A database of information about how microorganisms respond to different environments. The information in ComBase is referred to as quantitative microbiological data since it describes how levels of microorganisms, both spoilage organisms and pathogens, change over the course of time. The primary goal of the ComBase consortium is to improve efficiency in locating specific microbiological information, provide a more rapid means to compare data from different laboratories, and to reduce unnecessary redundancy in conducting microbiological studies. Cornbase was launched in 2003 The ComBase Initiative is a collaboration between the Food Standards Agency and the Institute of Food Research from the United Kingdom; the USDA Agricultural Research Service and its Eastern Regional Research Center from the United States; and the Food Safety Center in Australia. Its purpose is to make data and predictive tools on microbial responses to food environments freely available via web-based software. The ComBase Database (accessible via the ComBase Browser) consists of thousands of microbial growth and survival curves that have been collated in research establishments and from publications. They form the basis for numerous microbial models presented in ComBase Predictor, a useful tool for industry, academia and regulatory agencies. They can be used in developing new food technologies while maintaining food safety; in teaching and research; in assessing the microbial risk in foods or setting up new guidelines.

The microbial protein interaction database (MPIDB) provides physical microbial interaction data. The interactions are manually curated from the literature or imported from other databases, and are linked to supporting experimental evidence, as well as evidences based on interaction conservation, protein complex membership, and 3D domain contacts.

PMIP provides access to predictive models for foodborne pathogens, relevant regulatory policies and guidelines, and microbial data related to pathogenic and spoilage microorganisms in food products. The models in the Predictive Microbiology Information Portal are mainly from the Pathogen Modeling Program (PMP) of the USDA Agricultural Research Service (ARS) - Eastern Regional Research Center (ERRC) , and currently 15 models are in the portal. The main sources of rules/regulations are links to the USDA - Food Safety and Inspection Service and Food and Drug Administration websites. The microbial growth data are from Combase, which contains about 65,000 data points. It is a relational database that is jointly developed and maintained by the Food Research Institute of UK, USDA-ARS, and the Center of Excellence for Food Safety, Australia. The portal provides a searchable function that the users can use to obtain specific information that is of interest to them. The tutorial provides brief instructions and examples on how to navigate the portal and retrieve necessary information. The PMIP links users to numerous and diverse resources associated with models (PMP), databases (ComBase) , regulatory requirements, and food safety principles. Resources in this dataset:Resource Title: Predictive Microbiology Information Portal (PMIP) Web Site. File Name: Web Page, url: https://portal.errc.ars.usda.gov/

This database server is supported in fulfilment of the research mission of the Mycotoxin Prevention and Applied Microbiology Research Unit at the National Center for Agricultural Utilization Research in Peoria, Illinois. The linked website provides access to gene sequence databases for various groups of microorganisms, such as Streptomyces species or Aspergillus species and their relatives, that are the product of ARS research programs. The sequence databases are organized in the BIGSdb (Bacterial Isolate Genomic Sequence Database) software package developed by Keith Jolley and Martin Maiden at Oxford University. Resources in this dataset:Resource Title: ARS Microbial Genomic Sequence Database Server. File Name: Web Page, url: http://199.133.98.43

Introduction:
This sequence database (MARMICRODB) was introduced in the publication JW Becker, SL Hogle, K Rosendo, and SW Chisholm. 2019. Co-culture and biogeography of Prochlorococcus and SAR11. ISME J. doi:10.1038/s41396-019-0365-4. Please see the original publication and its associated supplementary material for the original description of this resource.

Motivation:
We needed a reference database to annotate shotgun metagenomes from the Tara Oceans project [1] the GEOTRACES cruises GA02, GA03, GA10, and GP13 and the HOT and BATS time series [2]. Our interests are primarily in quantifying and annotating the free-living, oligotrophic bacterial groups Prochlorococcus, Pelagibacterales/SAR11, SAR116, and SAR86 from these samples using the protein classifier tool Kaiju [3]. Kaiju’s sensitivity and classification accuracy depend on the composition of the reference database, and highest sensitivity is achieved when the reference database contains a comprehensive representation of expected taxa from an environment/sample of interest. However, the speed of the algorithm decreases as database size increases. Therefore, we aimed to create a reference database that maximized the representation of sequences from marine bacteria, archaea, and microbial eukaryotes, while minimizing (but not excluding) the sequences from clinical, industrial, and terrestrial host-associated samples.

