The second is to enable and support an active and open scientific community developing and distributing algorithms and software in bioinformatics and computational biology. The support includes guidance and training on software development and documentation, as well as the use of appropriate programming paradigms such as unit testing and judicious optimization. A primary goal is the distributed development of interoperable software components by scientific domain experts. In part we achieve this by urging the use of common data structures that enable workflows integrating multiple data types and disciplines. To facilitate research and innovation, we employ a high-level programming language. This choice yields rapid prototyping, creativity, flexibility and reproducibility in a way that neither point-and-click software nor a general-purpose programming language can. We have embraced R for its scientific and statistical computing capabilities, for its graphics facilities and for the convenience of an interpreted language. R also interfaces with low-level languages including C and C++ for computationally intensive operations, Java for integration with enterprise software and JavaScript for interactive web-based applications and reports.
