The 65 significant genes (133 overlapped probes) which were differentially regulated in H99 compared to environmental strains were analyzed. Shown were representative of each annotation cluster detected. Count represents number of genes which match the pathway database, and % represents the percentage of gene hits among the total genes in the pathway database. Enrichment score (ES) of each group was measured by the geometric mean of the EASE Scores (modified Fisher Exact) associated with the enriched annotation terms that belong to this gene group. Population hit (Pop Hits) represents how many have the function name in your gene list of interest, and population total (Pop Total) represents how many genes in overall population has that function name in the background genome (all genes in the species of interest in DAVID database). False discovery rate (FDR) represents the percentages of test which might be false positive. P values were analyzed using Fisher exact score to identify which sub-populations are over- or under-represented in a sample. Data were considered significant if *P

PathMe Framework Workflow. The PathMe software package facilitates the transformation of pathway content into BEL. The initial step consists of extracting, parsing and/or querying content from each pathway database to retrieve entities, concepts, interactions and reactions and their associated meta-data. Subsequently, database specific identifiers for all entities are unified to stable and consistent ones, where possible. Data is then directly mapped into equivalent BEL nodes and edges, translating all human pathways from the databases into BEL. Finally, an interactive pathway viewer is implemented such that any combination of BEL networks can be explored and the consensus surrounding pathway knowledge can be directly compared.

KEGG Pathway Database (Kanehisa, 2019; Kanehisa et al., 2025; Kanehisa & Goto, 2000) was used to collect genes on insulin signaling, lipid metabolism and inflammation pathways, underlying metabolic disorders common to Alzheimer's Disease and obesity. The prefix “hsa” was selected for human data, and three search terms were inserted: “inflammation”, “insulin” or “lipid”. The pathways with general descriptions surrounding the search terms were selected (Supplementary Material 1), and the section “Gene” was exported manually. Pathways with descriptions surrounding specific diseases or molecules were excluded, as well as duplicate genes, resulting in a total of 1879 genes (Supplementary Material 2).

PLANT REACTOME is an open-source, open access, manually curated and peer-reviewed pathway database. Pathway annotations are authored by expert biologists, in collaboration with the Reactome editorial staff and cross-referenced to many bioinformatics databases. These include project databases like Gramene, Ensembl, UniProt, ChEBI small molecule databases, PubMed, and Gene Ontology.

Bioinformatics resource system including web server and web service for functional annotation and enrichment analyses of gene lists. Consists of comprehensive knowledgebase and set of functional analysis tools. Includes gene centered database integrating heterogeneous gene annotation resources to facilitate high throughput gene functional analysis., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

