Rapid and reliable virus subtype identification is critical for accurate diagnosis of human infections, effective response to epidemic outbreaks and global-scale surveillance of highly pathogenic viral subtypes such as avian influenza H5N1. The polymerase chain reaction (PCR) has become the method of choice for virus subtype identification. However, designing subtype-specific PCR primer pairs is a very challenging task: on one hand, selected primer pairs must result in robust amplification in the presence of a significant degree of sequence heterogeneity within subtypes, on the other, they must discriminate between the subtype of interest and closely related subtypes. In this article, we present a new tool, called PrimerHunter, that can be used to select highly sensitive and specific primers for virus subtyping. Our tool takes as input sets of both target and nontarget sequences. Primers are selected such that they efficiently amplify any one of the target sequences, and none of the nontarget sequences. PrimerHunter ensures the desired amplification properties by using accurate estimates of melting temperature with mismatches, computed based on the nearest neighbor model via an efficient fractional programming algorithm. Validation experiments with three avian influenza HA subtypes confirm that primers selected by PrimerHunter have high sensitivity and specificity for target sequences.

Here we identify duplicated genes in five mammalian genomes and classify these duplicates based on the mechanisms by which they were generated. Retrotransposition accounts for at least half of all predicted duplicate genes in these genomes, with tandem and interspersed DNA-mediated duplicates comprising the other half. Estimation of the evolutionary rates in each class revealed greater rate asymmetry between retrotransposed and interspersed DNA duplicate pairs than between tandem duplicates, suggesting that retrotransposed and interspersed DNA duplicates are diverging more quickly. In an attempt to understand the basis of this asymmetry, we identified disruption of flanking DNA as an indicator of new duplicate fate-loss of local synteny accelerates the asymmetry of divergence of interspersed DNA duplicates. We also show that intact retrogenes are enriched in intergenic regions and indel purified regions of the human genome. Moreover, intact retrogenes closest to annotated genes show the greatest levels of purifying selective pressure. Together, these findings suggest that the differential evolution of duplicate genes may be significantly influenced by changes in local genome architecture.

BackgroundThere is an ever-expanding range of technologies that generate very large numbers of biomarkers for research and clinical applications. Choosing the most informative biomarkers from a high-dimensional data set, combined with identifying the most reliable and accurate classification algorithms to use with that biomarker set, can be a daunting task. Existing surveys of feature selection and classification algorithms typically focus on a single data type, such as gene expression microarrays, and rarely explore the model's performance across multiple biological data types.ResultsThis paper presents the results of a large scale empirical study whereby a large number of popular feature selection and classification algorithms are used to identify the tissue of origin for the NCI-60 cancer cell lines. A computational pipeline was implemented to maximize predictive accuracy of all models at all parameters on five different data types available for the NCI-60 cell lines. A validation experiment was conducted using external data in order to demonstrate robustness.ConclusionsAs expected, the data type and number of biomarkers have a significant effect on the performance of the predictive models. Although no model or data type uniformly outperforms the others across the entire range of tested numbers of markers, several clear trends are visible. At low numbers of biomarkers gene and protein expression data types are able to differentiate between cancer cell lines significantly better than the other three data types, namely SNP, array comparative genome hybridization (aCGH), and microRNA data.Interestingly, as the number of selected biomarkers increases best performing classifiers based on SNP data match or slightly outperform those based on gene and protein expression, while those based on aCGH and microRNA data continue to perform the worst. It is observed that one class of feature selection and classifier are consistently top performers across data types and number of markers, suggesting that well performing feature-selection/classifier pairings are likely to be robust in biological classification problems regardless of the data type used in the analysis.

Gene duplication has long been recognized as a major force in genome evolution and has recently been recognized as an important source of individual variation. For many years, the origin of functional gene duplicates was assumed to be whole or partial genome duplication events, but recently retrotransposition has also been shown to contribute new functional protein coding genes and siRNA's. In this study, we utilize pseudogenes to recreate more complete gene family histories, and compare the rates of RNA and DNA-mediated duplication and new functional gene formation in five mammalian genomes. We find that RNA-mediated duplication occurs at a much higher and more variable rate than DNA-mediated duplication, and gives rise to many more duplicated sequences over time. We show that, while the chance of RNA-mediated duplicates becoming functional is much lower than that of their DNA-mediated counterparts, the higher rate of retrotransposition leads to nearly equal contributions of new genes by each mechanism. We also find that functional RNA-mediated duplicates are closer to neighboring genes than non-functional RNA-mediated copies, consistent with co-option of regulatory elements at the site of insertion. Overall, new genes derived from DNA and RNA-mediated duplication mechanisms are under similar levels of purifying selective pressure, but have broadly different functions. RNA-mediated duplication gives rise to a diversity of genes but is dominated by the highly expressed genes of RNA metabolic pathways. DNA-mediated duplication can copy regulatory material along with the protein coding region of the gene and often gives rise to classes of genes whose function are dependent on complex regulatory information. This mechanistic difference may in part explain why we find that mammalian protein families tend to evolve by either one mechanism or the other, but rarely by both. Supplementary Material has been provided (see online Supplementary Material at www.liebertonline.com ).

Stem cell biology has experienced explosive growth over the past decade as researchers attempt to generate therapeutically relevant cell types in the laboratory. Recapitulation of endogenous developmental trajectories is a dominant paradigm in the design of directed differentiation protocols, and attempts to guide stem cell differentiation are often based explicitly on knowledge of in vivo development. Therefore, when designing protocols, stem cell biologists rely heavily upon information including (i) cell type-specific gene expression profiles, (ii) anatomical and developmental relationships between cells and tissues and (iii) signals important for progression from progenitors to target cell types. Here, we present the Stem Cell Lineage Database (SCLD) (http://scld.mcb.uconn.edu) that aims to unify this information into a single resource where users can easily store and access information about cell type gene expression, cell lineage maps and stem cell differentiation protocols for both human and mouse stem cells and endogenous developmental lineages. By establishing the SCLD, we provide scientists with a centralized location to organize access and share data, dispute and resolve contentious relationships between cell types and within lineages, uncover discriminating cell type marker panels and design directed differentiation protocols.