Self-Organizing Map for Characterizing Heterogeneous Nucleotide and Amino Acid Sequence Motifs

A self-organizing map (SOM) is an artificial neural network algorithm that can learn from the training data consisting of objects expressed as vectors and perform non-hierarchical clustering to represent input vectors into discretized clusters, with vectors assigned to the same cluster sharing similar numeric or alphanumeric features. SOM has been used widely in transcriptomics to identify co-expressed genes as candidates for co-regulated genes. I envision SOM to have great potential in characterizing heterogeneous sequence motifs, and aim to illustrate this potential by a parallel presentation of SOM with a set of numerical vectors and a set of equal-length sequence motifs. While there are numerous biological applications of SOM involving numerical vectors, few studies have used SOM for heterogeneous sequence motif characterization. This paper is intended to encourage (1) researchers to study SOM in this new domain and (2) computer programmers to develop user-friendly motif-characterization SOM tools for biologists.