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

Pathways identified by the Database for Annotation, Visualization and Integrated Discovery (DAVID version 6.7) in the Kyoto Encyclopedia of Genes and Genomes (KEGG).

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.

ns: non significant

Pathway rankings based on adjusted p-values. Those pathways with positive mean differences show that the gene-gene pairs on average have a higher correlation at a stressed pH, and a lower correlation at an ideal pH. (Full pathway ranking in Supplemental Data). *: Benjamini correction.

Pathway rankings based on adjusted p-values. Those pathways with positive mean differences show that the gene-gene pairs on average have a higher correlation in ER-positive patient samples and a lower correlation in ER-negative patient samples for that pathway. (Full pathway ranking in Supplemental Data). *: Benjamini correction.

Supplemental data for the manuscript

"Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments".

Content

functional_annotations.tar.gz

functional annotations for 10 different functional annotation systems, for 5090 species

genome_info_and_statistics.tar.gz

basic genome info, annotation system statistics, user query statistics

example_user_queries.tar.gz

three example files for user query inputs used in the analysis

README.txt

details about the files and file formats

RNA expression analysis was performed on the corpus luteum tissue at five time points after prostaglandin F2 alpha treatment of midcycle cows using an Affymetrix Bovine Gene v1 Array. The normalized linear microarray data was uploaded to the NCBI GEO repository (GSE94069). Subsequent statistical analysis determined differentially expressed transcripts ± 1.5-fold change from saline control with P ≤ 0.05. Gene ontology of differentially expressed transcripts was annotated by DAVID and Panther. Physiological characteristics of the study animals are presented in a figure. Bioinformatic analysis by Ingenuity Pathway Analysis was curated, compiled, and presented in tables. A dataset comparison with similar microarray analyses was performed and bioinformatics analysis by Ingenuity Pathway Analysis, DAVID, Panther, and String of differentially expressed genes from each dataset as well as the differentially expressed genes common to all three datasets were curated, compiled, and presented in tables. Finally, a table comparing four bioinformatics tools' predictions of functions associated with genes common to all three datasets is presented. These data have been further analyzed and interpreted in the companion article "Early transcriptome responses of the bovine mid-cycle corpus luteum to prostaglandin F2 alpha includes cytokine signaling". Resources in this dataset:Resource Title: Supporting information as Excel spreadsheets and tables. File Name: Web Page, url: http://www.sciencedirect.com/science/article/pii/S2352340917304031?via=ihub#s0070

Gene ontology of biotic and hormonal modules found by the DAVID database.

Underlined are highlighted terms particularly relevant to stress and the phenotypic traits.

List of key KEGG Pathways extracted from differentially expressed genes utilizing DAVID Bioinformatic Resource. Specific pathways are arranged in order of their p-value. Gene names and Fold changes for individual genes were added from the original data set.

BackgroundHepatocellular carcinoma (HCC) is a type of primary liver tumor with poor prognosis and high mortality, and its molecular mechanism remains incompletely understood. This study aimed to use bioinformatics technology to identify differentially expressed genes (DEGs) in HCC pathogenesis, hoping to identify novel biomarkers or potential therapeutic targets for HCC research.MethodsThe bioinformatics analysis of our research mostly involved the following two datasets: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). First, we screened DEGs based on the R packages (limma and edgeR). Using the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were carried out. Next, the protein-protein interaction (PPI) network of the DEGs was built in the STRING database. Then, hub genes were screened through the cytoHubba plug-in, followed by verification using the GEPIA and Oncomine databases. We demonstrated differences in levels of the protein in hub genes using the Human Protein Atlas (HPA) database. Finally, the hub genes prognostic values were analyzed by the GEPIA database. Additionally, using the Comparative Toxicogenomics Database (CTD), we constructed the drug-gene interaction network.ResultsWe ended up with 763 DEGs, including 247 upregulated and 516 downregulated DEGs, that were mainly enriched in the epoxygenase P450 pathway, oxidation-reduction process, and metabolism-related pathways. Through the constructed PPI network, it can be concluded that the P53 signaling pathway and the cell cycle are the most obvious in module analysis. From the PPI, we filtered out eight hub genes, and these genes were significantly upregulated in HCC samples, findings consistent with the expression validation results. Additionally, survival analysis showed that high level gene expression of CDC20, CDK1, MAD2L1, BUB1, BUB1B, CCNB1, and CCNA2 were connected with the poor overall survival of HCC patients. Toxicogenomics analysis showed that only topotecan, oxaliplatin, and azathioprine could reduce the gene expression levels of all seven hub genes.ConclusionThe present study screened out the key genes and pathways that were related to HCC pathogenesis, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of HCC.