The complement activation takes place through one or more of the well-established (alternative, classical or lectin) pathways consisting of plasma and membrane-bound proteins. All three pathways converge at the level of complement C3 (doi:10.6072/H0.MP.A004235.01) and are controlled by regulators (doi:10.1038/ni.1923). Complement C3 belongs to the alpha-2-macroglobulin family of proteins, and consists of a alpha-chain and an beta-chain. Cleavage of C3 which can be initiated by one or more of the above three distinct pathways, into C3b [Proteolysis@23-667,749-1663] and C3a [Proteolysis@672-748] is an important step in the complement activation cascade. Classical and lectin pathways, when activated with recognition of pathogens (or immune complexes) use C3-convertase [C4b2a] to cleave complement C3 into C3a and C3b (doi:10.1084/jem.125.2.359). However, in alternative pathway a small fraction of the C3 molecules are hydrolyzed to C3(H20) exposing new binding sites. This hydrated C3 [C3(H20)] recruits complement factor B [fB], which is then cleaved by complement factor D [fD] to result in formation of the minor form of C3-convertase [C3(H20)Bb] that cleaves C3 into C3a and C3b (doi:10.1084/jem.154.3.856). Further, addition of C3b to C3 convertase [C3bBb or C4b2a] results in C5 convertase [C3bBb3b or C4b2a3b], that cleaves complement C5 to C5a and C5b, is the last enzymatic step of the complement activation cascade (doi:10.1074/jbc.273.27.16828). During complement activation C5b interacts with complement C6, C7, C8 and C9 in a sequential and non-catalyzed manner to result in the formation of Terminal Complement Complex (TCC) (doi:10.1074/jbc.M111.219766). The entire network is considered as a simple recognition and elimination system of host-immune complexes and apoptotic and/or pathogens, and therefore promotes host immune homeostasis. The complement system is also involved in cross-talk with other processes related to coagulation, lipid metabolism and cancer. However, many pathogens counteract complement attack through a range of different mechanisms, such acquisition of host complement regulators to the surface of pathogen, or secretion of complement inactivation factors. In order to have a holistic view of the entire complement network, Dr. John D.Lambris group (University of Pennsylvania) developed the Complement Map Database (CMAP) which is a unique repository focused on documented molecular interactions described within the complement cascade and between complement and other biological systems. Information contained in CMAP (see doi:10.1093/bioinformatics/btt269) is entirely based on published experimental data and is fully revised by experts in the field. Further, the Signaling Gateway Molecule Pages (doi:10.1093/bioinformatics/btr190) has published a curated data on each protein involved in human complement activation pathways (refs. Dinasarapu et al, Chandrasekhar et al, Dinasarapu et al, Dinasarapu et al, Chandrasekhar et al, Chandrasekhar et al, Chandrasekhar et al, Chandrasekhar et al, Chandrasekhar et al, Chandrasekhar et al, Chandrasekhar et al, Dinasarapu et al, Dinasarapu et al, Min et al).

Transcriptomic information (spatiotemporal gene expression profile data) on the postnatal cerebellar development of mice (C57B/6J & ICR). It is a tool for mining cerebellar genes and gene expression, and provides a portal to relevant bioinformatics links. The mouse cerebellar circuit develops through a series of cellular and morphological events, including neuronal proliferation and migration, axonogenesis, dendritogenesis, and synaptogenesis, all within three weeks after birth, and each event is controlled by a specific gene group whose expression profile must be encoded in the genome. To elucidate the genetic basis of cerebellar circuit development, CDT-DB analyzes spatiotemporal gene expression by using in situ hybridization (ISH) for cellular resolution and by using fluorescence differential display and microarrays (GeneChip) for developmental time series resolution. The CDT-DB not only provides a cross-search function for large amounts of experimental data (ISH brain images, GeneChip graph, RT-PCR gel images), but also includes a portal function by which all registered genes have been provided with hyperlinks to websites of many relevant bioinformatics regarding gene ontology, genome, proteins, pathways, cell functions, and publications. Thus, the CDT-DB is a useful tool for mining potentially important genes based on characteristic expression profiles in particular cell types or during a particular time window in developing mouse brains.

IntroductionIt is well known that the presence of diabetes significantly affects the progression of periodontitis and that periodontitis has negative effects on diabetes and diabetes-related complications. Although this two-way relationship between type 2 diabetes and periodontitis could be understood through experimental and clinical studies, information on common genetic factors would be more useful for the understanding of both diseases and the development of treatment strategies.Materials and MethodsGene expression data for periodontitis and type 2 diabetes were obtained from the Gene Expression Omnibus database. After preprocessing of data to reduce heterogeneity, differentially expressed genes (DEGs) between disease and normal tissue were identified using a linear regression model package. Gene ontology and Kyoto encyclopedia of genes and genome pathway enrichment analyses were conducted using R package ‘vsn’. A protein-protein interaction network was constructed using the search tool for the retrieval of the interacting genes database. We used molecular complex detection for optimal module selection. CytoHubba was used to identify the highest linkage hub gene in the network.ResultsWe identified 152 commonly DEGs, including 125 upregulated and 27 downregulated genes. Through common DEGs, we constructed a protein-protein interaction and identified highly connected hub genes. The hub genes were up-regulated in both diseases and were most significantly enriched in the Fc gamma R-mediated phagocytosis pathway.DiscussionWe have identified three up-regulated genes involved in Fc gamma receptor-mediated phagocytosis, and these genes could be potential therapeutic targets in patients with periodontitis and type 2 diabetes.

