Many scientists, if not all, feel that their particular plant virus should appear in any list of the most important plant viruses. However, to our knowledge, no such list exists. The aim of this review was to survey all plant virologists with an association with Molecular Plant Pathology and ask them to nominate which plant viruses they would place in a 'Top 10' based on scientific/economic importance. The survey generated more than 250 votes from the international community, and allowed the generation of a Top 10 plant virus list for Molecular Plant Pathology. The Top 10 list includes, in rank order, (1) Tobacco mosaic virus, (2) Tomato spotted wilt virus, (3) Tomato yellow leaf curl virus, (4) Cucumber mosaic virus, (5) Potato virus Y, (6) Cauliflower mosaic virus, (7) African cassava mosaic virus, (8) Plum pox virus, (9) Brome mosaic virus and (10) Potato virus X, with honourable mentions for viruses just missing out on the Top 10, including Citrus tristeza virus, Barley yellow dwarf virus, Potato leafroll virus and Tomato bushy stunt virus. This review article presents a short review on each virus of the Top 10 list and its importance, with the intent of initiating discussion and debate amongst the plant virology community, as well as laying down a benchmark, as it will be interesting to see in future years how perceptions change and which viruses enter and leave the Top 10.

We show that brome mosaic virus (BMV) RNA replication protein 1a, 2a polymerase, and a cis-acting replication signal recapitulate the functions of Gag, Pol, and RNA packaging signals in conventional retrovirus and foamy virus cores. Prior to RNA replication, 1a forms spherules budding into the endoplasmic reticulum membrane, sequestering viral positive-strand RNA templates in a nuclease-resistant, detergent-susceptible state. When expressed, 2a polymerase colocalizes in these spherules, which become the sites of viral RNA synthesis and retain negative-strand templates for positive-strand RNA synthesis. These results explain many features of replication by numerous positive strand RNA viruses and reveal that these viruses, reverse transcribing viruses, and dsRNA viruses share fundamental similarities in replication and may have common evolutionary origins.

Mixture modeling provides an effective approach to the differential expression problem in microarray data analysis. Methods based on fully parametric mixture models are available, but lack of fit in some examples indicates that more flexible models may be beneficial. Existing, more flexible, mixture models work at the level of one-dimensional gene-specific summary statistics, and so when there are relatively few measurements per gene these methods may not provide sensitive detectors of differential expression. We propose a hierarchical mixture model to provide methodology that is both sensitive in detecting differential expression and sufficiently flexible to account for the complex variability of normalized microarray data. EM-based algorithms are used to fit both parametric and semiparametric versions of the model. We restrict attention to the two-sample comparison problem; an experiment involving Affymetrix microarrays and yeast translation provides the motivating case study. Gene-specific posterior probabilities of differential expression form the basis of statistical inference; they define short gene lists and false discovery rates. Compared to several competing methodologies, the proposed methodology exhibits good operating characteristics in a simulation study, on the analysis of spike-in data, and in a cross-validation calculation.