SUMMARY: We developed the MicrobiomeExplorer R package to facilitate the analysis and visualization of microbial communities. The MicrobiomeExplorer R package allows a user to perform typical microbiome analytic workflows and visualize their results, either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows, the application enables automated analysis report generation. AVAILABILITY AND IMPLEMENTATION: Available at https://github.com/zoecastillo/microbiomeExplorer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We describe a graphical programming system for a domain specific language in biosequence analysis. It supports the development of programs for RNA structure prediction and motif search, created by biologists with little or no programming skills. The system combines several programming paradigms in a productive way. It has a client - server architecture, with a transport layer in XML. The graphical front-end is implemented in the object-oriented paradigm (using Java). Graphics are compiled into a declarative domain-specific language for dynamic programming (ADP) that is embedded in Haskell. Finally, motif search programs expressed in ADP are compiled to imperative code in C, a step which includes substantial domain-specific optimization.

BACKGROUND: Ambiguity is a problem in biosequence analysis that arises in various analysis tasks solved via dynamic programming, and in particular, in the modeling of families of RNA secondary structures with stochastic context free grammars. Several types of analysis are invalidated by the presence of ambiguity. As this problem inherits undecidability (as we show here) from the namely problem for context free languages, there is no complete algorithmic solution to the problem of ambiguity checking. RESULTS: We explain frequently observed sources of ambiguity, and show how to avoid them. We suggest four testing procedures that may help to detect ambiguity when present, including a just-in-time test that permits to work safely with a potentially ambiguous grammar. We introduce, for the special case of stochastic context free grammars and RNA structure modeling, an automated partial procedure for proving non-ambiguity. It is used to demonstrate non-ambiguity for several relevant grammars. CONCLUSION: Our mechanical proof procedure and our testing methods provide a powerful arsenal of methods to ensure non-ambiguity.