To build a reference proteome, we established a BV-2 microglial proteome to a depth of 5494 unique protein groups using a novel strategy that combined FASP, StageTip-based high pH fractionation, and high-resolution mass spectrometry quickly and cost-efficiently. By bioinformatics analysis, the BV-2 proteome is a valuable resource for studies of microglial function, such as in the immune response, inflammatory response, and phagocytosis. Raw files were processed in MaxQuant version 1.2.2.5 and the Andromeda search engine against the IPI mouse database (version 3.87, 59,534 entries) containing both forward and reverse proteins sequences. Carbamidomethylation of cysteines was set as the fixed modification. Oxidation of methionine and acetylation of protein N-term was employed as a variable modification. The first search tolerance was set to 20 ppm, followed by a main search tolerance of 6 ppm. HCD fragment ion mass tolerance was set to 20 ppm. Peptides with a minimum of 6 amino acids were considered for identification. The false discovery rate (FDR) for all peptides, modification sites, and protein identifications were set to 0.01. Gene ontology of identified proteins at FDR < 1% was annotated using the DAVID bioinformatics resource tool and UniprotKB database. Pathway analysis was performed using the KEGG pathway database and Panther pathway database.

Acute kidney injury (AKI) is a global public health concern associated with high morbidity, mortality, and health-care costs, and the therapeutic measures are still limited. This study aims to investigate crucial genes correlated with AKI, and their potential functions, which might contribute to a better understanding of AKI pathogenesis. The high-throughput data GSE52004 and GSE98622 were downloaded from Gene Expression Omnibus; four group sets were extracted and integrated. Differentially expressed genes (DEGs) in the four group sets were identified by limma package in R software. The overlapping DEGs among four group sets were further analyzed by the VennDiagram package, and their potential functions were analyzed by the GO and KEGG pathway enrichment analyses using the DAVID database. Furthermore, the protein–protein interaction (PPI) network was constructed by STRING, and the functional modules of the PPI network were filtered by MCODE and ClusterOne in Cytoscape. Hub genes of overlapping DEGs were identified by Cyto-Hubba and cytoNCA. The expression of 35 key genes was validated by quantitative real-time PCR (qRT-PCR). Western blot and immunofluorescence were performed to validate an important gene Egr1. A total of 722 overlapping DEGs were differentially expressed in at least three group sets. These genes mainly enriched in cell proliferation and fibroblast proliferation. Additionally, 5 significant modules and 21 hub genes, such as Havcr1, Krt20, Sox9, Egr1, Timp1, Serpine1, Edn1, and Apln were screened by analyzing the PPI networks. The 5 significant modules were mainly enriched in complement and coagulation cascades and Metabolic pathways, and the top 21 hub genes were mainly enriched in positive regulation of cell proliferation. Through validation, Krt20 were identified as the top 1 upregulated genes with a log2 (fold change) larger than 10 in all these 35 genes, and 21 genes were validated as significantly upregulated; Egr1 was validated as an upregulated gene in AKI in both RNA and protein level. In conclusion, by integrated analysis of different high-throughput data and validation by experiment, several crucial genes were identified in AKI, such as Havcr1, Krt20, Sox9, Egr1, Timp1, Serpine1, Edn1, and Apln. These genes were very important in the process of AKI, which could be further utilized to explore novel diagnostic and therapeutic strategies.

This dataset provides information about the number of properties, residents, and average property values for Star Dust Trail cross streets in Saint David, AZ.

This dataset provides information about the number of properties, residents, and average property values for David Crockett Trail cross streets in Honey Grove, TX.

