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Integrative research about multiple biochemical subsystems has significant potential to help advance biology, bioengineering and medicine. However, it is difficult to obtain the diverse data needed for integrative research. To facilitate biochemical research, we developed Datanator (https://datanator.info), an integrated database and set of tools for finding clouds of multiple types of molecular data about specific molecules and reactions in specific organisms and environments, as well as data about chemically-similar molecules and reactions in phylogenetically-similar organisms in similar environments. Currently, Datanator includes metabolite concentrations, RNA modifications and half-lives, protein abundances and modifications, and reaction rate constants about a broad range of organisms. Going forward, we aim to launch a community initiative to curate additional data. Datanator also provides tools for filtering, visualizing and exporting these data clouds. We believe that Datanator can facilitate a wide range of research from integrative mechanistic models, such as whole-cell models, to comparative data-driven analyses of multiple organisms., Graphical Abstract Graphical AbstractDatanator (https://datanator.info) is an integrated database of molecular data. To help investigators find relevant data for research even in the absence of direct measurements, Datanator includes tools for assembling "clouds" of measurements centered on a specific molecule or reaction in a particular organism that encompass measurements of similar molecules and reactions in similar organisms. Datanator is ideal for integrative and comparative analysis and modeling.

High-throughput screening based on CRISPR-Cas9 libraries has become an attractive and powerful technique to identify target genes for functional studies. However, accessibility of public data is limited due to the lack of user-friendly utilities and up-to-date resources covering experiments from third parties. Here, we describe iCSDB, an integrated database of CRISPR screening experiments using human cell lines. We compiled two major sources of CRISPR-Cas9 screening: the DepMap portal and BioGRID ORCS. DepMap portal itself is an integrated database that includes three large-scale projects of CRISPR screening. We additionally aggregated CRISPR screens from BioGRID ORCS that is a collection of screening results from PubMed articles. Currently, iCSDB contains 1375 genome-wide screens across 976 human cell lines, covering 28 tissues and 70 cancer types. Importantly, the batch effects from different CRISPR libraries were removed and the screening scores were converted into a single metric to estimate the knockout efficiency. Clinical and molecular information were also integrated to help users to select cell lines of interest readily. Furthermore, we have implemented various interactive tools and viewers to facilitate users to choose, examine and compare the screen results both at the gene and guide RNA levels. iCSDB is available at https://www.kobic.re.kr/icsdb/.

ViruSurf, available at http://gmql.eu/virusurf/, is a large public database of viral sequences and integrated and curated metadata from heterogeneous sources (GenBank, COG-UK and NMDC); it also exposes computed nucleotide and amino acid variants, called from original sequences. A GISAID-specific ViruSurf database, available at http://gmql.eu/virusurf_gisaid/, offers a subset of these functionalities. Given the current pandemic outbreak, SARS-CoV-2 data are collected from the four sources; but ViruSurf contains other virus species harmful to humans, including SARS-CoV, MERS-CoV, Ebola, and Dengue.The database is centered on sequences, described from their biological, technological, and organizational dimensions. In addition, the analytical dimension characterizes the sequence in terms of its annotations and variants. The web interface enables expressing complex search queries in a simple way; arbitrary search queries can freely combine conditions on attributes from the four dimensions, extracting the resulting sequences.Several example queries on the database confirm and possibly improve results from recent research papers; results can be recomputed over time and upon selected populations. Effective search over large and curated sequence data may enable faster responses to future threats that could arise from new viruses.

