Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines

Background

Recent advances in genome technologies and the subsequent collection of genomic information at various molecular resolutions hold promise to accelerate the discovery of new therapeutic targets. A critical step in achieving these goals is to develop efficient clinical prediction models that integrate these diverse sources of high-throughput data. This step is challenging due to the presence of high-dimensionality and complex interactions in the data. For predicting relevant clinical outcomes, we propose a flexible statistical machine learning approach that acknowledges and models the interaction between platform-specific measurements through nonlinear kernel machines and borrows information within and between platforms through a hierarchical Bayesian framework. Our model has parameters with direct interpretations in terms of the effects of platforms and data interactions within and across platforms. The parameter estimation algorithm in our model uses a computationally efficient variational Bayes approach that scales well to large high-throughput datasets.

Results

We apply our methods of integrating gene/mRNA expression and microRNA profiles for predicting patient survival times to The Cancer Genome Atlas (TCGA) based glioblastoma multiforme (GBM) dataset. In terms of prediction accuracy, we show that our non-linear and interaction-based integrative methods perform better than linear alternatives and non-integrative methods that do not account for interactions between the platforms. We also find several prognostic mRNAs and microRNAs that are related to tumor invasion and are known to drive tumor metastasis and severe inflammatory response in GBM. In addition, our analysis reveals several interesting mRNA and microRNA interactions that have known implications in the etiology of GBM.

Conclusions

Our approach gains its flexibility and power by modeling the non-linear interaction structures between and within the platforms. Our framework is a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers. We have a freely available software at: http://odin.mdacc.tmc.edu/~vbaladan.

     

Home range size of adult Indo‐Pacific bottlenose dolphins (Tursiops aduncus) in a coastal and estuarine system is habitat and sex‐specific

This study examined sex‐specific differences in home range size of adult Indo‐Pacific bottlenose dolphins off Bunbury, Western Australia. We applied a new kernel density estimation approach that accounted for physical barriers to movements. A Bayesian mixture model was developed to estimate a sex effect in home range size with latent group partitioning constrained by association data. A post hoc analysis investigated group partitioning relating to the proportion of time spent in open vs. sheltered waters. From 2007 to 2013, photographic‐identification data were collected along boat‐based systematic transect lines (n = 586). Analyses focused on adult dolphins of known sex (sighted ≥ 30 times; n = 22 males and 34 females). The 95% utilization distributions of males varied between 27 and 187 km² (; 94.8 ± 48.15) and for females between 20 and 133 km² (65.6 ± 30.9). The mixture model indicated a 99% probability that males had larger home ranges than females. Dolphins mostly sighted in open waters had larger home ranges than those in sheltered waters. Home ranges of dolphins sighted in sheltered waters overlapped with areas of highest human activity. We suggest that sex differences in home ranges are driven by male mating strategies, and home range size differences between habitats may be influenced by prey availability and predation risk.     

Production of high activity Aspergillus niger BCC4525 β-mannanase in Pichia pastoris and its application for mannooligosaccharides production from biomass hydrolysis

A cDNA encoding β-mannanase was cloned from Aspergillus niger BCC4525 and expressed in Pichia pastoris KM71. The secreted enzyme hydrolyzed locust bean gum substrate with very high activity (1625 U/mL) and a relatively high k_cat/K_m (461 mg^?1 s^?1 mL). The enzyme is thermophilic and thermostable with an optimal temperature of 70 °C and 40% retention of endo-β-1,4-mannanase activity after preincubation at 70 °C. In addition, the enzyme exhibited broad pH stability with an optimal pH of 5.5. The recombinant enzyme hydrolyzes low-cost biomass, including palm kernel meal (PKM) and copra meal, to produce mannooligosaccharides, which is used as prebiotics to promote the growth of beneficial microflora in animals. An in vitro digestibility test simulating the gastrointestinal tract system of broilers suggested that the recombinant β-mannanase could effectively liberate reducing sugars from PKM-containing diet. These characteristics render this enzyme suitable for utilization as a feed additive to improve animal performance.     

Morphological variation associated with dispersal capacity in a tree‐killing bark beetle Dendroctonus ponderosae Hopkins

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