This dataset provides information about the number of properties, residents, and average property values for Sunrise Trail cross streets in Mc David, FL.

Biomarkers that predict disease progression might assist the development of better therapeutic strategies for aggressive cancers, such as ovarian cancer. Here, we investigated the role of collagen type XI alpha 1 (COL11A1) in cell invasiveness and tumor formation and the prognostic impact of COL11A1 expression in ovarian cancer. Microarray analysis suggested that COL11A1 is a disease progression-associated gene that is linked to ovarian cancer recurrence and poor survival. Overall design: Whole tumor gene expression profiling was conducted on tissue samples from 60 ovarian cancer patients, and characteristics and clinico-pathological features of the patients are provided. We used several steps to analyze the expression profiles of the samples to identify the genes whose expression values correlate with survival, recurrence and advanced disease stage. First, using hazard ratios from univariate Cox regression analysis, the top 200 survival-related genes were evaluated for intersection with the top 200 recurrence-related genes, and 44 genes were obtained. Second, we examined the 44 genes that met the criteria of fold-change values between advanced stage and early stage samples of greater than 2 or less than 0.5. Ultimately, 17 genes were identified. A heat map of the 17 genes is depicted in the associated publication. Gene ontology and pathway enrichment analyses of the 17 genes were performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). The major cellular component, biological process and molecular function of the 17 genes are associated with the extracellular region, intracellular signaling cascade, and protein binding and bridging, respectively. Two genes, COL11A1 and COL4A6, are involved in ECM-receptor interaction pathways. Notably, COL11A1 displayed the highest fold-change value in ovarian cancer disease progression; therefore, we selected COL11A1 for further experimental analysis.

File S1: Dataset of differently ex-pressed genes;File S2: The output of DIA analysis; File S3: The output of DAVID analysis.

Macroalgae are multicellular, aquatic autotrophs that play vital roles in global climate maintenance and have diverse applications in biotechnology and eco-engineering, which are directly linked to their multicellularity phenotypes. However, their genomic diversity and the evolutionary mechanisms underlying multicellularity in these organisms remain uncharacterized. Here, we sequenced 112 macroalgal genomes from diverse climates and phyla, identifying key genomic features that distinguish them from their microalgal relatives. We found that macroalgae have expanded gene families related to cellular adhesion, extracellular matrix formation, cytoskeletal organization and signaling pathways. We discovered that many of these genes have viral origins and are lineage-specific or conserved among the three major macroalgal phyla: Rhodophyta (red algae), Chlorophyta (green algae) and Ochrophyta (brown algae). Our work reveals genetic determinants of convergent and divergent evolutionary trajectories that have shaped morphological diversity in macroalgae and provides genome-wide frameworks to understand photosynthetic multicellular evolution in marine environments.

Table S1. Functional annotation and metadata for macroalgal species. This is a multi-sheet Excel workbook containing the PFAM count matrix for decontaminated assemblies and their strain metadata, contamination estimates, and source data for the ternary analysis. Related to Figs. 2 and 3.

Table S2. GO enrichment in macroalgal-specific genes. This is a multi-sheet Excel workbook containing enriched GO terms in macroalgal-specific PFAMs conserved in Rhodophyta, Phaeophyta, and Chlorophyta and response screening results comparing means in PFAM counts between divisions. Related to Fig. 3.

Table S3. Comparative genomics of micro- and macroalgae. This is a multi-sheet Excel workbook containing response screening tables comparing PFAM and GO variation in micro- compared to macroalgae. Related to Fig. 4.

Table S4. Unraveling the macroalgal adhesome. This is a multi-sheet Excel workbook containing macroalgal adhesome atlas and response screens among macroalgal phyla and between micro- and macroalgae. Related to Fig. 4.

Table S5. Endogenous viral elements in macroalgae. This is a multi-sheet Excel workbook containing VFAM count matrix and response screening results for comparisons of macroalgal VFAM counts by climate and habitat. This table also includes the EVOP matrix and response screening results comparing EVOPs found in the macroalgal genomes among climates and macroalgal tORFs with EsV-1-7 domains and their codomains. Related to Fig. 5.