Organ-on-a-chip (OOC) provides microphysiological conditions on a microfluidic chip, which makes up for the shortcomings of traditional in vitro cellular culture models and animal models. It has broad application prospects in drug development and screening, toxicological mechanism research, and precision medicine. A large amount of data could be generated through its applications, including image data, measurement data from sensors, ~omics data, etc. A database with proper architecture is required to help scholars in this field design experiments, organize inputted data, perform analysis, and promote the future development of novel OOC systems. In this review, we overview existing OOC databases that have been developed, including the BioSystics Analytics Platform (BAP) developed by the University of Pittsburgh, which supports study design as well as data uploading, storage, visualization, analysis, etc., and the organ-on-a-chip database (Ocdb) developed by Southeast University, which has collected a large amount of literature and patents as well as relevant toxicological and pharmaceutical data and provides other major functions. We used examples to overview how the BAP database has contributed to the development and applications of OOC technology in the United States for the MPS consortium and how the Ocdb has supported researchers in the Chinese Organoid and Organs-On-A-Chip society. Lastly, the characteristics, advantages, and limitations of these two databases were discussed.

Organs-on-a-Chip is a microfluidic microphysiological system that uses microfluidic technology to make high-resolution and real-time imaging analysis on the structure and function of living human cells at the level of tissue and organ in vitro. Compared with the traditional two-dimensional cell culture model and animal model, organs-on-a-chip technology can simulate the pathological and toxicological interactions between different organs or tissues more closely and reflect the collaborative response of multiple organs to drugs. Although lots of organs-on-a-chip-related literature have been published, none of current databases have achieved all the following functionalities yet: searching, downloading and analyzing data and results from literature of organs-on-a-chip. To address this need, we established a database named organs-on-a-chip database (OOCDB), as a platform to integrate information related to organs-on-a-chip from various sources: literature, patents, microarray and transcriptome sequencing raw data, many open access data of organs-on-a-chip and organoids, as well as the data generated in our lab. OOCDB comprises dozens of sub databases and analysis tools and each sub database contains a number of data related to organs-on-a-chip, aiming to provide a comprehensive, systematic and convenient search engine for researchers. In addition, it provides functions such as mathematical modeling, three-dimensional model and citation map to meet the needs of researchers and to promote the development of organs-on-a-chip. The organs-on-a-chip database can be visited at http://www.organchip.cn.

As space exploration programs progress, manned space missions will become more frequent and farther away from Earth, putting a greater emphasis on astronaut health. Through the collaborative efforts of researchers from various countries, the effect of the space environment factors on living systems is gradually being uncovered. Although a large number of interconnected research findings have been produced, their connection seems to be confused, and many unknown effects are left to be discovered. Simultaneously, several valuable data resources have emerged, accumulating data measuring biological effects in space that can be used to further investigate the unknown biological adaptations. In this review, the previous findings and their correlations are sorted out to facilitate the understanding of biological adaptations to space and the design of countermeasures. The biological effect measurement methods/data types are also organized to provide references for experimental design and data analysis. To aid deeper exploration of the data resources, we summarized common characteristics of the data generated from longitudinal experiments, outlined challenges or caveats in data analysis and provided corresponding solutions by recommending bioinformatics strategies and available models/tools.

Motivation The human major histocompatibility complex (MHC), also known as human leukocyte antigen (HLA), plays an important role in the adaptive immune system by presenting non-self-peptides to T cell receptors. The MHC region has been shown to be associated with a variety of diseases, including autoimmune diseases, organ transplantation and tumours. However, structural analytic tools of HLA are still sparse compared to the number of identified HLA alleles, which hinders the disclosure of its pathogenic mechanism. Result To provide an integrative analysis of HLA, we first collected 1296 amino acid sequences, 256 protein data bank structures, 120 000 frequency data of HLA alleles in different populations, 73 000 publications and 39 000 disease-associated single nucleotide polymorphism sites, as well as 212 modelled HLA heterodimer structures. Then, we put forward two new strategies for building up a toolkit for transplantation and tumour immunotherapy, designing risk alignment pipeline and antigenic peptide prediction pipeline by integrating different resources and bioinformatic tools. By integrating 100 000 calculated HLA conformation difference and online tools, risk alignment pipeline provides users with the functions of structural alignment, sequence alignment, residue visualization and risk report generation of mismatched HLA molecules. For tumour antigen prediction, we first predicted 370 000 immunogenic peptides based on the affinity between peptides and MHC to generate the neoantigen catalogue for 11 common tumours. We then designed an antigenic peptide prediction pipeline to provide the functions of mutation prediction, peptide prediction, immunogenicity assessment and docking simulation. We also present a case study of hepatitis B virus mutations associated with liver cancer that demonstrates the high legitimacy of our antigenic peptide prediction process. HLA3D, including different HLA analytic tools and the prediction pipelines, is available at http://www.hla3d.cn/.

Chronic hepatitis B virus (HBV), a potentially life-threatening liver disease, makes people vulnerable to serious diseases such as cancer. T lymphocytes play a crucial role in clearing HBV virus, while the pathway depends on the strong binding of T cell epitope peptide and HLA. However, the experimental identification of HLA-restricted HBV antigenic peptides is extremely time-consuming. In this study, we provide a novel prediction strategy based on structure to assess the affinity between the HBV antigenic peptide and HLA molecule. We used residue scanning, peptide docking and molecular dynamics methods to obtain the molecular docking model of HBV peptide and HLA, and then adopted the MM-GBSA method to calculate the binding affinity of the HBV peptide–HLA complex. Overall, we collected 59 structures of HLA-A from Protein Data Bank, and finally obtained 352 numerical affinity results to figure out the optimal bind choice between the HLA-A molecules and 45 HBV T cell epitope peptides. The results were highly consistent with the qualitative affinity level determined by the competitive peptide binding assay, which confirmed that our affinity prediction process based on an HLA structure is accurate and also proved that the homologous modeling strategy for HLA-A molecules in this study was reliable. Hence, our work highlights an effective way by which to predict and screen for HLA-peptide binding that would improve the treatment of HBV infection.

Brain science, an emerging, dynamic, multidisciplinary basic research field, is generating numerous valuable data. However, there are still several obstacles for the utilization of these data, such as data fragmentation, heterogeneity, availability, and annotation divergence. Thus, to overcome these obstacles and construct an online community, we developed a panoramic database named Brain Research Hotspot Database (BRHD). As of 30 January 2022, the database had been integrated with standardized vocabularies from various resources, including 423,681 papers, 46,344 patents, 9585 transcriptomic datasets, 261 cell markers, as well as with information regarding brain initiatives that were officially launched and well-known scholars in brain research. Based on the keywords entered by users and the search options they set, data can be accessed and retrieved through exact and fuzzy search scenarios. In addition, for brain diseases, we developed three featured functions based on deep data mining: (1) a brain disease–genome network, which collects the associations between common brain diseases, genes, and mutations reported in the literature; (2) brain and gut microbiome associations, based on the literature related to this topic, with added annotations for reference; (3) 3D brain structure, containing a high-precision brain anatomy model with visual links to quickly connect to an organ-on-a-chip database. In short, the BRHD integrates data from a variety of brain science resources to provide a friendly user interface and freely accessible viewing and downloading environment. Furthermore, the original functions developed based on these data provide references and insights for brain research.