Atomically detailed lipid bilayer models for the interpretation of small angle neutron and X-ray scattering data.

We present a new atom density profile (ADP) model and a statistical approach for extracting structural characteristics of lipid bilayers from X-ray and neutron scattering data. Models for five lipids with varying head and tail chemical composition in the fluid phase, 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine (DOPC), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC), 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine (DPPC), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylserine (POPS), and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylglycerol (POPG), are optimized using a simplex based method to simultaneously reproduce both neutron and X-ray scattering data. Structural properties are determined using statistical analysis of multiple optimal model structures. The method and models presented make minimal assumptions regarding the atomic configuration, while taking into account the underlying physical properties of the system. The more general model and statistical approach yield data with well defined uncertainties, indicating the precision in determining density profiles, atomic locations, and bilayer structural characteristics. Resulting bilayer structures include regions exhibiting large conformational variation. Due to the increased detail in the model, the results demonstrate the possibility of a distinct hydration layer within the interfacial (backbone) region.

One size does not fit all: on how Markov model order dictates performance of genomic sequence analyses.

The structural simplicity and ability to capture serial correlations make Markov models a popular modeling choice in several genomic analyses, such as identification of motifs, genes and regulatory elements. A critical, yet relatively unexplored, issue is the determination of the order of the Markov model. Most biological applications use a predetermined order for all data sets indiscriminately. Here, we show the vast variation in the performance of such applications with the order. To identify the 'optimal' order, we investigated two model selection criteria: Akaike information criterion and Bayesian information criterion (BIC). The BIC optimal order delivers the best performance for mammalian phylogeny reconstruction and motif discovery. Importantly, this order is different from orders typically used by many tools, suggesting that a simple additional step determining this order can significantly improve results. Further, we describe a novel classification approach based on BIC optimal Markov models to predict functionality of tissue-specific promoters. Our classifier discriminates between promoters active across 12 different tissues with remarkable accuracy, yielding 3 times the precision expected by chance. Application to the metagenomics problem of identifying the taxum from a short DNA fragment yields accuracies at least as high as the more complex mainstream methodologies, while retaining conceptual and computational simplicity.