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LIPID Metabolites And Pathways Strategy (LIPID MAPS®) is a multi-institutional supported website and database that provides access to a large number of globally used lipidomics resources. LIPID MAPS® has internationally led the field of lipid curation, classification, and nomenclature since 2003. We strive to produce new open-access databases, informatics tools and lipidomics-focused training activities will be generated and made publicly available for researchers studying lipids in health and disease. LIPID MAPS® is currently funded by a multi-institutional grant from Wellcome, held jointly by Cardiff University, University of California San Diego, the Babraham Institute Cambridge, and Swansea University, as well as an Innovation Study funded by ELIXIR. This current phase will see that LIPID MAPS® is maintained and importantly, further developed in line with the global demand and development of lipidomics. LIPID MAPS® has an internationally recognized classification system and the largest curated lipid structure database in the world.

BuG@Sbase is a microbial gene expression and comparative genomic database containing microarray datasets.

This site provides access to the output of the institution. Users may set up RSS feeds to be alerted to new content. The interface is in English.

NODE (The National Omics Data Encyclopedia) provides an integrated, compatible, comparable, and scalable multi-omics resource platform that supports flexible data management and effective data release. NODE uses a hierarchical data architecture to support storage of muti-omics data including sequencing data, MS based proteomics data, MS or NMR based metabolomics data, and fluorescence imaging data. Launched in early 2017, NODE has collected and published over 900 terabytes of omics data for researchers from China and all over the world in last three years, 22% of which contains multiple omics data. NODE provides functions around the whole life cycle of omics data, from data archive, data requests/responses to data sharing, data analysis, data review and publish.

mirDNMR is a database for the collection of gene-centered background DNMRs obtained from different methods and population variation data. The database has the following functions: (i) browse and search the background DNMRs of each gene predicted by four different methods, including GC content (DNMR-GC), sequence context (DNMR-SC), multiple factors (DNMR-MF) and local DNA methylation level (DNMR-DM); (ii) search variant frequencies in publicly available databases, including ExAC, ESP6500, UK10K, 1000G and dbSNP and (iii) investigate the DNM burden to prioritize candidate genes based on the four background DNMRs using three statistical methods (TADA, Binomial and Poisson test). In conclusion, mirDNMR can be widely used to identify the genetic basis of sporadic genetic diseases.

The UK Stem Cell Bank (UKSCB) facilitates the use and sharing of quality-controlled stem cell lines to support scientific research and clinical development of stem cell therapies. The work of the UK Stem Cell Bank covers three main areas: banking for all UK-derived human embryonic stem cell lines, research in the standardisation, quality and safety of human pluripotent stem cell (hPSC)-based products, and regulation regarding international best practice, policies and guidelines relating to stem cell use and regulation around the world.

FerrDb V2 contains 1001 ferroptosis regulators and 143 ferroptosis-disease associations manually curated from 3288 articles. Specifically, there are 621 gene regulators, of which 264 are drivers, 238 are suppressors, 9 are markers, and 110 are unclassified genes; and there are 380 substance regulators, with 201 inducers and 179 inhibitors. Compared to FerrDb V1, curated articles increase by > 300%, ferroptosis regulators increase by 175%, and ferroptosis-disease associations increase by 50.5%. Circular RNA and pseudogene are novel regulators in FerrDb V2, and the percentage of non-coding RNA increases from 7.3% to 13.6%. External gene-related data were integrated, enabling thought-provoking and gene-oriented analysis in FerrDb V2.

PDX Finder is an open repository for the upload and storage of clinical, genomic and functional Patient-Derived Xenograph (PDX) data which provides a comprehensive global catalogue of PDX models available for researchers across distributed repository databases. Integrated views are provided for histopathological image data, molecular classification of tumors, host mouse strain metadata, tumor genomic data and metrics on tumor response to chemotherapeutics. The data model for PDX Finder is based on the minimal information standard for PDX models developed in collaboration with a broad range of stakeholders who create and/or use PDX models in basic and pre-clinical cancer research.