Abstract Background Comparative Genomic Hybridization (CGH) with DNA microarrays has many biological applications including surveys of copy number changes in tumorogenesis, species detection and identification, and functional genomics studies among related organisms. Array CGH has also been used to infer phylogenetic relatedness among species or strains. Although the use of the entire genome can be seen as a considerable advantage for use in phylogenetic analysis, few such studies have questioned the reliability of array CGH to correctly determine evolutionary relationships. A potential flaw in this application lies in the fact that all comparisons are made to a single reference species. This situation differs from traditional DNA sequence, distance-based phylogenetic analyses where all possible pairwise comparisons are made for the isolates in question. By simulating array data based on the Neurospora crassa genome, we address this potential flaw and other questions regarding array CGH phylogeny. Results Our simulation data indicates that having a single reference can, in some cases, be a serious limitation when using this technique. Additionally, the tree building process with a single reference is sensitive to many factors including tree topology, choice of tree reconstruction method, and the distance metric used. Conclusions Without prior knowledge of the topology and placement of the reference taxon in the topology, the outcome is likely to be wrong and the error undetected. Given these limitations, using CGH to reveal phylogeny based on sequence divergence does not offer a robust alternative to traditional phylogenetic analysis.

Personalized nutrition (PN) approaches have been shown to help drive behavior change and positively influence health outcomes. This has led to an increase in the development of commercially available PN programs, which utilize various forms of individual-level information to provide services and products for consumers. The lack of a well-accepted definition of PN or an established set of guiding principles for the implementation of PN creates barriers for establishing credibility and efficacy. To address these points, the North American Branch of the International Life Sciences Institute convened a multidisciplinary panel. In this article, a definition for PN is proposed: "Personalized nutrition uses individual-specific information, founded in evidence-based science, to promote dietary behavior change that may result in measurable health benefits." In addition, 10 guiding principles for PN approaches are proposed: 1) define potential users and beneficiaries; 2) use validated diagnostic methods and measures; 3) maintain data quality and relevance; 4) derive data-driven recommendations from validated models and algorithms; 5) design PN studies around validated individual health or function needs and outcomes; 6) provide rigorous scientific evidence for an effect on health or function; 7) deliver user-friendly tools; 8) for healthy individuals, align with population-based recommendations; 9) communicate transparently about potential effects; and 10) protect individual data privacy and act responsibly. These principles are intended to establish a basis for responsible approaches to the evidence-based research and practice of PN and serve as an invitation for further public dialog. Several challenges were identified for PN to continue gaining acceptance, including defining the health-disease continuum, identification of biomarkers, changing regulatory landscapes, accessibility, and measuring success. Although PN approaches hold promise for public health in the future, further research is needed on the accuracy of dietary intake measurement, utilization and standardization of systems approaches, and application and communication of evidence.