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.
Characters
Fast character-based identification
The BIONUMERICS software offers a tool for screening an entry against the database, based upon a character type experiment. This identification tool benefits from a bulk-fetching mechanism, which makes it many times faster for identification against large databases. This tutorial describes how to perform such character-based identification of entries.
Principal components analysis and discriminant analysis on a character data set
Import VNTR data from a text file
Import MLST data from an Excel file
Creating a Minimum Spanning Tree based on MLVA data
This tutorial illustrates how to create a Minimum Spanning Tree (MST) based on MLVA repeat numbers. The same steps are also applicable for clustering of other categorical character data sets such as MLST.
Creating a Minimum Spanning Tree based on MLST data
This tutorial illustrates how to create a Minimum Spanning Tree (MST) based on MLST allele numbers. The same steps are also applicable for clustering of other categorical character data sets such as MLVA.
Creating a custom mappings similarity matrix
In BIONUMERICS, character values can be mapped to categorical names according to predefined criteria. When character mappings are present, it becomes possible to define a custom mappings similarity matrix, which determines how similarities are calculated among the mappings. This can be useful when analyzing data sets like SNPs, VNTRs, SSRs, etc. In this tutorial the use of a custom mappings matrix is illustrated.
Combined analysis of character data
This tutorial illustrates how to create a dendrogram based on character data coming from different experiments, using a composite data set.
Clustering a phenotypic test assay
This tutorial illustrates how to cluster character data in BIONUMERICS. A test assay that reveals the metabolic activity of bacteria on different compounds is used as an example.