Using Self-Organizing Maps to Visualize, Filter and Cluster Multidimensional Bio-Omics Data

In the face of ever-growing of biological data at the genome scale (denoted as omics data) [1,2], investigators of virtually every aspect of biological research are shifting their attention to massive information extracted from omics data. The ‘omics’ refers to a complete set of bi‐ omolecules, such as DNAs, RNAs, proteins and other molecular entities. Omics data are produced by high-throughput technologies. At first, these technologies were known as cDNA microarray [3] and oligonucleotide chips [4]. Then, they were diversely evolved into ChIP-on-Chip [5] and ChIP-Sequencing [6,7], two-dimensional gel electrophoresis and mass spectrometry [8] and high-throughput two-hybrid screening [9]. Recently, they are high‐ lighted by next-generation sequencing technologies such as DNA-seq [10] and RNA-seq [11]. Because of these technological advances, biological information can be quantified in parallel and on a genome scale, but at a much-reduced cost. Nearly, omics data cover every aspect of biological information and thus secure the studies being carried out from a ge‐ nome-wise perspective. To name but a few examples, they can be used (i) to catalog the whole genome within a living organism (genomics), (ii) to monitor the gene expression at RNA level (transcriptomics) or at protein level (proteomics), (iii) to study the protein-pro‐ tein interactions (interactomics) and transcription factor-DNA binding patterns (regularo‐ mics), and (iv) to characterize DNA or histone modifications exerting on the chromosomes (epigenomics). These multi-layer omics data not just constitute a global overview of molecu‐ lar constituents, but also provide an opportunity for studying biological mechanisms. In contrast to conventional reductionism focusing on individual biomolecules, omics ap‐ proaches allow the study of emergent behaviors of biological systems. This conceptual ad‐ vance has led to the advent of systems biology [12], an interdisciplinary research field with the ultimate goal of in silico modeling of biological systems.