Results/Description:
MARMICRODB consists of 56 million sequence non-redundant protein sequences from 18769 bacterial/archaeal/eukaryote genome and transcriptome bins and 7492 viral genomes optimized for use with the protein homology classifier Kaiju [3]. To ensure maximum representation of marine bacteria, archaea, and microbial eukaryotes, we included translated genes/transcripts from 5397 representative “specI” species clusters from the proGenomes database [4]; 113 transcriptomes from the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) [5]; 10509 metagenome assembled genomes from the Tara Oceans expedition [6,7], the Red Sea [8], the Baltic Sea [9], and other aquatic and terrestrial sources [10]; 994 isolate genomes from the Genomic Encyclopedia of Bacteria and Archaea [11]; 7492 viral genomes from NCBI RefSeq [12]; 786 bacterial and archaeal genomes from MarRef [13]; and 677 marine single cell genomes [14]. In order to annotate metagenomic reads at the clade/ecotype level (subspecies) for the focal taxa Prochlorococcus, Synechococcus, SAR11/Pelagibacterales, SAR86, and SAR116, we generated custom MARMICRODB taxonomies based on curated genome phylogenies for each group. The curated phylogenies, Kaiju formatted Burrows-Wheeler index, translated genes, the custom taxonomy hierarchy, an interactive kronaplot of the taxonomic composition, and scripts and instructions for how to use or rebuild the resource is available from 10.5281/zenodo.3520509.

Methods:
The curation and quality control of MARMICRODB single cell, metagenome assembled, and isolate genomes was performed as described in [15]. Briefly, we downloaded all MARMICRODB genomes as raw nucleotide assemblies from NCBI. We determined an initial genome taxonomy for these assemblies using checkM with the default lineage workflow [16]. All genome bins met the completion/contamination thresholds outlined in prior studies [7,17]. For single cell and metagenome assembled genomes, especially those from Tara Oceans Mediterranean sea samples [18], we use the GTDB-Tk classification workflow [19] to verify the taxonomic fidelity of each genome bin. We then selected genomes with a checkM taxonomic assignment of Prochlorococcus, Synechococcus, SAR11/Pelagibacterales, SAR86, and SAR116 for further analysis and confirmed taxonomic assignment using blast matches to known Prochlorococcus/Synechococcus ITS sequences and by matching 16S sequences to the SILVA database [20]. To refine our estimates of completeness/contamination of Prochlorococcus genome bins we created a custom set of 730 single copy protein families (available from 10.5281/zenodo.3719132) from closed, isolate Prochlorococcus genomes [21] for quality assessments with checkM. For Synechococcus we used the CheckM taxonomic-specific workflow with the genus Synechococcus. After the custom CheckM quality control, we excluded any genome bins from downstream analysis that had an estimated quality < 30, defined as %completeness – 5x %contamination resulting in 18769 genome/transcriptome bins. We predicted genes in the resulting genome bins using prodigal [22] and excluded protein sequences with lengths less than 20 and greater than 20000 amino acids, removed non-standard amino acid residues, and condensed redundant protein sequences to a single representative sequence to which we assigned a lowest common ancestor (LCA) taxonomy identifier from the NCBI taxonomy database [23]. The resulting protein sequences were compiled and used to build a Kaiju [3] search database.

