BioNumerics Seven tutorials

All information pertaining the BIONUMERICS database. This includes import of descriptive information about strains, accession or biological samples (commonly referred to as entries in BIONUMERICS), modifications to the database layout and setup, entry selections, user management, etc.

Any type of data that can be translated into a densitometric curve is considered a fingerprint type in the BIONUMERICS and GelCompar II software. This includes commonly used genotyping methods employing agarose or polyacrylamide slab gel electrophoresis (PFGE, rep-PCR, RAPD, PCR-DGGE, etc.), in which case the data are usually imported as two-dimensional gel images (bitmaps). Another major group consists of capillary electrophoresis profiles such as AFLP, ARISA, T-RFLP, etc. Here, the raw electropherograms generated by an automated sequencer (genetic analyzer) or derived peak table text files can be imported. Finally, any other profile (generated e.g. by gas chromatography, HPLC or spectrophotometry) that can be seen as peaks or bands, can be analyzed as a fingerprint.

Spectrum types (or spectra) share some features with fingerprints, but are specifically designed to hold and process e.g. mass spectrometry data such as MALDI TOF MS, LC MS, ESI and SELDI.

A character is basically a name-value pair of which the value can be binary, multi-state or continuous. Because of this very broad definition, a wide variety of data can be analyzed as character types (= an array of characters). This includes morphological and biochemical features, commercial test panels (API®, Biolog®, Vitek®, etc.), antibiotics resistance profiles, fatty acid profiles, microarrays, SNP arrays, repeat numbers in MLVA, allelic profiles in MLST, etc.

Nucleic acid or amino acid sequences, from import and assembly over multiple alignment to annotation, SNP analysis, primer design and comparison of complete chromosomes.

A sequence read set is designed to hold large sets of short reads generated by next generation sequencing (NGS). Base sequences and their associated quality scores are stored for single-end and paired-end reads, originating from various high-throughput sequencing platforms such as Illumina, Ion Torrent, PacBio, Oxford Nanopore, etc.

Trend data refers to sequential measurements that express an evolution of one parameter in function of another. Examples include enzymatic activity, growth curves, (high resolution) melting curves, etc. Such data can be imported, analyzed and compared (by means of parameters calculated on regression curves) in the BIONUMERICS software.

BIONUMERICS stores its data in a relational (SQL) database, usually referred to as “connected database” in the software. Following database types are supported: Microsoft SQL Server, Microsoft Access, SQLite, Oracle and MySQL. This topic contains information on the creation and setup of databases in the database management software (DBMS) and on how to link BIONUMERICS via an ODBC connection to an existing database.