BacDive in 2019: bacterial phenotypic data for High-throughput biodiversity analysis.

The bacterial metadatabase BacDive (http://bacdive.dsmz.de) has become a comprehensive resource for structured data on the taxonomy, morphology, physiology, cultivation, isolation and molecular data of prokaryotes. With its current release (7/2018) the database offers information for 63 669 bacterial and archaeal strains including 12 715 type strains. During recent developments of BacDive, the enrichment of information on existing strains was prioritized. This has resulted in a 146% increase of database content over the past three years. Especially rich datasets were integrated from 4782 manual annotated species descriptions in the International Journal of Systematic and Evolutionary Microbiology which yielded standardized phenotypic data for 5468 type strains. Another important improvement of content was achieved through the mobilization of 8977 Analytical Profile Index (API®) test results that constitute physiological data for the identification of 5237 strains. BacDive offers a unique API® data collection with respect to size and diversity. In addition, data on fatty acid profiles and antibiotic susceptibility tests were integrated. A revised graphical user interface and new search tools such as the API® test finder, the TAXplorer, or the Microbial Isolation Source Search significantly improve the user experience.

Mobilization and integration of bacterial phenotypic data-Enabling next generation biodiversity analysis through the BacDive metadatabase.

Microbial data and metadata are scattered throughout the scientific literature, databases and unpublished lab notes and thereby often are difficult to access. Hot spots of (meta)data are internal descriptions of culture collections and initial descriptions of novel taxa in primary literature. Here we describe three exemplary mobilization projects which yielded metadata published through the prokaryotic metadatabase BacDive. The Reichenbach collection of myxobacteria includes information on 12,535 typewritten index cards which were digitized. A total of 37,156 data points were extracted by text mining. In the second mobilization project, Analytical Profile Index (API) tests on paper forms were targeted. Overall 6820 API tests were digitized, which provide physiological data of 4524 microbial strains. Thirdly, the extraction of metadata from 523 new species descriptions of the International Journal of Systematic and Evolutionary Microbiology, yielding 35,651 data points, is described. All data sets were integrated and published in BacDive. Thereby these metadata not only became accessible and searchable but were also linked to strain taxonomy, isolation source, cultivation condition, and molecular biology data.

BacDive--The Bacterial Diversity Metadatabase in 2016.

BacDive-the Bacterial Diversity Metadatabase (http://bacdive.dsmz.de) provides strain-linked information about bacterial and archaeal biodiversity. The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology. The majority of data is manually annotated and curated. Currently (with release 9/2015), BacDive covers 53 978 strains. Newly implemented RESTful web services provide instant access to the content in machine-readable XML and JSON format. Besides an overall increase of data content, BacDive offers new data fields and features, e.g. the search for gene names, plasmids or 16S rRNA in the advanced search, as well as improved linkage of entries to external life science web resources.

BacDive--the Bacterial Diversity Metadatabase.

BacDive-the Bacterial Diversity Metadatabase (http://bacdive.dsmz.de) merges detailed strain-linked information on the different aspects of bacterial and archaeal biodiversity. Currently (release 9/2013), BacDive contains entries for 23 458 strains and provides information on their taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology. Where available, links to access the respective biological resources are given. The majority of the BacDive data is manually annotated and curated. The BacDive portal offers an easy-to-use simple search and in addition powerful advanced search functionalities allowing to combine more than 30 search fields for text and numerical data. The user can compile individual sets of strains to a download selection that can easily be imported into nearly all spreadsheet applications.

BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA.

The BRENDA (BRaunschweig ENzyme DAtabase) enzyme portal (http://www.brenda-enzymes.org) is the main information system of functional biochemical and molecular enzyme data and provides access to seven interconnected databases. BRENDA contains 2.7 million manually annotated data on enzyme occurrence, function, kinetics and molecular properties. Each entry is connected to a reference and the source organism. Enzyme ligands are stored with their structures and can be accessed via their names, synonyms or via a structure search. FRENDA (Full Reference ENzyme DAta) and AMENDA (Automatic Mining of ENzyme DAta) are based on text mining methods and represent a complete survey of PubMed abstracts with information on enzymes in different organisms, tissues or organelles. The supplemental database DRENDA provides more than 910 000 new EC number-disease relations in more than 510 000 references from automatic search and a classification of enzyme-disease-related information. KENDA (Kinetic ENzyme DAta), a new amendment extracts and displays kinetic values from PubMed abstracts. The integration of the EnzymeDetector offers an automatic comparison, evaluation and prediction of enzyme function annotations for prokaryotic genomes. The biochemical reaction database BKM-react contains non-redundant enzyme-catalysed and spontaneous reactions and was developed to facilitate and accelerate the construction of biochemical models.

Development of a classification scheme for disease-related enzyme information.

BACKGROUND: BRENDA (BRaunschweig ENzyme DAtabase, http://www.brenda-enzymes.org) is a major resource for enzyme related information. First and foremost, it provides data which are manually curated from the primary literature. DRENDA (Disease RElated ENzyme information DAtabase) complements BRENDA with a focus on the automatic search and categorization of enzyme and disease related information from title and abstracts of primary publications. In a two-step procedure DRENDA makes use of text mining and machine learning methods. RESULTS: Currently enzyme and disease related references are biannually updated as part of the standard BRENDA update. 910,897 relations of EC-numbers and diseases were extracted from titles or abstracts and are included in the second release in 2010. The enzyme and disease entity recognition has been successfully enhanced by a further relation classification via machine learning. The classification step has been evaluated by a 5-fold cross validation and achieves an F1 score between 0.802 ± 0.032 and 0.738 ± 0.033 depending on the categories and pre-processing procedures. In the eventual DRENDA content every category reaches a classification specificity of at least 96.7% and a precision that ranges from 86-98% in the highest confidence level, and 64-83% for the smallest confidence level associated with higher recall. CONCLUSIONS: The DRENDA processing chain analyses PubMed, locates references with disease-related information on enzymes and categorises their focus according to the categories causal interaction, therapeutic application, diagnostic usage and ongoing research. The categorisation gives an impression on the focus of the located references. Thus, the relation categorisation can facilitate orientation within the rapidly growing number of references with impact on diseases and enzymes. The DRENDA information is available as additional information in BRENDA.

BRENDA, the enzyme information system in 2011.

The BRENDA (BRaunschweig ENzyme Database, http://www.brenda-enzymes.org) enzyme information system is the main collection of enzyme functional and property data for the scientific community. The majority of the data are manually extracted from the primary literature. The content covers information on function, structure, occurrence, preparation and application of enzymes as well as properties of mutants and engineered variants. The number of manually annotated references increased by 30% to more than 100,000, the number of ligand structures by 45% to almost 100,000. New query, analysis and data management tools were implemented to improve data processing, data presentation, data input and data access. BRENDA now provides new viewing options such as the display of the statistics of functional parameters and the 3D view of protein sequence and structure features. Furthermore a ligand summary shows comprehensive information on the BRENDA ligands. The enzymes are linked to their respective pathways and can be viewed in pathway maps. The disease text mining part is strongly enhanced. It is possible to submit new, not yet classified enzymes to BRENDA, which then are reviewed and classified by the International Union of Biochemistry and Molecular Biology. A new SBML output format of BRENDA kinetic data allows the construction of organism-specific metabolic models.