The above filtering criteria resulted in 605 Prochlorococcus, 96 Synechococcus, 186 SAR11/Pelagibacterales, 60 SAR86, and 59 SAR116 high-quality genome bins. We constructed a high quality fixed reference phylogenetic tree for each taxonomic group based on genomes manually selected for completeness and the phylogenetic diversity. For example the Prochlorococcus and Synechococcus genomes for the fixed reference phylogeny are estimated > 90% complete, and SAR11 genomes are estimated > 70% complete. We created multiple sequence alignments of phylogenetically conserved genes from these genomes using the GTDB-Tk pipeline [19] with default settings. The pipeline identifies conserved proteins (120 bacterial proteins) and generates concatenated multi-protein alignments [17] from the genome assemblies using hmmalign from the hmmer software suite. We further filtered the resulting alignment columns using the bacterial and archaeal alignment masks from [17] (http://gtdb.ecogenomic.org/downloads). We removed columns represented by fewer than 50% of all taxa and/or columns with no single amino acid residue occuring at a frequency greater than 25%. We trimmed the alignments using trimal [24] with the automated -gappyout option to trim columns based on their gap distribution. We inferred reference phylogenies using multithreaded RAxML [25] with the GAMMA model of rate heterogeneity, empirically determined base frequencies, and the LG substitution model [26](PROTGAMMALGF). Branch support is based on 250 resampled bootstrap trees. This tree was then pruned to only allow a maximum average distance to the closest leaf (ADCL) of 0.003 to reduce the phylogenetic redundancy in the tree [27]. We then “placed” genomes that either did not pass completeness threshold or were considered phylogenetically redundant by ADCL within the fixed reference phylogeny for each group using pplacer [28] representing each placed genome as a pendant edge in the final tree. We then examined the resulting tree and manually selected clade/ecotype cutoffs to be as consistent as possible with clade definitions previously outlined for these groups [29–32]. We then gave clades from each taxonomic group custom taxonomic identifiers and we added these identifiers to the MARMICRODB Kaiju taxonomic hierarchy.

Software/databases used:
checkM v1.0.11[16]
HMMERv3.1b2 (http://hmmer.org/)
prodigal v2.6.3 [22]
trimAl v1.4.rev22 [24]
AliView v1.18.1 [33] [34]
Phyx v0.1 [35]
RAxML v8.2.12 [36]
Pplacer v1.1alpha [28]
GTDB-Tk v0.1.3 [19]
Kaiju v1.6.0 [34]
GTDB RS83 (https://data.ace.uq.edu.au/public/gtdb/data/releases/release83/83.0/)
NCBI Taxonomy (accessed 2018-07-02) [23]
TIGRFAM v14.0 [37]
PFAM v31.0 [38]

Discussion/Caveats:
MARMICRODB is optimized for metagenomic samples from the marine environment, in particular planktonic microbes from the pelagic euphotic zone. We expect this database may also be useful for classifying other types of marine metagenomic samples (for example mesopelagic, bathypelagic, or even benthic or marine host-associated), but it has not been tested as such. The original purpose of this database was to quantify clades/ecotypes of Prochlorococcus, Synechococcus, SAR11/Pelagibacterales, SAR86, and SAR116 in metagenomes from Tara Oceans Expedition and the GEOTRACES project. We carefully annotated and quality controlled genomes from these five groups, but the processing of the other marine taxa was largely automated and unsupervised. Taxonomy for other groups was copied over from the Genome Taxonomy Database (GTDB) [19,39] and NCBI Taxonomy [23] so any inconsistencies in those databases will be propagated to MARMICRODB. For most use cases MARMICRODB can probably be used unmodified, but if the user’s goal is to focus on a particular organism/clade that we did not curate in the database then the user may wish to spend some time curating those genomes (ie checking for contamination, dereplicating, building a genome phylogeny for custom taxonomy node assignment). Currently the custom taxonomy is hardcoded in the MARMICRODB.fmi index, but if users wish to modify MARMICRODB by adding or removing genomes, or reconfiguring taxonomic ranks the names.dmp and nodes.dmp files can easily be modified as well as the fasta file of protein sequences. However, the Kaiju index will need to be rebuilt, and user will require a high

Database which contains the signal transduction proteins for complete and draft bacterial and archaeal genomes. The MiST2 database identifies and catalogs the repertoire of signal transduction proteins in microbial genomes.

