These are probably the major opportunities for significant performance gains in the near future ( 2–5).
In this context, web-based applications allow developers to take advantage of the increased availability of multicore processors and clusters built with off-the-shelf components. Moreover, and of particular relevance, when dealing with large datasets, computational capabilities are not limited by the user's hardware (only by the servers').
#Pomello san francisco software
For end-users, a key feature of web-based applications is that they make few demands on users' software and hardware, since only a web browser is needed ( 1). There is a continuous demand for web-based applications for the analysis of genomic and proteomic data. The possibility of including additional covariates, parallelization of computation, open-source availability of the code and comprehensive testing suite make Pomelo II a unique tool. A comprehensive test suite is also available, and covers both the user interface and the numerical results. The source code is available, allowing for extending and reusing the software. Access to, and further analysis of, additional biological information and annotations (PubMed references, Gene Ontology terms, KEGG and Reactome pathways) are available either for individual genes (from clickable links in tables and figures) or sets of genes. Permutation-based and Cox model analysis use parallel computing, which permits taking advantage of multicore CPUs and computing clusters. Pomelo II implements: permutation-based tests for class comparisons ( t-test, ANOVA) and regression survival analysis using Cox model contingency table analysis with Fisher's exact test linear models (of which t-test and ANOVA are especial cases) that allow additional covariates for complex experimental designs and use empirical Bayes moderated statistics.
Pomelo II ( ) is an open-source, web-based, freely available tool for the analysis of gene (and protein) expression and tissue array data.
0 kommentar(er)
