GeneSigDB: a manually curated database and resource for analysis of gene expression signatures

GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a ‘basket’ feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org

An international standardization programme towards the application of gene expression profiling in routine leukaemia diagnostics: the Microarray Innovations in LEukemia study prephase

Gene expression profiling has the potential to enhance current methods for the diagnosis of haematological malignancies. Here, we present data on 204 analyses from an international standardization programme that was conducted in 11 laboratories as a prephase to the Microarray Innovations in LEukemia (MILE) study. Each laboratory prepared two cell line samples, together with three replicate leukaemia patient lysates in two distinct stages: (i) a 5-d course of protocol training, and (ii) independent proficiency testing. Unsupervised, supervised, and r2 correlation analyses demonstrated that microarray analysis can be performed with remarkably high intra-laboratory reproducibility and with comparable quality and reliability

Mass segregation in young compact star clusters in the Large Magellanic Cloud: I. Data and Luminosity Functions

We have undertaken a detailed analysis of HST/WFPC2 and STIS imaging observations, and of supplementary wide-field ground-based observations obtained with the NTT of two young ~10-25 Myr) compact star clusters in the LMC, NGC 1805 and NGC 1818. The ultimate goal of our work is to improve our understanding of the degree of primordial mass segregation in star clusters. This is crucial for the interpretation of observational luminosity functions (LFs) in terms of the initial mass function (IMF), and for constraining the universality of the IMF. We present evidence for strong luminosity segregation in both clusters. The LF slopes steepen with cluster radius; in both NGC 1805 and NGC 1818 the LF slopes reach a stable level well beyond the clusters' core or half-light radii. In addition, the brightest cluster stars are strongly concentrated within the inner ~4 R_hl. The global cluster LF, although strongly nonlinear, is fairly well approximated by the core or half-light LF; the (annular) LFs at these radii are dominated by the segregated high-luminosity stars, however. We present tentative evidence for the presence of an excess number of bright stars surrounding NGC 1818, for which we argue that they are most likely massive stars that have been collisionally ejected from the cluster core. We therefore suggest that the cores of massive young stars clusters undergo significant dynamical evolution, even on time-scales as short as ~25 Myr.Comment: 19 pages, incl. 10 embedded postscript figures, MNRAS, resubmitted (referee's comments included