Objective To screen the shared genes of sepsis and senescence by combining gene expression database (GEO) and machine learning algorithms, and to conduct bioinformatics analysis to understand the comorbid mechanism of sepsis and aging.Methods Sepsis-related genes were obtained from CTD, DisGeNet, and GeneCards, and aging-related genes were obtained from Aging Atlas database. GSE13904, GSE28705, and GSE8121 sepsis microarray data and aging dataset GSE173608 were obtained from the GEO database; Webstalt database was used for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed by CNSknowall, and immune cell infiltration analysis of the expression matrix of shared genes was performed using the CIBERSORTX platform. Finally, the BIDOS database was used to analyze the correlation between the expression level of characteristic genes and the survival time and APACHEII score of sepsis patients.Results There were 151 genes shared between sepsis and senescence, and the characteristic genes of TXN, CCL4, IL7 and SIRT1 were the common diagnostic markers of sepsis and senescence. The main gene sets that were up-regulated by the shared genes included cAMP signaling pathway, chemokine signaling pathway, PD-1 expression and PD-1 cancer checkpoint pathway, phospholipase D signaling pathway, PI3K-Akt signaling pathway, prolactin signaling pathway and other signaling pathways. Immune filtration analysis showed that the high expression of CCL4 was positively correlated with the prognosis of sepsis, and CCL4 was significantly positively correlated with activated natural killer cells. TXN was significantly positively correlated with resting dendritic cells.Conclusion CCL4, TXN, IL7 and SIRT1 can be used as diagnostic biomarkers for sepsis and aging, and are closely related to the pathophysiological process of sepsis and aging.

The uploaded files contain the entire data frame and associated data for the AOP-DB v.2 (Mortensen et al. Nature Data Descriptor, submitted). This record contains the second of two files:File 2)

("aopdb_gi_08-25-2020.sql.tar.gz ") which is the gene_interactions table.This is a tar.gz file for a single data table that contains gene interaction information stored in the AOP-DB. This table needs to be compiled by the user with File 1 of the same project (DOI:10.6084/m9.figshare.13042886) to reconstruct the complete AOP-DB v2 in their local environment.

This datasets collection was first introduced in the article:

Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs. (Bioinformatics 2023)

The collection contains three datasets that contain information about three dynamical properties computed on a set of 483 biochemical pathways downloaded from the BioModels database. The three dynamical properties are:

• robustness
• sensitivity
• monotonicity

The files are organized as follows:

1. The `pathways` directory contains 483 files in .dot format for each biochemical pathway downloaded from the BioModels database (May 2021), represented in Petri net format (see this article for the exact definition). The file name is the ID of the pathway in the BioModels database.
2. The other folders contain one .csv file for each property. A single .csv file contains 4 columns:
   1. `PathwayID`: the ID of the Pathway in the BioModels database
   2. `Input`: the input molecular species on which the property has been assessed
   3. `Output`: the output molecular species on which the property has been assessed
   4. `Property`: the value of the property assessed with numerical simulations on the pathway for that particular input/output species pair.
3. The `loader.py` file is an optional script that allows to use the data in python. The script requires that the libraries `networkx`, `pandas`, and `pydot` are installed in the target machine.

BOCK is a knowledge graph integrating oligogenic disease information (originally from the Oligogenic Disease Database (Natchtegael et al. 2022)) together with multiple biological networks and ontologies.

Compared to more generic knowledge graphs, we selected specifically networks relevant to understand the molecular mechanisms of epistasis, placing genes as the central entities, and focused on trusted resources describing a large set of human genes and their interactions.

