Motivation:

The vast amount of already available and currently generated read mapping data re-quires comprehensive visualization, and should benefit from bioinformatics tools offering a wide spec-trum of analysis functionality from just one source. Appropriate handling of multiple mapped reads during mapping analyses remains an issue that demands improvement.

Results:

The capabilities of the read mapping analysis and visualization tool ReadXplorer were vastly enhanced. Here, we present an even finer granulated read mapping classification, improving the level of detail for analyses and visualizations. The spectrum of automatic analysis functions has been broadened to include genome rearrangement detection as well as correlation analysis between two mapping data sets. Existing functions were refined and enhanced, namely the computation of differ-entially expressed genes, the read count and normalization analysis and the transcription start site (TSS) detection. Additionally, ReadXplorer 2 features a highly improved support for large eukaryotic data sets and a command line version, enabling its integration into workflows. Finally, the new version is now able to display any kind of tabular results from other bioinformatics tools.

Availability:

http://www.readxplorer.org