Abstract
A logical consequence of the development of high-throughput technologies and the arising of new omic approaches is the consideration of combining those complementing genome-wide measurements at the same experiment. However, given the high complexity and the size of this heterogeneous data, it is a challenge to provide a way to detect and communicate the relevant information to the researcher in an understandable and appropriate way.
Within this scenario, the integrative omics analysis and, in particular, the integrative visualization of heterogeneous omics data emerge as a very attractive approach which can lead toward a better understanding of global functional properties.
There are several examples today of tools that accept diverse omics measurements and generate join visual of the data, and a first distinction would be whether the tool is devoted to display genomic information, often visualized using genome browsers that shows the genome features continuously along chromosome coordinates; or network information, such as interactions between the different molecular features (genes, proteins and metabolites), which are usually visualized in the form of graphs or pathways.
A well know tool for this purpose is Paintomics 2.0, a web-based tool which represents multiple gene expression and metabolomics datasets onto KEGG Pathways and performs joined pathway analysis considering both types of data; which now has a new version, Paintomics 3.0.
With this new version of the tool, we extend the underlying idea of joint visualization to support multiple omic data types, such as Gene Expression, Proteomics, DNase-se, ChIP-seq, miRNA-seq among other; and includes new and powerful features, upgraded databases, and a fresh, modern and friendly user-interface.
