results based on overlapping gene content. GeneSetCluster is implemented as a package in R. The package and the vignette can be downloaded at https://github.com/TranslationalBioinformaticsUnit

Abstract 3206: IQuery: A tool aggregating multiple gene set analysis methods, based on networks and pathways in a community-driven public data repository

Abstract The investigation of gene sets identified in experiments and analyses is a common task for biologists. This includes identification of relevant pathway networks, neighborhoods of protein interactions, and associations with drugs and diseases. However, services that provide these analyses face the following challenges: (1) Curated pathway content tends to lack biological context and lag behind recent findings, as it focuses on consensus biology and is expensive to maintain; (2) User interfaces for network analyses need to be transparent and intuitive, without requiring complex parameter choices; and (3) Users have diverse, changing network analysis needs, requiring an evolving portfolio of methods and data. To address these challenges, we present NDEx Integrated Query (IQuery), a novel and flexible resource for gene set analysis providing enrichment, protein-protein interaction, and protein association analyses based on pathways and networks from NDEx, the Network Data Exchange. IQuery provides researchers with a simple “one click” query interface which runs multiple simultaneous queries without the need to specify the analyses to perform, query parameters, or network data sources. The results are aggregated and presented in a rich, intuitive interface for the user to browse, preview, save, or analyze. For each network, the user can view descriptions, citations, author information and other metadata, and inspect the data associated with each node and edge. The IQuery database is NDEx, a public data commons which is constantly growing via the addition of new public network resources and networks submitted by the authors of publications. The incorporation of published networks in IQuery enables timely coverage of new research as well as clear biological context for each pathway, and fosters cross-discipline insights and collaboration between teams. IQuery is also integrated with Cytoscape, the widely used network visualization and analysis application, enabling query result networks to be seamlessly downloaded to Cytoscape for editing, merging with other networks, annotation with user datasets, or use with any of hundreds of community developed apps. IQuery is built as a modular, web service-oriented platform. The primary IQuery service distributes each query to all component services using a standard API. This enables easy addition of new analyses, including those written by collaborators. This allows IQuery to grow in two ways: Through the growth of the NDEx database, and through the addition of modules providing new gene set analysis tools. In sum, IQuery is a powerful and versatile new network analysis service with collaboration and extensibility at its core, which will allow users to find, access, and analyze networks relevant to their research, and provide opportunities for cross-discipline communication and insight. Citation Format: Dexter R. Pratt, Keiichiro Ono, Sophie Liu, Jing Chen, Christopher Churas. IQuery: A tool aggregating multiple gene set analysis methods, based on networks and pathways in a community-driven public data repository [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3206.

CGPS: A machine learning-based approach integrating multiple gene set analysis tools for better prioritization of biologically relevant pathways

Gene set enrichment (GSE) analyses play an important role in the interpretation of large-scale transcriptome datasets. Multiple GSE tools can be integrated into a single method as obtaining optimal results is challenging due to the plethora of GSE tools and their discrepant performances. Several existing ensemble methods lead to different scores in sorting pathways as integrated results; furthermore, it is difficult for users to choose a single ensemble score to obtain optimal final results. Here, we develop an ensemble method using a machine learning approach called Combined Gene set analysis incorporating Prioritization and Sensitivity (CGPS) that integrates the results provided by nine prominent GSE tools into a single ensemble score (R score) to sort pathways as integrated results. Moreover, to the best of our knowledge, CGPS is the first GSE ensemble method built based on a priori knowledge of pathways and phenotypes. Compared with 10 widely used individual methods and five types of ensemble scores from two ensemble methods, we demonstrate that sorting pathways based on the R score can better prioritize relevant pathways, as established by an evaluation of 120 simulated datasets and 45 real datasets. Additionally, CGPS is applied to expression data involving the drug panobinostat, which is an anticancer treatment against multiple myeloma. The results identify cell processes associated with cancer, such as the p53 signaling pathway (hsa04115); by contrast, according to two ensemble methods (EnrichmentBrowser and EGSEA), this pathway has a rank higher than 20, which may cause users to miss the pathway in their analyses. We show that this method, which is based on a priori knowledge, can capture valuable biological information from numerous types of gene set collections, such as KEGG pathways, GO terms, Reactome, and BioCarta. CGPS is publicly available as a standalone source code at ftp://ftp.cbi.pku.edu.cn/pub/CGPS_download/cgps-1.0.0.tar.gz.

GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data.

Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from “data-to-knowledge-to-innovation,” a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ (geneanalytics.genecards.org), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®—the human gene database; the MalaCards—the human diseases database; and the PathCards—the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®—the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene–tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell “cards” in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics, and others yet to emerge on the postgenomics horizon.

JAG: A Computational Tool to Evaluate the Role of Gene-Sets in Complex Traits.

Gene-set analysis has been proposed as a powerful tool to deal with the highly polygenic architecture of complex traits, as well as with the small effect sizes typically found in GWAS studies for complex traits. We developed a tool, Joint Association of Genetic variants (JAG), which can be applied to Genome Wide Association (GWA) data and tests for the joint effect of all single nucleotide polymorphisms (SNPs) located in a user-specified set of genes or biological pathway. JAG assigns SNPs to genes and incorporates self-contained and/or competitive tests for gene-set analysis. JAG uses permutation to evaluate gene-set significance, which implicitly controls for linkage disequilibrium, sample size, gene size, the number of SNPs per gene and the number of genes in the gene-set. We conducted a power analysis using the Wellcome Trust Case Control Consortium (WTCCC) Crohn’s disease data set and show that JAG correctly identifies validated gene-sets for Crohn’s disease and has more power than currently available tools for gene-set analysis. JAG is a powerful, novel tool for gene-set analysis, and can be freely downloaded from the CTG Lab website.

MAGMA: generalized gene-set analysis of GWAS data.

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn’s Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn’s Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn’s Disease data was found to be considerably faster as well.

Differentially Expressed Genes and Pathways in Endothelial Cells Exposed to Hemin or Plasma from Patients with Sickle Cell Disease: A Meta-Analysis of Gene Expression Studies

