FUNAGE-Pro: comprehensive web server for gene set enrichment analysis of prokaryotes.
Ontology highlight
ABSTRACT:
SUBMITTER: de Jong A
PROVIDER: S-EPMC9252808 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
| S-EPMC4987924 | biostudies-literature
Project description:The structural and conformational organization of chromosomes is crucial for gene expression regulation in eukaryotes and prokaryotes as well. Up to date, gene expression data generated using either microarray or RNA-sequencing are available for many bacterial genomes. However, differential gene expression is usually investigated with methods considering each gene independently, thus not taking into account the physical localization of genes along a bacterial chromosome. Here, we present WoPPER, a web tool integrating gene expression and genomic annotations to identify differentially expressed chromosomal regions in bacteria. RNA-sequencing or microarray-based gene expression data are provided as input, along with gene annotations. The user can select genomic annotations from an internal database including 2780 bacterial strains, or provide custom genomic annotations. The analysis produces as output the lists of positionally related genes showing a coordinated trend of differential expression. Graphical representations, including a circular plot of the analyzed chromosome, allow intuitive browsing of the results. The analysis procedure is based on our previously published R-package PREDA. The release of this tool is timely and relevant for the scientific community, as WoPPER will fill an existing gap in prokaryotic gene expression data analysis and visualization tools. WoPPER is open to all users and can be reached at the following URL: https://WoPPER.ba.itb.cnr.it.
| S-EPMC5570229 | biostudies-literature
Project description:Most complex disease-associated loci mapped by genome-wide association studies (GWAS) are located in non-coding regions. It remains elusive which genes the associated loci regulate and in which tissues/cell types the regulation occurs. Here, we present PCGA (https://pmglab.top/pcga), a comprehensive web server for jointly estimating both associated tissues/cell types and susceptibility genes for complex phenotypes by GWAS summary statistics. The web server is built on our published method, DESE, which represents an effective method to mutually estimate driver tissues and genes by integrating GWAS summary statistics and transcriptome data. By collecting and processing extensive bulk and single-cell RNA sequencing datasets, PCGA has included expression profiles of 54 human tissues, 2,214 human cell types and 4,384 mouse cell types, which provide the basis for estimating associated tissues/cell types and genes for complex phenotypes. We develop a framework to sequentially estimate associated tissues and cell types of a complex phenotype according to their hierarchical relationships we curated. Meanwhile, we construct a phenotype-cell-gene association landscape by estimating the associated tissues/cell types and genes of 1,871 public GWASs. The association landscape is generally consistent with biological knowledge and can be searched and browsed at the PCGA website.
| S-EPMC9252750 | biostudies-literature
Project description:MotivationGene set enrichment analysis (GSEA) is a commonly used algorithm for characterizing gene expression changes. However, the currently available tools used to perform GSEA have a limited ability to analyze large datasets, which is particularly problematic for the analysis of single-cell data. To overcome this limitation, we developed a GSEA package in Python (GSEApy), which could efficiently analyze large single-cell datasets.ResultsWe present a package (GSEApy) that performs GSEA in either the command line or Python environment. GSEApy uses a Rust implementation to enable it to calculate the same enrichment statistic as GSEA for a collection of pathways. The Rust implementation of GSEApy is 3-fold faster than the Numpy version of GSEApy (v0.10.8) and uses >4-fold less memory. GSEApy also provides an interface between Python and Enrichr web services, as well as for BioMart. The Enrichr application programming interface enables GSEApy to perform over-representation analysis for an input gene list. Furthermore, GSEApy consists of several tools, each designed to facilitate a particular type of enrichment analysis.Availability and implementationThe new GSEApy with Rust extension is deposited in PyPI: https://pypi.org/project/gseapy/. The GSEApy source code is freely available at https://github.com/zqfang/GSEApy. Also, the documentation website is available at https://gseapy.rtfd.io/.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9805564 | biostudies-literature
Project description:Biological data analysis often deals with lists of genes arising from various studies. The g:Profiler toolset is widely used for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. The mission of g:Profiler is to provide a reliable service based on up-to-date high quality data in a convenient manner across many evidence types, identifier spaces and organisms. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. The current update provides a better user experience due to a modern responsive web interface, standardised API and libraries. The results are delivered through an interactive and configurable web design. Results can be downloaded as publication ready visualisations or delimited text files. In the current update we have extended the support to 467 species and strains, including vertebrates, plants, fungi, insects and parasites. By supporting user uploaded custom GMT files, g:Profiler is now capable of analysing data from any organism. All past releases are maintained for reproducibility and transparency. The 2019 update introduces an extensive technical rewrite making the services faster and more flexible. g:Profiler is freely available at https://biit.cs.ut.ee/gprofiler.