In the past few decades, major initiatives have been launched around the world to address chemical safety testing. These efforts aim to innovate and improve the efficacy of existing methods with the long-term goal of developing new risk assessment paradigms. The transcriptomic and toxicological profiling of mammalian cells has resulted in the creation of multiple toxicogenomic datasets and corresponding tools for analysis. To enable easy access and analysis of these valuable toxicogenomic data, we have developed ToxicoDB (toxicodb.ca), a free and open cloud-based platform integrating data from large in vitro toxicogenomic studies, including gene expression profiles of primary human and rat hepatocytes treated with 231 potential toxicants. To efficiently mine these complex toxicogenomic data, ToxicoDB provides users with harmonized chemical annotations, time- and dose-dependent plots of compounds across datasets, as well as the toxicity-related pathway analysis. The data in ToxicoDB have been generated using our open-source R package, ToxicoGx (github.com/bhklab/ToxicoGx). Altogether, ToxicoDB provides a streamlined process for mining highly organized, curated, and accessible toxicogenomic data that can be ultimately applied to preclinical toxicity studies and further our understanding of adverse outcomes.

Nanobodies, a subclass of antibodies found in camelids, are versatile molecular binding scaffolds composed of a single polypeptide chain. The small size of nanobodies bestows multiple therapeutic advantages (stability, tumor penetration) with the first therapeutic approval in 2018 cementing the clinical viability of this format. Structured data and sequence information of nanobodies will enable the accelerated clinical development of nanobody-based therapeutics. Though the nanobody sequence and structure data are deposited in the public domain at an accelerating pace, the heterogeneity of sources and lack of standardization hampers reliable harvesting of nanobody information. We address this issue by creating the Integrated Database of Nanobodies for Immunoinformatics (INDI, http://naturalantibody.com/nanobodies). INDI collates nanobodies from all the major public outlets of biological sequences: patents, GenBank, next-generation sequencing repositories, structures and scientific publications. We equip INDI with powerful nanobody-specific sequence and text search facilitating access to >11 million nanobody sequences. INDI should facilitate development of novel nanobody-specific computational protocols helping to deliver on the therapeutic promise of this drug format.

Over the past decade, many databases focusing on microsatellite mining on a genomic scale were released online with at least one of the following major deficiencies: (i) lacking the classification of microsatellites as genic or non-genic, (ii) not comparing microsatellite motifs at both genic and non-genic levels in order to identify unique motifs for each class or (iii) missing SSR marker development. In this study, we have developed ‘SSRome’ as a web-based, user-friendly, comprehensive and dynamic database with pipelines for exploring microsatellites in 6533 organisms. In the SSRome database, 158 million microsatellite motifs are identified across all taxa, in addition to all the mitochondrial and chloroplast genomes and expressed sequence tags available from NCBI. Moreover, 45.1 million microsatellite markers were developed and classified as genic or non-genic. All the stored motif and marker datasets can be downloaded freely. In addition, SSRome provides three user-friendly tools to identify, classify and compare motifs on either a genome- or transcriptome-wide scale. With the implementation of PHP, HTML and JavaScript, users can upload their data for analysis via a user-friendly GUI. SSRome represents a powerful database and mega-tool that will assist researchers in developing and dissecting microsatellite markers on a high-throughput scale.

Model organism and human databases are rich with information about genetic and physical interactions. These data can be used to interpret and guide the analysis of results from new studies and develop new hypotheses. Here, we report the development of the Molecular Interaction Search Tool (MIST; http://fgrtools.hms.harvard.edu/MIST/). The MIST database integrates biological interaction data from yeast, nematode, fly, zebrafish, frog, rat and mouse model systems, as well as human. For individual or short gene lists, the MIST user interface can be used to identify interacting partners based on protein–protein and genetic interaction (GI) data from the species of interest as well as inferred interactions, known as interologs, and to view a corresponding network. The data, interologs and search tools at MIST are also useful for analyzing ‘omics datasets. In addition to describing the integrated database, we also demonstrate how MIST can be used to identify an appropriate cut-off value that balances false positive and negative discovery, and present use-cases for additional types of analysis. Altogether, the MIST database and search tools support visualization and navigation of existing protein and GI data, as well as comparison of new and existing data.

