Bioinformatics clustering

Finally, it is the choice of bioinformatics software that enables the researcher to quickly assess obtained array data. Clustering using self-organizing maps and literature networks are ideally suited for this task. 

Totrov M (2011) Ligand binding site superposition and comparison based on Atomic Property Fields identification of distant homologues, convergent evolution and PDB-wide clustering of binding sites. BMC Bioinformatics 12 835... 

Fig. 4. Application of bioinformatics tools to 2D-DIGE data analysis. Proteome data consisting of the normalized spot intensity values are exported from the image analysis software and their correlation with clinicopathological data examined. Using informatics tools including clustering algorithms and machine-learning methods, a novel cancer classification based on proteome data is established, and key proteomic features and proteins corresponding to biomarker candidates are identified.

A number of bioinformatic programs can calculate the distance between RNA secondary structures and perform clustering analysis to identify ensembles of related structures (Liu et al., 2008 Torarinsson et al., 2007). Partitioning the alignment in a set of ensembles thus reduces the total number of phylogenetic variants to be considered and single representative from each ensemble can be subjected to crystallization trials. 

To gain primary insights about structural features of a metabolic product encoded by a biosynthetic assembly line, bioinformatics analysis can be very helpful 8,24). The procedure can allow the prediction of putative physico-chemical properties of the putative metabolic product encoded, for example, by a cryptic biosynthetic gene cluster 138). The prediction assists in choosing adequate fermentation conditions... 

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