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.

Organs-on-a-chip is a microfluidic microphysiological system that uses microfluidic technology to analyze the structure and function of living human cells at the tissue and organ levels in vitro. Organs-on-a-chip technology, as opposed to traditional two-dimensional cell culture and animal models, can more closely simulate pathologic and toxicologic interactions between different organs or tissues and reflect the collaborative response of multiple organs to drugs. Despite the fact that many organs-on-a-chip-related data have been published, none of the current databases have all of the following functions: searching, downloading, as well as analyzing data and results from the literature on organs-on-a-chip. Therefore, we created an organs-on-a-chip database (OOCDB) as a platform to integrate information about organs-on-a-chip from various sources, including literature, patents, raw data from microarray and transcriptome sequencing, several open-access datasets of organs-on-a-chip and organoids, and data generated in our laboratory. OOCDB contains dozens of sub-databases and analysis tools, and each sub-database contains various data associated with organs-on-a-chip, with the goal of providing researchers with a comprehensive, systematic, and convenient search engine. Furthermore, it offers a variety of other functions, such as mathematical modeling, three-dimensional modeling, and citation mapping, to meet the needs of researchers and promote the development of organs-on-a-chip. The OOCDB is available at http://www.organchip.cn.

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/.

BackgroundLungs were initially thought to be sterile. However, with the development of sequencing technologies, various commensal microorganisms, especially bacteria, have been observed in the lungs of healthy humans. Several studies have also linked lung microbes to infectious lung diseases. However, few databases have focused on the metagenomics of lungs to provide microbial compositions and corresponding metadata information. Such a database would be handy for researching and treating lung diseases.MethodsTo provide researchers with a preliminary understanding of lung microbes and their research methods, the LDMD collated nearly 10,000 studies in the literature covering over 30 diseases, gathered basic information such as the sources of lung microbe samples, sequencing methods, and processing software, as well as analyzed the metagenomic sequencing characteristics of lung microbes. Besides, the LDMD also contained data collected in our laboratory.ResultsIn this study, we established the Lung Disease Microorganisms Database (LDMD), a comprehensive database of microbes involved in lung disease. The LDMD offered sequence analysis capabilities, allowing users to upload their sequencing results, align them with the data collated in the database, and visually analyze the results.ConclusionIn conclusion, the LDMD possesses various functionalities that provide a convenient and comprehensive resource to study the lung metagenome and treat lung diseases.