All entities in the KG are linked to their source database entry via an URI (Uniform Resource Identifier) to facilitate integrations within larger bioinformatics linked data repositories.

BOCK 2.0 integrates recent versions of the used ontologies and databases, as well as additional pathway-specific (The Reactome Pathway Knowledgebase 2024, Milacic et al.) and tissue-specific information (COXPRESdb v8, Obayashi et al.). Additionally the database used for the coexpression relation between genes, has been replaced by COXPRESdb v8.

We provide BOCK 2.0 in three formats:

1. GraphML (Graph Markup Language): a network format enabling the fast import of the KG by multiple libraries (e.g networkx) and tools (e.g Cytoscape).
2. XML (Extensible Markup Language): a text-encoding system that is human-readable and compatible with many systems.
3. Neo4J import files: tab-separated files that can be easily imported into Neo4J using the neo4j-admin utils.

RaMP Relational Database of Metabolic Pathways Update. 20220808.

Objective To identify the core genes linking human parvovirus B19 (B19V) infection and rheumatoid arthritis (RA) using bioinformatics methods, providing new insights into etiology and targeted therapy.Methods The B19V-infected and control dataset (GSE103460) and the RA patient and healthy control dataset (GSE55235) were downloaded from the GEO database. Differentially expressed genes (DEGs) for B19V infection and RA were identified separately using R language. The intersection of DEGs from both diseases was taken to obtain common genes (co-DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the co-DEGs. A protein-protein interaction (PPI) network was constructed using the STRING database and visualized with Cytoscape to screen for hub genes.Results A total of 772 DEGs (411 up-regulated, 361 down-regulated) were identified in the B19V gene expression profile, and 1,413 DEGs (781 up-regulated, 632 down-regulated) were identified in the RA gene expression profile. The intersection revealed 104 key co-DEGs associated with both B19V and RA. Enrichment analysis indicated that these co-DEGs were significantly involved in pathways related to viral infectious diseases, immune cell differentiation (Th17 cell differentiation), inflammatory signaling (TNF, PI3K-Akt), and various cancers. Finally, the top 10 hub genes were identified based on the Maximal Clique Centrality (MCC) algorithm via the CytoHubba plugin: JUN, FOS, EGR1, DUSP1, FOSB, PTGS2, MYC, CDKN1A, ZFP36, and JUNB.Conclusion This bioinformatics study identifies 10 core genes, including JUN, FOS, EGR1, DUSP1, and FOSB, that are commonly associated with both B19V infection and RA. These genes are primarily enriched in inflammatory stress and immune regulation processes related to pathways such as immune cell differentiation and inflammatory signaling, providing new clues and potential therapeutic targets for elucidating the molecular mechanism by which B19V infection contributes to RA pathogenesis.

Bio Resource for array genes is a free online resource for easy access to collective and integrated information from various public biological resources for human, mouse, rat, fly and c. elegans genes. The resource includes information about the genes that are represented in Unigene clusters. This resource provides interactive tools to selectively view, analyze and interpret gene expression patterns against the background of gene and protein functional information. Different query options are provided to mine the biological relationships represented in the underlying database. Search button will take you to the list of query tools available. This Bio resource is a platform designed as an online resource to assist researchers in analyzing results of microarray experiments and developing a biological interpretation of the results. This site is mainly to interpret the unique gene expression patterns found as biological changes that can lead to new diagnostic procedures and drug targets. This interactive site allows users to selectively view a variety of information about gene functions that is stored in an underlying database. Although there are other online resources that provide a comprehensive annotation and summary of genes, this resource differs from these by further enabling researchers to mine biological relationships amongst the genes captured in the database using new query tools. Thus providing a unique way of interpreting the microarray data results based on the knowledge provided for the cellular roles of genes and proteins. A total of six different query tools are provided and each offer different search features, analysis options and different forms of display and visualization of data. The data is collected in relational database from public resources: Unigene, Locus link, OMIM, NCBI dbEST, protein domains from NCBI CDD, Gene Ontology, Pathways (Kegg, Genmapp and Biocarta) and BIND (Protein interactions). Data is dynamically collected and compiled twice a week from public databases. Search options offer capability to organize and cluster genes based on their Interactions in biological pathways, their association with Gene Ontology terms, Tissue/organ specific expression or any other user-chosen functional grouping of genes. A color coding scheme is used to highlight differential gene expression patterns against a background of gene functional information. Concept hierarchies (Anatomy and Diseases) of MESH (Medical Subject Heading) terms are used to organize and display the data related to Tissue specific expression and Diseases. Sponsors: BioRag database is maintained by the Bioinformatics group at Arizona Cancer Center. The material presented here is compiled from different public databases. BioRag is hosted by the Biotechnology Computing Facility of the University of Arizona. 2002,2003 University of Arizona.

