Common applications with BioNumerics
BioNumerics is a general platform for databasing and analysis of virtually all types of biological data, featuring three basic concepts:
- Databasing: The unique SQL database design offers storage, retrieval and querying tools for 1D profiles, 2D gels, sequences, character data, curves and kinetic readings, similarity matrices, etc. BioNumerics is the perfect backbone for cental and lab-wide storage and retrieval of biodata.
- Comparison: The software's main emphasis is on mining large data sets: screening for differences and similarities, revealing and visualizing differential data, grouping and clustering, identification, and statistics. The unique power of BioNumerics lies in its capacity to synthesize the results from different experimental approaches into conclusive answers.
- Networking: Thanks to its integrated networking and XML based Internet tools, BioNumerics has widely been implemented in networks for the exchange of biodata in a uniform and standardized way.
Below we list a number of applications for which BioNumerics is commonly used. Thanks to its flexible design and comprehensive scripting possibilities, the software can be used in many more research or diagnostic fields. A number of techniques for which the data is commonly analyzed using BioNumerics are also listed.
Please contact us if you would like to learn more about integrating your specific application(s) and/or techniques in BioNumerics.
Application fields
- Bacterial, fungal and viral epidemiological typing
- Bacterial source tracking
- Microbial community analysis
- Environmental study
- Fermentation monitoring (breweries, dairy products, wine,...)
- Food quality control
- Authenticity testing (food, organic products)
- Variety/cultivar identification
- Managing starter cultures
- Mutation detection and analysis
- Disease and cancer diagnostics
- Plant and animal breeding
- Phorensic study
- Platform for transcriptomics and proteomics
- Taxonomy and identification
- Phylogenetic inference and evolution
- Population modelling
- HIV drug resistance prediction
Techniques
Data from most techniques can directly be processed and analyzed in BioNumerics. Some techniques require special import, processing and/or automation steps. For these techniques (in bold), a dedicated Plugin is available, developed and supported by Applied Maths. Click on the links for more information.
Fingerprint types
- All 1-D electrophoresis patterns obtained from all kinds of instruments or gels (PFGE, AFLP, RFLP, RAPD, REP-PCR, ARDRA, isoelectric focusing etc.)
- Gradient gel electrophoresis (DGGE, TGGE)
- Microsatellite analysis
- Variable Number Tandem Repeat (VNTR) analysis (see MLVA)
- Multi-Locus VNTR Analysis (MLVA)
- Hetero-Duplex Analysis (HDA) (see CSCE analysis)
- Conformation-Sensitive Capillary Electrophoresis (CSCE) analysis
- Mass spectrometry (MALDI, SELDI)
- Gas chromatography and HPLC (fatty acid, quinone profiling)
- DHPLC typing
- Riboprinter® patterns
Character types
- Antibiotic resistance testing
- Fatty Acid Methyl Ester (FAME) analysis
- Biolog® Phenotype arrays
- API® phenotypic test panels (BioMérieux®)
- Microarrays
- Dot blots and probe sets
- Spoligo typing
- Biochemical tests
- Enzymatic and metabolic activity tests
Sequence types
- Contig assembly from automated sequencers (Applied Biosystems, Beckman, Amersham)
- SNP and mutation analysis
- Housekeeping gene sequence analysis (typing, population study)
- Multilocus Sequence Typing (MLST)
- Spa-typing of multidrug resistant Staphylococcus aureus (MRSA)
- Ribosomal RNA gene sequence analysis (identification, phylogeny)
- HIV drug resistance analysis and prediction
Trend data types
- Real-time PCR
- Kinetic readings of metabolic/enzymatic activity (e.g. Biolog®, PhenePlate®)
Matrix types
- DNA hybridization
- Complete or partial similarity/distance matrices
2D gel types
- Multigel matching and comparison from all common 2D gel techniques
- Multiplex 2D gels (DIGE)
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