Noncoding RNAs (ncRNAs) play crucial regulatory roles in a variety of biological circuits. To document regulatory interactions between ncRNAs and biomolecules, we previously created the NPInter database (http://bigdata.ibp.ac.cn/npinter). Since the last version of NPInter was issued, a rapidly growing number of studies have reported novel interactions and accumulated numerous high-throughput interactome data. We have therefore updated NPInter to its fourth edition in which are integrated 600 000 new experimentally identified ncRNA interactions. ncRNA–DNA interactions derived from ChIRP-seq data and circular RNA interactions have been included in the database. Additionally, disease associations were annotated to the interacting molecules. The database website has also been redesigned with a more user-friendly interface and several additional functional modules. Overall, NPInter v4.0 now provides more comprehensive data and services for researchers working on ncRNAs and their interactions with other biomolecules.

EuPathDB (http://EuPathDB.org; formerly ApiDB) is an integrated database covering the eukaryotic pathogens of the genera Cryptosporidium, Giardia, Leishmania, Neospora, Plasmodium, Toxoplasma, Trichomonas and Trypanosoma. While each of these groups is supported by a taxon-specific database built upon the same infrastructure, the EuPathDB portal offers an entry point to all these resources, and the opportunity to leverage orthology for searches across genera. The most recent release of EuPathDB includes updates and changes affecting data content, infrastructure and the user interface, improving data access and enhancing the user experience. EuPathDB currently supports more than 80 searches and the recently-implemented ‘search strategy’ system enables users to construct complex multi-step searches via a graphical interface. Search results are dynamically displayed as the strategy is constructed or modified, and can be downloaded, saved, revised, or shared with other database users.

Small non-coding RNAs (sRNAs) carry out a variety of biological functions and affect protein synthesis and protein activities in prokaryotes. Recently, numerous sRNAs and their targets were identified in Escherichia coli and in other bacteria. It is crucial to have a comprehensive resource concerning the annotation of small non-coding RNAs in microbial genomes. This work presents an integrated database, namely sRNAMap, to collect the sRNA genes, the transcriptional regulators of sRNAs and the sRNA target genes by integrating a variety of biological databases and by surveying literature. In this resource, we collected 397 sRNAs, 62 regulators/sRNAs and 60 sRNAs/targets in 70 microbial genomes. Additionally, more valuable information of the sRNAs, such as the secondary structure of sRNAs, the expressed conditions of sRNAs, the expression profiles of sRNAs, the transcriptional start sites of sRNAs and the cross-links to other biological databases, are provided for further investigation. Besides, various textual and graphical interfaces were designed and implemented to facilitate the data access in sRNAMap. sRNAMap is available at http://sRNAMap.mbc.nctu.edu.tw/.

The Prostate Gene Database (PGDB: http://www.ucsf.edu/pgdb) is a curated and integrated database of genes or genomic loci related to the human prostate and prostatic diseases. Currently, PGDB covers genes involved in a number of molecular and genetic events of the prostate including gene amplification, mutation, gross deletion, methylation, polymorphism, linkage and over-expression, as published in the literature. Genes that are specifically expressed in prostate, as evidenced by analysis of data from expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE), are also included. There are a total of 165 unique entries in the database. Users can either browse or query the PGDB through a web interface. For each gene, in addition to basic gene information and rich cross-references to other databases, inclusive and relevant literature references are provided to support the inclusion of the gene in the database. Detailed expression data calculated from the UniGene and SAGEmap databases are also presented.

Chromosome specific databases are an important research tool as they integrate data from different directions, such as genetic and physical mapping data, expression data, sequences etc. They supplement the genome-wide repositories in molecular biology, such as GenBank, Swiss-Prot or OMIM, which usually concentrate on one type of information. The Integrated X Chromosome Database (IXDB, http://ixdb.mpimg-berlin-dahlem.mpg.de/) is a repository for physical mapping data of the human X chromosome and aims at providing a global view of genomic data at a chromosomal level. We present here an update of IXDB which includes schema extensions for storing submaps and sequence information, additional links to external databases, and the integration of an increasing number of physical and transcript mapping data. The gene data was completely updated according to the approved gene symbols of the HUGO Nomenclature Committee. IXDB receives over 1000 queries per month, an indication that its content is valuable to researchers seeking mapping data of the human X chromosome.