ImmuneData: an integrated data discovery system for immunology data repositories.

To meet the increasing demand for data sharing, data reuse and meta-analysis in the immunology research community, we have developed the data discovery system ImmuneData. The system provides integrated access to five immunology data repositories funded by the National Institute of Allergy and Infectious Diseases, Division of Allergy, Immunology and Transplantation, including ImmPort, ImmuneSpace, ITN TrialShare, ImmGen and IEDB. ImmuneData restructures the data repositories' metadata into a uniform schema using domain experts' knowledge and state-of-the-art Natural Language Processing (NLP) technologies. It comes with a user-friendly web interface, accessible at http://www.immunedata.org/, and a Google-like search engine for biological researchers to find and access data easily. The vast quantity of synonyms used in biomedical research increase the likelihood of incomplete search results. Thus, our search engine converts queries submitted by users into ontology terms, which are then expended by NLP technologies to ensure that the search results will include all synonyms for a particular concept. The system also includes an advanced search function to build customized queries to meet higher-level users' needs. ImmuneData ensures the FAIR principle (Findability, Accessibility, Interoperability and Reusability) of the five data repositories to benefit data reuse in the immunology research community. The data pipeline constructing our system can be extended to other data repositories to build a more comprehensive biological data discovery system. DATABASE URL: http://www.immunedata.org/.

An informatics research platform to make public gene expression time-course datasets reusable for more scientific discoveries.

The exponential growth of genomic/genetic data in the era of Big Data demands new solutions for making these data findable, accessible, interoperable and reusable. In this article, we present a web-based platform named Gene Expression Time-Course Research (GETc) Platform that enables the discovery and visualization of time-course gene expression data and analytical results from the NIH/NCBI-sponsored Gene Expression Omnibus (GEO). The analytical results are produced from an analytic pipeline based on the ordinary differential equation model. Furthermore, in order to extract scientific insights from these results and disseminate the scientific findings, close and efficient collaborations between domain-specific experts from biomedical and scientific fields and data scientists is required. Therefore, GETc provides several recommendation functions and tools to facilitate effective collaborations. GETc platform is a very useful tool for researchers from the biomedical genomics community to present and communicate large numbers of analysis results from GEO. It is generalizable and broadly applicable across different biomedical research areas. GETc is a user-friendly and efficient web-based platform freely accessible at http://genestudy.org/.

Restructured GEO: restructuring Gene Expression Omnibus metadata for genome dynamics analysis.

Motivation: Gene Expression Omnibus (GEO) and other publicly available data store their metadata in the format of unstructured English text, which is very difficult for automated reuse. Results: We employed text mining techniques to analyze the metadata of GEO and developed Restructured GEO database (ReGEO). ReGEO reorganizes and categorizes GEO series and makes them searchable by two new attributes extracted automatically from each series' metadata. These attributes are the number of time points tested in the experiment and the disease being investigated. ReGEO also makes series searchable by other attributes available in GEO, such as platform organism, experiment type, associated PubMed ID as well as general keywords in the study's description. Our approach greatly expands the usability of GEO data, demonstrating a credible approach to improve the utility of vast amount of publicly available data in the era of Big Data research.

RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse.

Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named 'RegNetwork', of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data. Database URL: http://www.regnetworkweb.org.