MBGD is a database for comparative analysis of completely sequenced microbial genomes, the number of which is now growing rapidly. The aim of MBGD is to facilitate comparative genomics from various points of view such as ortholog identification, paralog clustering, motif analysis and gene order comparison. The heart of MBGD function is to create orthologous or homologous gene cluster table. For this purpose, similarities between all genes are precomputed and stored into the database, in addition to the annotations of genes such as function categories that were assigned by the original authors and motifs that were found in the translated sequence. Using these homology data, MBGD dynamically creates orthologous gene cluster table. Users can change a set of organisms or cutoff parameters to create their own orthologous grouping. Based on this cluster table, users can further analyze multiple genomes from various points of view with the functions such as global map comparison, local map comparison, multiple sequence alignment and phylogenetic tree construction.

ComBase includes a systematically formatted database of quantified microbial responses to the food environment with more than 65,000 records, and is used for: Informing the design of food safety risk management plans Producing Food Safety Plans and HACCP plans Reducing food waste Assessing microbiological risk in foods The ComBase Browser enables you to search thousands of microbial growth and survival curves that have been collated in research establishments and from publications. The ComBase Predictive Models are a collection of software tools based on ComBase data to predict the growth or inactivation of microorganisms as a function of environmental factors such as temperature, pH and water activity in broth. Interested users can also contribute growth or inactivation data via the Donate Data page, which includes instructional videos, data template and sample, and an Excel demo file of data and macros for checking data format and syntax. Resources in this dataset:Resource Title: Website Pointer to ComBase. File Name: Web Page, url: https://www.combase.cc/index.php/en/ ComBase is an online tool for quantitative food microbiology. Its main features are the ComBase database and ComBase models, and can be accessed on any web platform, including mobile devices. The focus of ComBase is describing and predicting how microorganisms survive and grow under a variety of primarily food-related conditions. ComBase is a highly useful tool for food companies to understand safer ways of producing and storing foods. This includes developing new food products and reformulating foods, designing challenge test protocols, producing Food Safety plans, and helping public health organizations develop science-based food policies through quantitative risk assessment. Over 60,000 records have been deposited into ComBase, describing how food environments, such as temperature, pH, and water activity, as well as other factors (e.g. preservatives and atmosphere) affect the growth of bacteria. Each data record shows users how bacteria populations change for a particular combination of environmental factors. Mathematical models (the ComBase Predictor and Food models) were developed on systematically generated data to predict how various organisms grow or survive under various conditions.

nifH genes and amplicons from various sources used in our study.

Database that collects and provides all known physical microbial interactions. Currently, 24,295 experimentally determined interactions among proteins of 250 bacterial species/strains can be browsed and downloaded. These microbial interactions have been manually curated from the literature or imported from other databases (IntAct, DIP, BIND, MINT) and are linked to 26,578 experimental evidences (PubMed ID, PSI-MI methods). In contrast to these databases, interactions in MPIDB are further supported by 68,346 additional evidences based on interaction conservation, co-purification, and 3D domain contacts (iPfam, 3did). (spoke/matrix) binary interactions inferred from pull-down experiments are not included.

(Version 20230306)

Version 4 (20230306) of the RKI MALDI-ToF mass spectra database is the third update of the original database (version 20161027, https://doi.org/10.5281/zenodo.163517). The RKI Database v.4 now contains a total of 11055 MALDI-ToF mass spectra from 1599 microbial strains of highly pathogenic (i.e. biosafety level 3, BSL-3) bacteria such as Bacillus anthracis, Brucella melitensis, Yersinia pestis, Burkholderia mallei / pseudomallei and Francisella tularensis as well as a selection of spectra of their close and distant relatives. The database can be used as a reference for the diagnosis of BSL-3 bacteria using proprietary and free software packages for MALDI-ToF MS-based microbial identification. The spectral data are provided as a zip archive (zenodo db 230306.zip) containing the original mass spectra in their native data format (Bruker Daltonics). Please refer to the pdf file (230306-ZENODO-Metadata.pdf) for information on cultivation conditions, sample preparation and details of the spectra acquisition. Please do not try to print this document (>1600 pages!).