Introduction: Vaso-occlusion and chronic hemolysis are recognized as the most important pathogenic mechanisms of sickle cell disease (SCD), feeding a vicious circle that leads to acute and chronic complications. Although phenotypic aspects of this pro-inflammatory state have been described in detail, much less is known about the upstream pathways that activate and perpetuate inflammation in SCD. It has been known for more than 50 years that patients with SCD present higher plasma concentrations of heme. More recently, the role of heme as a mediator of inflammation in SCD has been confirmed in relevant models, suggesting that free heme can be a trigger for both microvascular occlusion and acute chest syndrome (ACS). In the past, microarray-based gene expression experiments have been used to study the effects of heme on endothelial cells (EC), as well as gene expression signatures of SCD. These studies can be analyzed in combination, since original raw data are now collected in public archives. In fact, it has been shown that by analyzing data from multiple experiments by meta-analysis, biases and artifacts between datasets can be cancelled out, potentially allowing true relationships to stand out. In order to gain insights into the cellular and molecular pathways activated by heme in endothelial cells (EC), as well as about their potential relevance in SCD, we performed a meta-analysis of microarray-based gene expression studies involving heme, EC and SCD.Material and methods: Microarray data were identified by searching two public databases (GEO and Array-Express) using the following search criteria: (“sickle cell disease” and “homo sapiens”). Eleven studies were identified, of which two were selected for our meta-analysis (GSE1849; GSE25014). One study evaluated the effect of heme 5µM in human primary pulmonary artery (PAECs) and microvascular EC (PMVECs) (12 samples), while the other study evaluated the effect of plasma from SCD patients (9 patients in steady state and 12 patients during ACS) in PAECs. To perform the meta-analysis we used INMEX, an integrative web-based tool for meta-analysis of expression data. For the meta-analysis, we applied a combining rank orders method based in the RankProd package. Genes with expression fold-changes (FC) in the same direction (either up or down) of 1.4 in at least one study were selected as candidates for differentially expressed (cDE) genes. Selected genes were ranked based on p value, and a p value ≤0.05 was considered statistically significant. To further understand functions of the subset of genes that were cDE in both studies, we performed gene ontology enrichment analysis. The functional analysis was undertaken using INMEX, and confirmed in other three gene set analysis tools (Pathway Commons, WikiPathways and KEGG). Only pathways that were identified in more than one tool were considered in the analysis.Results: Two different meta-analysis were performed. Gene expression data from heme-stimulated EC was compared to: (i) data from EC stimulated by plasma from SCD patients at steady-state; or (ii) data from EC stimulated by plasma obtained during ACS. In the first (heme x steady-state) and second (heme x ACS) analysis, 799 and 786 genes were consistently up- or down-regulated in both studies. The up- and down-regulated genes with the lowest p values were C2CD4A (C2 calcium-dependent domain containing 4A), and KLHL23 (kelch-like family member 23), respectively. In addition, genes associated with depletion of reactive oxygen species and coagulation activation were also identified. The most significant pathways identified in the gene set analysis were “IL5-mediated signaling events” (heme x steady-state; p= 0.0012) and “MAPK signaling pathway” (heme x ACS; p= 0.0073512) respectively.Results and conclusion: Genes and pathways that are DE both in EC stimulated by heme or by plasma from SCD patients could be relevant elements in the pathogenesis of inflammation in SCD. Heme has been shown to increase the generation of ROS and to induce the expression of inflammatory and pro-coagulant proteins by EC. The results of our meta-analysis are consistent with these effects. Therefore, the comprehensive list of genes and pathways identified in our study could help the generation new hypothesis about the mechanisms involved in heme-induced activation and perpetuation of inflammation in SCD. DisclosuresNo relevant conflicts of interest to declare.

Assembling cell context-specific gene sets: a case in cardiomyopathy.

An increasing amount of evidence suggests that canonical pathways and standard molecular signature databases are incomplete and inadequate to model the complex behavior of cell physiology and pathology. Yet, many Gene Set Analysis (GSA) studies still rely on these databases to identify disease biomarkers and molecular mechanisms within a specific cell context. While tremendous effort has been invested in developing GSA tools, there is limited number of studies focusing on de novo assembly of context-specific gene sets as opposed to simply applying GSA using the standard gene set database. In this paper, we propose a pipeline to derive the entire collection of Cell context-Specific Gene Sets (CSGS) from a molecular interaction network, based on the hypothesis that molecular events linked to a specific phenotypic response should cluster within a subnet of interacting genes. Gene sets are assigned using both physical properties of the network and functional annotations of the neighboring nodes. The identified gene sets could provide a precise starting point such that the downstream GSA will cover all functional pathways in this particular cell context and, at the same time, avoid the noise and excessive multiple-hypothesis testing due to inclusion of irrelevant gene sets from the standard database. We applied the pipeline in the context of cardiomyopathy and demonstrated its superiority over MSigDB gene set collection in terms of: (i) reproducibility and robustness in GSA, (ii) effectiveness in uncovering molecular mechanisms associated with cardiomyopathy, and (iii) the performance in distinguishing diseased vs. normal states.

