Computational ecosystems for data-driven medical genomics

In the path towards personalized medicine, the integrative bioinformatics infrastructure is a critical enabling resource. Until large-scale reference data became available, the attributes of the computational infrastructure were postulated by many, but have mostly remained unverified. Now that large-scale initiatives such as The Cancer Genome Atlas (TCGA) are in full swing, the opportunity is at hand to find out what analytical approaches and computational architectures are really effective. A recent report did just that: first a software development environment was assembled as part of an informatics research program, and only then was the analysis of TCGA's glioblastoma multiforme multi-omic data pursued at the multi-omic scale. The results of this complex analysis are the focus of the report highlighted here. However, what is reported in the analysis is also the validating corollary for an infrastructure development effort guided by the iterative identification of sound design criteria for the architecture of the integrative computational infrastructure. The work is at least as valuable as the data analysis results themselves: computational ecosystems with their own high-level abstractions rather than rigid pipelines with prescriptive recipes appear to be the critical feature of an effective infrastructure. Only then can analytical workflows benefit from experimentation just like any other component of the biomedical research program.