Assembly models for Papovaviridae based on tiling theory

A vital constituent of a virus is its protein shell, called the viral capsid, that encapsulates and hence provides protection for the viral genome. Assembly models are developed for viral capsids built from protein building blocks that can assume different local bonding structures in the capsid. This situation occurs, for example, for viruses in the family of Papovaviridae, which are linked to cancer and are hence of particular interest for the health sector. More specifically, the viral capsids of the (pseudo-) T = 7 particles in this family consist of pentamers that exhibit two different types of bonding structures. While this scenario cannot be described mathematically in terms of Caspar-Klug theory (Caspar D L D and Klug A 1962 Cold Spring Harbor Symp. Quant. Biol. 27 1), it can be modelled via tiling theory (Twarock R 2004 J. Theor. Biol. 226 477). The latter is used to encode the local bonding environment of the building blocks in a combinatorial structure, called the assembly tree, which is a basic ingredient in the derivation of assembly models for Papovaviridae along the lines of the equilibrium approach of Zlotnick (Zlotnick A 1994 J. Mol. Biol. 241 59). A phase space formalism is introduced to characterize the changes in the assembly pathways and intermediates triggered by the variations in the association energies characterizing the bonds between the building blocks in the capsid. Furthermore, the assembly pathways and concentrations of the statistically dominant assembly intermediates are determined. The example of Simian virus 40 is discussed in detail.     

Efficient oligonucleotide probe selection for pan-genomic tiling arrays

Background  

Array comparative genomic hybridization is a fast and cost-effective method for detecting, genotyping, and comparing the genomic sequence of unknown bacterial isolates. This method, as with all microarray applications, requires adequate coverage of probes targeting the regions of interest. An unbiased tiling of probes across the entire length of the genome is the most flexible design approach. However, such a whole-genome tiling requires that the genome sequence is known in advance. For the accurate analysis of uncharacterized bacteria, an array must query a fully representative set of sequences from the species' pan-genome. Prior microarrays have included only a single strain per array or the conserved sequences of gene families. These arrays omit potentially important genes and sequence variants from the pan-genome.     

Generalized hierarchical markov models for the discovery of length-constrained sequence features from genome tiling arrays

A generalized hierarchical Markov model for sequences that contain length-restricted features is introduced. This model is motivated by the recent development of high-density tiling array data for determining genomic elements of functional importance. Due to length constraints on certain features of interest, as well as variability in probe behavior, usual hidden Markov-type models are not always applicable. A robust Bayesian framework that can incorporate length constraints, probe variability, and bias is developed. Moreover, a novel recursion-based Monte Carlo algorithm is proposed to estimate the parameters and impute hidden states under length constraints. Application of this methodology to yeast chromosomal arrays demonstrate substantial improvement over currently existing methods in terms of sensitivity as well as biological interpretability.     

Selecting oligonucleotide probes for whole-genome tiling arrays with a cross-hybridization potential

For designing oligonucleotide tiling arrays popular, current methods still rely on simple criteria like Hamming distance or longest common factors, neglecting base stacking effects which strongly contribute to binding energies. Consequently, probes are often prone to cross-hybridization which reduces the signal-to-noise ratio and complicates downstream analysis. We propose the first computationally efficient method using hybridization energy to identify specific oligonucleotide probes. Our Cross-Hybridization Potential (CHP) is computed with a Nearest Neighbor Alignment, which efficiently estimates a lower bound for the Gibbs free energy of the duplex formed by two DNA sequences of bounded length. It is derived from our simplified reformulation of t-gap insertion-deletion-like metrics. The computations are accelerated by a filter using weighted ungapped q-grams to arrive at seeds. The computation of the CHP is implemented in our software OSProbes, available under the GPL, which computes sets of viable probe candidates. The user can choose a trade-off between running time and quality of probes selected. We obtain very favorable results in comparison with prior approaches with respect to specificity and sensitivity for cross-hybridization and genome coverage with high-specificity probes. The combination of OSProbes and our Tileomatic method, which computes optimal tiling paths from candidate sets, yields globally optimal tiling arrays, balancing probe distance, hybridization conditions, and uniqueness of hybridization.     

FunSys: Software for functional analysis of prokaryotic transcriptome and proteome

The vast amount of data produced by next-generation sequencing (NGS) has necessitated the development of computational tools to assist in understanding the myriad functions performed by the biological macromolecules involved in heredity. In this work, we developed the FunSys programme, a stand-alone tool with an user friendly interface that enables us to evaluate and correlate differential expression patterns from RNA sequencing and proteomics datasets. The FunSys generates charts and reports based on the results of the analysis of differential expression to aid the interpretation of the results. AVAILABILITY: The database is available for free at https://sourceforge.net/projects/funsysufpa/     

Glycan arrays for functional glycomics

Interactions between carbohydrates and proteins mediate intracellular traffic, cell adhesion, cell recognition and immune system function. Two recent papers describe how arrays of oligosaccharide and polysaccharide molecules can be used to investigate these interactions more fully.     

A minimal tiling path cosmid library for functional analysis of the Pseudomonas aeruginosa PAO1 genome

Pseudomonas aeruginosa is an important pathogenic and environmental bacterium, with the most widely studied strain being PAO1. Using the PAO1 reference cosmid library and the recently completed PAO1 genome sequence, we have mapped a minimal tiling path across the genome using a two-step strategy. First, we sequenced both ends of a set of over 500 random and previously mapped clones to create a backbone. Second, we end-sequenced a second set of cosmid clones that were identified to lie within the larger gaps using hybridization of the reference library filters with probes designed against sequences at the center of each gap. The minimal tiling path was calculated using the program Domino (http://www.bit.uq.edu.au/download/), with the overlap between adjacent clones set to 5 kb (where possible) to minimize the chance of truncating genes. This yielded a minimal tiling cosmid library (334 clones) covering 93.7% of the genome in 57 contigs. This library has reduced to a workable set the number of clones required to represent the majority of the P. aeruginosa genome and gives the precise location of each cosmid, enabling most genes of interest to be located on clones without further screening. This library should prove a useful resource to accelerate functional analysis of the P. aeruginosa genome.     

Large-scale functional analysis using peptide or protein arrays

The array format for analyzing peptide and protein function offers an attractive experimental alternative to traditional library screens. Powerful new approaches have recently been described, ranging from synthetic peptide arrays to whole proteins expressed in living cells. Comprehensive sets of purified peptides and proteins permit high-throughput screening for discrete biochemical properties, whereas formats involving living cells facilitate large-scale genetic screening for novel biological activities. In the past year, three major genome-scale studies using yeast as a model organism have investigated different aspects of protein function, including biochemical activities, gene disruption phenotypes, and protein-protein interactions. Such studies show that protein arrays can be used to examine in parallel the functions of thousands of proteins previously known only by their DNA sequence.