| S-EPMC6602461 | biostudies-literature
Project description:DAVID is a popular bioinformatics resource system including a web server and web service for functional annotation and enrichment analyses of gene lists. It consists of a comprehensive knowledgebase and a set of functional analysis tools. Here, we report all updates made in 2021. The DAVID Gene system was rebuilt to gain coverage of more organisms, which increased the taxonomy coverage from 17 399 to 55 464. All existing annotation types have been updated, if available, based on the new DAVID Gene system. Compared with the last version, the number of gene-term records for most annotation types within the updated Knowledgebase have significantly increased. Moreover, we have incorporated new annotations in the Knowledgebase including small molecule-gene interactions from PubChem, drug-gene interactions from DrugBank, tissue expression information from the Human Protein Atlas, disease information from DisGeNET, and pathways from WikiPathways and PathBank. Eight of ten subgroups split from Uniprot Keyword annotation were assigned to specific types. Finally, we added a species parameter for uploading a list of gene symbols to minimize the ambiguity between species, which increases the efficiency of the list upload and eliminates confusion for users. These current updates have significantly expanded the Knowledgebase and enhanced the discovery power of DAVID.
| S-EPMC9252805 | biostudies-literature
Project description:BackgroundGene Ontology enrichment analysis provides an effective way to extract meaningful information from complex biological datasets. By identifying terms that are significantly overrepresented in a gene set, researchers can uncover biological features shared by genes. In addition to extracting enriched terms, it is also important to visualize the results in a way that is conducive to biological interpretation.ResultsHere we present FunSet, a new web server to perform and visualize enrichment analysis. The web server identifies Gene Ontology terms that are statistically overrepresented in a target set with respect to a background set. The enriched terms are displayed in a 2D plot that captures the semantic similarity between terms, with the option to cluster terms via spectral clustering and identify a representative term for each cluster. FunSet can be used interactively or programmatically, and allows users to download the enrichment results both in tabular form and in graphical form as SVG files or in data format as JSON or csv. To enhance reproducibility of the analyses, users have access to historical data for the ontology and the annotations. The source code for the standalone program and the web server are made available with an open-source license.
| S-EPMC6598242 | biostudies-literature
Project description:Characterization of gene lists obtained from high-throughput genomic experiments is an essential task to uncover the underlying biological insights. A common strategy is to perform enrichment analyses that utilize standardized biological annotations, such as GO and KEGG pathways, which attempt to encompass all domains of biology. However, this approach provides generalized, static results that may fail to capture subtleties associated with research questions within a specific domain. Thus, there is a need for an application that can provide precise, relevant results by leveraging the latest research. We have therefore developed an interactive web application, Macrophage Annotation of Gene Network Enrichment Tool (MAGNET), for performing enrichment analyses on gene sets that are specifically relevant to macrophages. Using the hypergeometric distribution, MAGNET assesses the significance of overlapping genes with annotations that were curated from published manuscripts and data repositories. We implemented numerous features that enhance utility and user-friendliness, such as the simultaneous testing of multiple gene sets, different visualization options, option to upload custom datasets, and downloadable outputs. Here, we use three example studies compared against our current database of ten publications on mouse macrophages to demonstrate that MAGNET provides relevant and unique results that complement conventional enrichment analysis tools. Although specific to macrophage datasets, we envision MAGNET will catalyze developments of similar applications in other domains of interest. MAGNET can be freely accessed at the URL https://magnet-winterlab.herokuapp.com. Website implemented in Python and PostgreSQL, with all major browsers supported. The source code is available at https://github.com/sychen9584/MAGNET.
| S-EPMC9833518 | biostudies-literature
Project description:BACKGROUND:The Gene Ontology (GO) knowledgebase is the world's largest source of information on the functions of genes. Since the beginning of GO project, various tools have been developed to perform GO enrichment analysis experiments. GO enrichment analysis has become a commonly used method of gene function analysis. Existing GO enrichment analysis tools do not consider tissue-specific information, although this information is very important to current research. RESULTS:In this paper, we built an easy-to-use web tool called TS-GOEA that allows users to easily perform experiments based on tissue-specific GO enrichment analysis. TS-GOEA uses strict threshold statistical method for GO enrichment analysis, and provides statistical tests to improve the reliability of the analysis results. Meanwhile, TS-GOEA provides tools to compare different experimental results, which is convenient for users to compare the experimental results. To evaluate its performance, we tested the genes associated with platelet disease with TS-GOEA. CONCLUSIONS:TS-GOEA is an effective GO analysis tool with unique features. The experimental results show that our method has better performance and provides a useful supplement for the existing GO enrichment analysis tools. TS-GOEA is available at http://120.77.47.2:5678.
| S-EPMC6876092 | biostudies-literature
Project description:BackgroundGene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis.ResultsWe developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study.ConclusionsThe results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.
| S-EPMC3505158 | biostudies-literature