Version 20230306 of the RKI database contains for the first time a file in btmsp format (230306_v4_RKI_DB_BSL3.btmsp). This file was generated using the MALDI Biotyper software (Bruker Daltonics) and contains a total of 1599 main spectra from the BSL-3 database in the proprietary data format of the MALDI Biotyper software. *.btmsp files can be imported and used for identification with this software solution. Note that the btmsp file available in database version 4 is broken and cannot be imported. Please refer to updated database versions (4.1, or 4.2) to download valid btmsp files.

The pkf files (230306_ZENODO_30Peaks_0.75.pkf, 230306_ZENODO_45Peaks_0.75.pkf) represent two versions of the MS peak list data in a Matlab compatible format. The latter data can be imported into MicrobeMS, a free Matlab-based software solution developed at the RKI. MicrobeMS can be used for the identification of microorganisms by MALDI-ToF MS and is available at https://wiki-ms.microbe-ms.com.

The RKI mass spectrometry database is updated regularly.

The author would like to thank the following individuals for providing microbial strains and species or mass spectra thereof. Without their help, this work would not have been possible.

Wolfgang Beyer - University of Hohenheim, Faculty of Agricultural Sciences, Stuttgart, Germany

Guido Werner - Robert Koch-Institute, Nosocomial Pathogens and Antibiotic Resistances (FG13), Wernigerode, Germany

Alejandra Bosch - CINDEFI, CONICET-CCT La Plata, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina

Michal Drevinek - National Institute for Nuclear, Biological and Chemical Protection, Milin, Czech Republic

Roland Grunow, Daniela Jacob, Silke Klee, Susann Dupke and Holger Scholz - Robert Koch-Institute, Highly Pathogenic Microorganisms (ZBS2), Berlin, Germany

Jörg Rau - Chemisches und Veterinäruntersuchungsamt Stuttgart, Fellbach, Germany

Jens Jacob - Robert Koch-Institute, Hospital Hygiene, Infection Prevention and Control (FG14), Berlin, Germany

Martin Mielke - Robert Koch-Institute, Department 1 - Infectious Diseases, Berlin, Germany

Monika Ehling-Schulz - Functional Microbiology, Institute of Microbiology, University of Veterinary Medicine, Vienna, Austria

Armand Paauw - Department of Medical Microbiology, CBRN protection, Universitair Medisch Centrum Utrecht, TNO, Rijswijk, The Netherlands

Herbert Tomaso – Friedrich-Löffler-Institut (FLI), Federal Research Institute for Animal Health, Jena, Germany

Gabriel Karner - Karner Düngerproduktion GmbH, Research & Development, Neulengbach, Austria

Rainer Borriss - Institute of Marine Biotechnology e.V. (IMaB), Greifswald, Germany

Le Thi Thanh Tam - Division of Plant Pathology and Phyto-Immunology, Plant Protection Research Institute, Hanoi, Socialist Republic of Vietnam

Xuewen Gao - College of Plant Protection, Nanjing Agricultural University, Key Laboratory of Integrated Management of Crop Diseases and Pests, Nanjing, People’s Republic of China

A fasta-formatted database of 36,866,870 predicted proteins representing 4,351 unique species from 117 phyla., A database of 36,866,870 predicted proteins representing 4,351 unique species from 117 phyla (see table below) was constructed using the UniProt Reference Proteome (RP) at the 35% co-membership threshold including 4,295 Representative Proteome Groups (RPGs) (Chen et al. 2011) in addition to all taxonomically identifiable transcriptomes of the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) (Keeling et al. 2014) that were processed through WinstonCleaner (https://github.com/kolecko007/WinstonCleaner). The database also included proteins inferred from the annotated and assembled genomes of Aurantiochytrium limacinum ATCC MYA-1381, Schizochytrium aggregatum ATCC 28209, and Aplanochytrium kerguelensis PBS07 from the U.S. Department of Energy’s Joint Genome Institute (JGI), all PFAM PF00494 Aurantiochytrium sp. KH105 proteome hits from the Okinawa Institute of Science and Technology Marine Genomics Unit genome browser, all of UniProt's annotated Hondaea fermentalgiana pr...,

Link Function: information

To evaluate effects of human influence on the health of Puget Sound's pelagic ecosystems, we propose a sampling program across multiple oceanographic basins measuring key attributes of the pelagic foodweb. We will quantify seasonal abundance and composition of pelagic biota from lower trophic levels (e.g., bacteria and phytoplankton) to middle trophic levels (e.g., zooplankton, small pelagic fi...

