Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM)

Core outcome measures for opioid abuse liability laboratory assessment studies in humans: IMMPACT recommendations

A critical component in development of opioid analgesics is assessment of their abuse liability (AL). Standardization of approaches and measures used in assessing AL have the potential to facilitate comparisons across studies, research laboratories, and drugs. The goal of this report is to provide consensus recommendations regarding core outcome measures for assessing the abuse potential of opioid medications in humans in a controlled laboratory setting. Although many of the recommended measures are appropriate for assessing the AL of medications from other drug classes, the focus here is on opioid medications because they present unique risks from both physiological (e.g., respiratory depression, physical dependence) and public health (e.g., individuals in pain) perspectives. A brief historical perspective on AL testing is provided, and those measures that can be considered primary and secondary outcomes and possible additional outcomes in AL assessment are then discussed. These outcome measures include the following: subjective effects (some of which comprise the primary outcome measures, including drug liking; physiological responses; drug self-administration behavior; and cognitive and psychomotor performance. Before presenting recommendations for standardized approaches and measures to be used in AL assessments, the appropriateness of using these measures in clinical trials with patients in pain is discussed