TriTrypDB: An integrated functional genomics resource for kinetoplastida.

Parasitic diseases caused by kinetoplastid parasites are a burden to public health throughout tropical and subtropical regions of the world. TriTrypDB (https://tritrypdb.org) is a free online resource for data mining of genomic and functional data from these kinetoplastid parasites and is part of the VEuPathDB Bioinformatics Resource Center (https://veupathdb.org). As of release 59, TriTrypDB hosts 83 kinetoplastid genomes, nine of which, including Trypanosoma brucei brucei TREU927, Trypanosoma cruzi CL Brener and Leishmania major Friedlin, undergo manual curation by integrating information from scientific publications, high-throughput assays and user submitted comments. TriTrypDB also integrates transcriptomic, proteomic, epigenomic, population-level and isolate data, functional information from genome-wide RNAi knock-down and fluorescent tagging, and results from automated bioinformatics analysis pipelines. TriTrypDB offers a user-friendly web interface embedded with a genome browser, search strategy system and bioinformatics tools to support custom in silico experiments that leverage integrated data. A Galaxy workspace enables users to analyze their private data (e.g., RNA-sequencing, variant calling, etc.) and explore their results privately in the context of publicly available information in the database. The recent addition of an annotation platform based on Apollo enables users to provide both functional and structural changes that will appear as 'community annotations' immediately and, pending curatorial review, will be integrated into the official genome annotation

FungiDB: An Integrated Bioinformatic Resource for Fungi and Oomycetes

FungiDB (fungidb.org) is a free online resource for data mining and functional genomics analysis for fungal and oomycete species. FungiDB is part of the Eukaryotic Pathogen Genomics Database Resource (EuPathDB, eupathdb.org) platform that integrates genomic, transcriptomic, proteomic, and phenotypic datasets, and other types of data for pathogenic and non-pathogenic, free-living and parasitic organisms. FungiDB is one of the largest EuPathDB databases containing nearly 100 genomes obtained from GenBank, AspGD, The Broad Institute, JGI, Ensembl, and other sources. FungiDB offers a user-friendly web interface with embedded bioinformatics tools that support custom in silico experiments that leverage FungiDB-integrated data. In addition, a Galaxy-based workspace enables users to generate custom pipelines for large scale data analysis (e.g. RNA-Seq, variant calling, etc.). This review provides an introduction to the FungiDB resources and focuses on available features, tools and queries and how they can be used to mine data across a diverse range of integrated FungiDB datasets and records

EuPathDB: the eukaryotic pathogen genomics database resource

The Eukaryotic Pathogen Genomics Database Resource (EuPathDB, http://eupathdb.org) is a collection of databases covering 170+ eukaryotic pathogens (protists & fungi), along with relevant free-living and non-pathogenic species, and select pathogen hosts. To facilitate the discovery of meaningful biological relationships, the databases couple preconfigured searches with visualization and analysis tools for comprehensive data mining via intuitive graphical interfaces and APIs. All data are analyzed with the same workflows, including creation of gene orthology profiles, so data are easily compared across data sets, data types and organisms. EuPathDB is updated with numerous new analysis tools, features, data sets and data types. New tools include GO, metabolic pathway and word enrichment analyses plus an online workspace for analysis of personal, non-public, large-scale data. Expanded data content is mostly genomic and functional genomic data while new data types include protein microarray, metabolic pathways, compounds, quantitative proteomics, copy number variation, and polysomal transcriptomics. New features include consistent categorization of searches, data sets and genome browser tracks; redesigned gene pages; effective integration of alternative transcripts; and a EuPathDB Galaxy instance for private analyses of a user's data. Forthcoming upgrades include user workspaces for private integration of data with existing EuPathDB data and improved integration and presentation of host–pathogen interactions

Regulation of gene expression in ovarian cancer cells by luteinizing hormone receptor expression and activation

<p>Abstract</p> <p>Background</p> <p>Since a substantial percentage of ovarian cancers express gonadotropin receptors and are responsive to the relatively high concentrations of pituitary gonadotropins during the postmenopausal years, it has been suggested that receptor activation may contribute to the etiology and/or progression of the neoplasm. The goal of the present study was to develop a cell model to determine the impact of luteinizing hormone (LH) receptor (LHR) expression and LH-mediated LHR activation on gene expression and thus obtain insights into the mechanism of gonadotropin action on ovarian surface epithelial (OSE) carcinoma cells.</p> <p>Methods</p> <p>The human ovarian cancer cell line, SKOV-3, was stably transfected to express functional LHR and incubated with LH for various periods of time (0-20 hours). Transcriptomic profiling was performed on these cells to identify LHR expression/activation-dependent changes in gene expression levels and pathways by microarray and qRT-PCR analyses.</p> <p>Results</p> <p>Through comparative analysis on the LHR-transfected SKOV-3 cells exposed to LH, we observed the differential expression of 1,783 genes in response to LH treatment, among which five significant families were enriched, including those of growth factors, translation regulators, transporters, G-protein coupled receptors, and ligand-dependent nuclear receptors. The most highly induced early and intermediate responses were found to occupy a network impacting transcriptional regulation, cell growth, apoptosis, and multiple signaling transductions, giving indications of LH-induced apoptosis and cell growth inhibition through the significant changes in, for example, tumor necrosis factor, Jun and many others, supportive of the observed cell growth reduction in <it>in vitro </it>assays. However, other observations, e.g. the substantial up-regulation of the genes encoding the endothelin-1 subtype A receptor, stromal cell-derived factor 1, and insulin-like growth factor II, all of which are potential therapeutic targets, may reflect a positive mediation of ovarian cancer growth.</p> <p>Conclusion</p> <p>Overall, the present study elucidates the extensive transcriptomic changes of ovarian cancer cells in response to LH receptor activation, which provides a comprehensive and objective assessment for determining new cancer therapies and potential serum markers, of which over 100 are suggested.</p

