Prediction of highly expressed genes in microbes based on chromatin accessibility

Background  

It is well known that gene expression is dependent on chromatin structure in eukaryotes and it is likely that chromatin can play a role in bacterial gene expression as well. Here, we use a nucleosomal position preference measure of anisotropic DNA flexibility to predict highly expressed genes in microbial genomes. We compare these predictions with those based on codon adaptation index (CAI) values, and also with experimental data for 6 different microbial genomes, with a particular interest in experimental data from Escherichia coli. Moreover, position preference is examined further in 328 sequenced microbial genomes.     

Hierarchical multi-scale occupancy estimation for monitoring wildlife populations

Occupancy estimation is an effective analytic framework, but requires repeated surveys of a sample unit to estimate the probability of detection. Detection rates can be estimated from spatially replicated rather than temporally replicated surveys, but this may violate the closure assumption and result in biased estimates of occupancy. We present a new application of a multi-scale occupancy model that permits the simultaneous use of presence–absence data collected at 2 spatial scales and uses a removal design to estimate the probability of detection. Occupancy at the small scale corresponds to local territory occupancy, whereas occupancy at the large scale corresponds to regional occupancy of the sample units. Small-scale occupancy also corresponds to a spatial availability or coverage parameter where a species may be unavailable for sampling at a fraction of the survey stations. We applied the multi-scale occupancy model to a hierarchical sample design for 2 bird species in the Black Hills National Forest: brown creeper (Certhia americana) and lark sparrow (Chondestes grammacus). Our application of the multi-scale occupancy model is particularly well suited for hierarchical sample designs, such as spatially replicated survey stations within sample units that are typical of avian monitoring programs. The model appropriately accounts for the non-independence of the spatially replicated survey stations, addresses the closure assumption for the spatially replicated survey stations, and is useful for decomposing the observation process into detection and availability parameters. This analytic approach is likely to be useful for monitoring at local and regional scales, modeling multi-scale habitat relationships, and estimating population state variables for rare species of conservation concern. © 2011 The Wildlife Society.