This database contains ~4,300 microbial genomes assembled from diverse fermented foods. These genomes were obtained from a larger set of 13,850 microbial genomes by clustering them at 99% average nucleotide identity (ANI) to create a "species"-representative database.

This zipped tarball (.tar.gz) contains a pre-built database for Bacannot (https://github.com/fmalmeida/bacannot).

Files are in the naming convention YEAR_MONTH_DAY.

Currently database generation date: 28 of March, 2025.

Database containing detailed information about small molecules produced by human microbiome. Provides metabolite data including structure, names, descriptions, chemical taxonomy, chemical ontology, physico-chemical data, spectra and contains detailed information about microbes that produce these chemicals, enzymatic reactions responsible for their production, bioactivity of chemicals and anatomical location of these chemicals and microbes. Many data fields in the database are hyperlinked to other databases including FooDB, HMDB, KEGG, PubChem, MetaCyc, ChEBI, UniProt, and GenBank. Database is FAIR compliant.The data in MiMeDB are released under the Creative Commons (CC) 4.0 License.

The goal of the National Microbial Germplasm Program is to ensure that the genetic diversity of agriculturally important microorganisms is maintained to enhance and increase agricultural efficiency and profitability. The program collects, authenticates, and characterizes potentially useful microbial germplasm; preserves microbial genetic diversity; and facilitates distribution and utilization of microbial germplasm for research and industry. The Agricultural Research Service maintains several microbial germplasm collections including: USDA ARS Culture Collection USDA ARS Collection of Entomopathogenic Fungal Cultures (ARSEF) Query or Download the Rhizobium Database US National Fungus Collections Resources in this dataset:Resource Title: National Microbial Germplasm Program . File Name: Web Page, url: https://www.ars-grin.gov/nmg/ Main web site for the National Microbial Germplasm Program with links to component databases/collections.

Databases and data used for Pansweep.

Canadian Journal of Infectious Diseases and Medical Microbiology CiteScore 2024-2025 - ResearchHelpDesk - Canadian Journal of Infectious Diseases and Medical Microbiology is a peer-reviewed, Open Access journal that publishes original research articles, review articles, and clinical studies related to infectious diseases of bacterial, viral and parasitic origin. The journal welcomes articles describing research on pathogenesis, epidemiology of infection, diagnosis and treatment, antibiotics and resistance, and immunology. Canadian Journal of Infectious Diseases and Medical Microbiology is archived in Portico, which provides permanent archiving for electronic scholarly journals, as well as via the LOCKSS initiative. It operates a fully open access publishing model which allows open global access to its published content. This model is supported through Article Processing Charges. Canadian Journal of Infectious Diseases and Medical Microbiology is included in many leading abstracting and indexing databases. Abstracting and Indexing The following is a list of the Abstracting and Indexing databases that cover Canadian Journal of Infectious Diseases and Medical Microbiology published by Hindawi. Abstracts on Hygiene and Communicable Diseases Agricultural Economics Database Agroforestry Abstracts Botanical Pesticides CAB Abstracts Directory of Open Access Journals (DOAJ) EMBASE Global Health Google Scholar Journal Citation Reports - Science Edition Open Access Journals Integrated Service System Project (GoOA) Primo Central Index PubMed PubMed Central Science Citation Index Expanded Scopus The Summon Service WorldCat Discovery Services All of Hindawi’s content is archived in Portico, which provides permanent archiving for electronic scholarly journals, as well as via the LOCKSS initiative.