Tree and Network Inference module
Cluster analysis, also called unsupervised learning, is an indispensable tool in bioinformatics. BioNumerics brings together the power and flexibility of its relational database, the contribution of multiple techniques, and a wide range of clustering algorithms in a clustering module with unique capabilities.
The Comparison window in BioNumerics
The heart of BioNumerics' analysis functions is the Comparison window, presenting a comprehensive overview of all available experiments for a selection of entries and enabling the user to show and compare any combination of experiments. Similarity or distance matrices and dendrograms can be calculated for any selected experiment, and the obtained groupings can be compared with patterns or characters obtained from other experiments. A large number of similarity and distance coefficients and clustering methods are available, in order to provide the most appropriate clustering for all data types and clustering purposes.
Interpreting trees of up to 20,000 entries is not a simple task. BioNumerics offers a comprehensive set of features for interpreting and mining of complex data sets, including viewing tools such as two-way zoom-sliders, swapping and abridging of branches, rerooting of trees, displaying data (characters, patterns, curves or sequences) in various modes, assigning colors or symbols to groups, etc. Furthermore, adding entries to, or deleting entries from large clusterings is facilitated using the incremental clustering feature. Rather than recalculating matrices and trees, BioNumerics automatically updates, so that adding or deleting entries becomes a matter of a few seconds.
The comparison window has numerous edit options and offers enhanced publishing and printing facilities in a WYSIWYG environment. All features of a comparison can be stored to disk.