Pathway Prediction tool for UniProtKB data along with comparison results to Rule_Base, HAMAP_Rule and SAAS on multiple prokaryotic organisms.

In this paper, we present KEGGscape a pathway data integration and visualization app for Cytoscape (http://apps.cytoscape.org/apps/keggscape). KEGG is a comprehensive public biological database that contains large collection of human curated pathways. KEGGscape utilizes the database to reproduce the corresponding hand-drawn pathway diagrams with as much detail as possible in Cytoscape. Further, it allows users to import pathway data sets to visualize biologist-friendly diagrams using the Cytoscape core visualization function (Visual Style) and the ability to perform pathway analysis with a variety of Cytoscape apps. From the analyzed data, users can create complex and interactive visualizations which cannot be done in the KEGG PATHWAY web application. Experimental data with Affymetrix E. coli chips are used as an example to demonstrate how users can integrate pathways, annotations, and experimental data sets to create complex visualizations that clarify biological systems using KEGGscape and other Cytoscape apps.

The top 5 GO terms enriched by DEmRNA involved in the ceRNA network.

The volatility of metabolites can influence their biological roles and inform optimal methods for their detection. Yet, volatility information is not readily available for the large number of described metabolites, limiting the exploration of volatility as a fundamental trait of metabolites. Here, we adapted methods to estimate vapor pressure from the functional group composition of individual molecules (SIMPOL.1) to predict the gas-phase partitioning of compounds in different environments. We implemented these methods in a new open pipeline called volcalc that uses chemoinformatic tools to automate these volatility estimates for all metabolites in an extensive and continuously updated pathway database: the Kyoto Encyclopedia of Genes and Genomes (KEGG) that connects metabolites, organisms, and reactions. We first benchmark the automated pipeline against a manually curated data set and show that the same category of volatility (e.g., nonvolatile, low, moderate, high) is predicted for 93% of compounds. We then demonstrate how volcalc might be used to generate and test hypotheses about the role of volatility in biological systems and organisms. Specifically, we estimate that 3.4 and 26.6% of compounds in KEGG have high volatility depending on the environment (soil vs. clean atmosphere, respectively) and that a core set of volatiles is shared among all domains of life (30%) with the largest proportion of kingdom-specific volatiles identified in bacteria. With volcalc, we lay a foundation for uncovering the role of the volatilome using an approach that is easily integrated with other bioinformatic pipelines and can be continually refined to consider additional dimensions to volatility. The volcalc package is an accessible tool to help design and test hypotheses on volatile metabolites and their unique roles in biological systems.

An archived version of the KEGG data directory for use in anvi'o. Can be set up on a computer with an anvi'o installation using the command anvi-setup-kegg-kofams --kegg-archive KEGG_build_2024-08-30_6b658b5c4379.tar.gz (specify a --kegg-data-dir to avoid overriding the default KEGG data directory, if one exists).This is the first anvi'o KEGG snapshot to contain KEGG pathway map PNG and KGML files, which can be used with anvi-draw-kegg-pathways to display data from anvi'o databases in the context of pathway maps.Contains MODULES.db version 4.Hash value of the database is 6b658b5c4379