EuPathDB: the eukaryotic pathogen database

ABSTRACT EuPathDB (http://eupathdb.org) resources include 11 databases supporting eukaryotic pathogen genomic and functional genomic data, isolate data and phylogenomics. EuPathDB resources are built using the same infrastructure and provide a sophisticated search strategy system enabling complex interrogations of underlying data. Recent advances in EuPathDB resources include the design and implementation of a new data loading workflow, a new database supporting Piroplasmida (i.e. Babesia and Theileria), the addition of large amounts of new data and data types and the incorporation of new analysis tools. New data include genome sequences and annotation, strand-specific RNA-seq data, splice junction predictions (based on RNAseq), phosphoproteomic data, high-throughput phenotyping data, single nucleotide polymorphism data based on high-throughput sequencing (HTS) and expression quantitative trait loci data. New analysis tools enable users to search for DNA motifs and define genes based on their genomic colocation, view results from searches graphically (i.e. genes mapped to chromosomes or isolates displayed on a map) and analyze data from columns in result tables (word cloud and histogram summaries of column content). The manuscript herein describes updates to EuPathDB since the previous report published in NAR in 2010

VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center in 2023.

The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) is a Bioinformatics Resource Center funded by the National Institutes of Health with additional funding from the Wellcome Trust. VEuPathDB supports >600 organisms that comprise invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Since 2004, VEuPathDB has analyzed omics data from the public domain using contemporary bioinformatic workflows, including orthology predictions via OrthoMCL, and integrated the analysis results with analysis tools, visualizations, and advanced search capabilities. The unique data mining platform coupled with >3000 pre-analyzed data sets facilitates the exploration of pertinent omics data in support of hypothesis driven research. Comparisons are easily made across data sets, data types and organisms. A Galaxy workspace offers the opportunity for the analysis of private large-scale datasets and for porting to VEuPathDB for comparisons with integrated data. The MapVEu tool provides a platform for exploration of spatially resolved data such as vector surveillance and insecticide resistance monitoring. To address the growing body of omics data and advances in laboratory techniques, VEuPathDB has added several new data types, searches and features, improved the Galaxy workspace environment, redesigned the MapVEu interface and updated the infrastructure to accommodate these changes

VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center

The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) represents the 2019 merger of VectorBase with the EuPathDB projects. As a Bioinformatics Resource Center funded by the National Institutes of Health, with additional support from the Welllcome Trust, VEuPathDB supports &gt;500 organisms comprising invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Designed to empower researchers with access to Omics data and bioinformatic analyses, VEuPathDB projects integrate &gt;1700 pre-analysed datasets (and associated metadata) with advanced search capabilities, visualizations, and analysis tools in a graphic interface. Diverse data types are analysed with standardized workflows including an in-house OrthoMCL algorithm for predicting orthology. Comparisons are easily made across datasets, data types and organisms in this unique data mining platform. A new site-wide search facilitates access for both experienced and novice users. Upgraded infrastructure and workflows support numerous updates to the web interface, tools, searches and strategies, and Galaxy workspace where users can privately analyse their own data. Forthcoming upgrades include cloud-ready application architecture, expanded support for the Galaxy workspace, tools for interrogating host-pathogen interactions, and improved interactions with affiliated databases (ClinEpiDB, MicrobiomeDB) and other scientific resources, and increased interoperability with the Bacterial &amp; Viral BRC

Gene expression profiling of epithelial ovarian tumours correlated with malignant potential

© 2004 Warrenfeltz et al; licensee BioMed Central Ltd.The electronic version of this article is the complete one and can be found online at: http://www.molecular-cancer.com/content/3/1/27DOI: 10.1186/1476-4598-3-27Background Epithelial ovarian tumours exhibit a range of malignant potential, presenting distinct clinical phenotypes. Improved knowledge of gene expression changes and functional pathways associated with these clinical phenotypes may lead to new treatment targets, markers for early detection and a better understanding of disease progression. Results Gene expression profiling (Affymetrix, U95Av2) was carried out on 18 ovarian tumours including benign adenomas, borderline adenocarcinomas of low malignant potential and malignant adenocarcinomas. Clustering the expression profiles of samples from patients not treated with chemotherapy prior to surgery effectively classified 92% of samples into their proper histopathological group. Some cancer samples from patients treated with chemotherapy prior to surgery clustered with the benign adenomas. Chemotherapy patients whose tumours exhibited benign-like expression patterns remained disease free for the duration of this study as indicated by continued normal serum CA-125 levels. Statistical analysis identified 163 differentially expressed genes: 61 genes under-expressed in cancer and 102 genes over-expressed in cancer. Profiling the functional categories of co-ordinately expressed genes within this list revealed significant correlation between increased malignant potential and loss of both IGF binding proteins and cell adhesion molecules. Interestingly, in several instances co-ordinately expressed genes sharing biological function also shared chromosomal location. Conclusion Our findings indicate that gene expression profiling can reliably distinguish between benign and malignant ovarian tumours. Expression profiles of samples from patients pre-treated with chemotherapy may be useful in predicting disease free survival and the likelihood of recurrence. Loss of expression of IGF binding proteins as well as specific cell adhesion molecules may be a significant mechanism of disease progression in ovarian cancer. Expression levels in borderline tumours were intermediate between benign adenomas and malignant adenocarcinomas for a significant portion of the differentially expressed genes, suggesting that borderline tumours are a transitional state between benign and malignant tumours. Finally, genes displaying coordinated changes in gene expression were often genetically linked, suggesting that changes in expression for these genes are the consequence of regional duplications, deletions or epigenetic events