Emergence of bacterial resistance is a major issue for all classes of antibiotics; therefore, the identification of new classes is critically needed. Recently we reported the discovery of platensimycin by screening natural product extracts using a target-based whole-cell strategy with antisense silencing technology in concert with cell free biochemical validations. Continued screening efforts led to the discovery of platencin, a novel natural product that is chemically and biologically related but different from platensimycin. Platencin exhibits a broad-spectrum Gram-positive antibacterial activity through inhibition of fatty acid biosynthesis. It does not exhibit cross-resistance to key antibiotic resistant strains tested, including methicillin-resistant Staphylococcus aureus, vancomycin-intermediate S. aureus, and vancomycin-resistant Enterococci. Platencin shows potent in vivo efficacy without any observed toxicity. It targets two essential proteins, ␤-ketoacyl-[acyl carrier protein (ACP)] synthase II (FabF) and III (FabH) with IC 50 values of 1.95 and 3.91 g/ml, respectively, whereas platensimycin targets only FabF (IC 50 ‫؍‬ 0.13 g/ml) in S. aureus, emphasizing the fact that more antibiotics with novel structures and new modes of action can be discovered by using this antisense differential sensitivity wholecell screening paradigm.platensimycin ͉ natural product ͉ thiolactomycin ͉ antisense ͉ fatty acid synthesis

Canonical correlations analysis (CCA) is an exploratory statistical method to highlight correlations between two data sets acquired on the same experimental units. The cancor() function in R (R Development Core Team 2007) performs the core of computations but further work was required to provide the user with additional tools to facilitate the interpretation of the results. We implemented an R package, CCA, freely available from the Comprehensive R Archive Network (CRAN, http://CRAN.R-project.org/), to develop numerical and graphical outputs and to enable the user to handle missing values. The CCA package also includes a regularized version of CCA to deal with data sets with more variables than units. Illustrations are given through the analysis of a data set coming from a nutrigenomic study in the mouse.

Motivation: With the availability of many ‘omics’ data, such as transcriptomics, proteomics or metabolomics, the integrative or joint analysis of multiple datasets from different technology platforms is becoming crucial to unravel the relationships between different biological functional levels. However, the development of such an analysis is a major computational and technical challenge as most approaches suffer from high data dimensionality. New methodologies need to be developed and validated.Results: efficiently performs integrative analyses of two types of ‘omics’ variables that are measured on the same samples. It includes a regularized version of canonical correlation analysis to enlighten correlations between two datasets, and a sparse version of partial least squares (PLS) regression that includes simultaneous variable selection in both datasets. The usefulness of both approaches has been demonstrated previously and successfully applied in various integrative studies.Availability: is freely available from http://CRAN.R-project.org/ or from the web site companion (http://math.univ-toulouse.fr/biostat) that provides full documentation and tutorials.Contact: k.lecao@uq.edu.auSupplementary information: Supplementary data are available at Bioinformatics online.