Graphite Web: web tool for gene set analysis exploiting pathway topology

Graphite web is a novel web tool for pathway analyses and network visualization for gene expression data of both microarray and RNA-seq experiments. Several pathway analyses have been proposed either in the univariate or in the global and multivariate context to tackle the complexity and the interpretation of expression results. These methods can be further divided into ‘topological’ and ‘non-topological’ methods according to their ability to gain power from pathway topology. Biological pathways are, in fact, not only gene lists but can be represented through a network where genes and connections are, respectively, nodes and edges. To this day, the most used approaches are non-topological and univariate although they miss the relationship among genes. On the contrary, topological and multivariate approaches are more powerful, but difficult to be used by researchers without bioinformatic skills. Here we present Graphite web, the first public web server for pathway analysis on gene expression data that combines topological and multivariate pathway analyses with an efficient system of interactive network visualizations for easy results interpretation. Specifically, Graphite web implements five different gene set analyses on three model organisms and two pathway databases. Graphite Web is freely available at http://graphiteweb.bio.unipd.it/.

Assembling cell context-specific gene sets: a case in cardiomyopathy

Summary An increasing amount of evidence suggests that canonical pathways and standard molecular signature databases are incomplete and inadequate to model the complex behavior of cell physiology and pathology. Yet, many Gene Set Analysis (GSA) studies still rely on these databases to identify disease biomarkers and molecular mechanisms within a specific cell context. While tremendous effort has been invested in developing GSA tools, there is limited number of studies focusing on de novo assembly of context-specific gene sets as opposed to simply applying GSA using the standard gene set database.In this paper, we propose a pipeline to derive the entire collection of Cell context-Specific Gene Sets (CSGS) from a molecular interaction network, based on the hypothesis that molecular events linked to a specific phenotypic response should cluster within a subnet of interacting genes. Gene sets are assigned using both physical properties of the network and functional annotations of the neighboring nodes. The identified gene sets could provide a precise starting point such that the downstream GSA will cover all functional pathways in this particular cell context and, at the same time, avoid the noise and excessive multiple-hypothesis testing due to inclusion of irrelevant gene sets from the standard database. We applied the pipeline in the context of cardiomyopathy and demonstrated its superiority over MSigDB gene set collection in terms of: (i) reproducibility and robustness in GSA, (ii) effectiveness in uncovering molecular mechanisms associated with cardiomyopathy, and (iii) the performance in distinguishing diseased vs. normal states.

GeneTrail--advanced gene set enrichment analysis

We present a comprehensive and efficient gene set analysis tool, called ‘GeneTrail’ that offers a rich functionality and is easy to use. Our web-based application facilitates the statistical evaluation of high-throughput genomic or proteomic data sets with respect to enrichment of functional categories. GeneTrail covers a wide variety of biological categories and pathways, among others KEGG, TRANSPATH, TRANSFAC, and GO. Our web server provides two common statistical approaches, ‘Over-Representation Analysis’ (ORA) comparing a reference set of genes to a test set, and ‘Gene Set Enrichment Analysis’ (GSEA) scoring sorted lists of genes. Besides other newly developed features, GeneTrail's statistics module includes a novel dynamic-programming algorithm that improves the P-value computation of GSEA methods considerably. GeneTrail is freely accessible at http://genetrail.bioinf.uni-sb.de

GAzer: gene set analyzer

Gene Set Analyzer (GAzer) is a web-based integrated gene set analysis tool covering previously reported parametric and non-parametric models. Based on a simulation test for the reported algorithms, we classified and implemented three main statistical methods consisting of the z-statistic, gene permutation and sample permutation for ten gene set categories including Gene Ontology (GO) for human, mouse, rat and yeast. This tool identifies significantly altered gene sets scored by z-statistics and P-values from the z-test or permutation test and provides q-values and Bonferroni P-values to correct multiple hypothesis testing. GAzer allows users to observe changes in expression of each gene in a gene set or to see the significance of the gene sets containing a gene(s) of interest, thus allowing interactive data analysis both at the gene and gene set level. Moreover, GAzer offers extensive annotation for each gene. The GAzer gene set analyzer is freely available at http://integromics.kobic.re.kr/GAzer/. This can be found on the web page (http://integromics.kobic.re.kr/GAzer/supplement.jsp).