Provides a manually curated repository of cancer genes derived from scientific literature. NCG collects more than 2300 genes whose somatic modification is known or predicted to possess a cancer driver role. Users can annotate the systems-level properties such as evolutionary origin, duplicability, RNA/protein and miRNA/protein interactions. It serves for the study of individual genes and characterization of cancer genes as a group.
Is an improved functional gene network for laboratory mouse, Mus musculus, which is the choice for many biomedical researches. To improve the previous version of MouseNet, a large volume of new microarray data derived from diverse biological contexts has been incorporated. We have also continued to improve machine learning algorithms to infer co-functional links from genomics data. MouseNet v2 now covers 88% of coding genomes with higher accuracy. All of the functional gene networks are released for free and can be searched using the MouseNet v2 web server, which offers a useful resource for mouse, human and other vertebrate genetics.
Reports altered genes in oral cancer. OrCGDB is designed to be optimal for utilization of the information for diagnosis, prognosis and treatment. It has been used to predict the possible role of differentially expressed markers in cell transformation. The database provides the scientist information and external links for the genes involved in oral cancer, interactions between them, and their role in the biology of oral cancer along with clinical relevance.
A probabilistic functional gene network for baker's yeast, Saccharomyces cerevisiae, which has been a major model organism for eukaryotic genetics and cell biology. YeastNet v3 provides a new web interface to run the tools for network-guided hypothesis generations. YeastNet v3 also provides edge information for all data-specific networks (approximately 2 million functional links) as well as the integrated networks. Therefore, users can construct alternative versions of the integrated network by applying their own data integration algorithm to the same data-specific links.
An evolutive and interactive database of flowering time genes. The hand-curated database contains information on 306 genes and links to 1595 publications gathering the work of >4500 authors. Gene/protein functions and interactions within the flowering pathways were inferred from the analysis of related publications, included in the database and translated into interactive manually drawn snapshots. The content of the FLOR-ID database could be easily incremented with modules expanding beyond flowering time, for example to gametophyte development.
An accurate and high-resolution atlas of gene expression and gene co-regulation in human retina. We collected 50 high-quality post-mortem human retinas from donors and performed high-coverage RNA-sequencing analysis to yield a comprehensive RefT of the human retina. Moreover, we exploited inter-individual variability in gene expression to infer a gene co-expression network and to predict, via a guilty-by-association approach, photoreceptor-specific expression of 253 genes. This atlas represents a valuable resource for the research community at large and help in better elucidating pathophysiological processes in the human retina.
A database of soybean co-functional networks and a companion web tool for network-based functional predictions. SoyNet maps 1 940 284 co-functional links between 40 812 soybean genes (covering 72.8% of the coding genome), which were inferred from 21 distinct types of genomics data including 734 microarrays and 290 RNA-seq samples from soybean. SoyNet provides a route to functional investigation of the soybean genome, elucidating genes and pathways of agricultural importance.
An automatically collected database of gene lists, which were reported mostly by experimental studies in various biological and clinical contexts. At the moment, the database covers 3369 gene lists extracted from 2644 papers published in approximately 80 peer-reviewed journals. As input, CCancer accepts a gene list. An enrichment analyses is implemented to generate, as output, a highly informative survey over recently published studies that report gene lists, which significantly intersect with the query gene list. A report on gene pairs from the input list which were frequently reported together by other biological studies is also provided.
Allows various functional genomics analyses. ADEPTUS offers four different types of analysis: (1) analysis of a gene list, (2) analysis of a disease or a tissue, (3) analysis a profile to predict cancer site from mutated genes and ultimately (4) analysis to predict phenotype from expression. This database contains more than 38000 gene expression profiles and more than 100 diseases.
Offers numerous functional interactions and extensive poplar gene functional annotations. PoplarGene is an online database that integrates two network-assisted gene prioritization algorithms, neighborhood-based prioritization and context-based prioritization. This resource can be used to perform gene prioritization and to identify genes underlying traits in a complementary manner. Moreover, it can be utilized for other woody plant proteomes via orthology transfer using two optional orthology mapping algorithms.
A database of cancer gene networks estimated from the publicly available cancer gene expression data. TCNG allows to estimate genome-wide gene networks consisting of more 20,000 genes from gene expression data using nonparametric Bayesian networks. The gene networks are estimated using the Japanese national flagship supercomputer "K computer". This is a result of ISLiM project which aims at developing biological software that utilizes "K computer".
A platform for genome functional annotations and multi-dimensional network analyses in Sorghum (Sorghum bicolor [L.] Moench). SorghumFDB encompassed most information, such as various annotations of whole genome assemblies, miRNA sequences and target genes, common gene families, network constructions using transcriptome data, PPI data and miRNA-target pairs, as well as multiple gene function annotation elements. Visualization tools (Gbrowse, Cytoscape and open-flash-chart) and four analysis-based tools, BLAST, GSEA, motif significance analysis and pattern set, were provided to determine the functional prediction.
A probabilistic functional gene network for Escherichia coli, which is an intensively studied species of bacteria, due to its utility in both exploring the molecular mechanisms underlying fundamental biological processes and manufacturing useful metabolites for the biomedical industry. All integrated cofunctional associations can be downloaded, enabling orthology-based reconstruction of gene networks for other bacterial species as well.
Permits users to access the integrated database of the Genome Network Project. GNP_Y2H is a system that integrates experimental data generated from the project in association with the public databases.
Provides a set of constructed insect pathways. iPathDB is searchable by keywords for species, pathway ID and pathway name. It returns some gene sequences, annotations and a pathway map. The database contains insect pathways generated by iPathCons, a pipeline that uses official gene sets (OGSs) or transcriptomes to proceed. The aim of the database is to employ insect pathways to model human diseases.
A comprehensive knowledgebase for pathway analysis in mouse. Interpretation of high-throughput genomics data based on biological pathways constitutes a constant challenge, partly because of the lack of supporting pathway database. GSKB is a functional genomics knowledgebase in mouse, which includes 33261 pathways and gene sets compiled from 40 sources such as Gene Ontology, KEGG, GeneSetDB, PANTHER, microRNA and transcription factor target genes, etc. In addition, 8747 lists of differentially expressed genes from 2526 published gene expression studies were manually collected and curated to enable the detection of similarity to previously reported gene expression signatures. These two types of data constitute the comprehensive Gene Set Knowledgebase (GSKB), which can be readily used by various pathway analysis software such as gene set enrichment analysis (GSEA).
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