A framework for collaborative computational research

Analysis of high troughput biological data often involves the use of many software packages and in-house written code. For each analysis step there are multiple options of software tools available, each with its own capabilities, limitations and assumptions on the input and output data. The development of bioinformatics pipelines involves a great deal of experimentation with different tools and parameters, considering how each would fit to the big picture and the practical implications of their use. Organizing data analysis could prove challenging. In this work we present a set of methods and tools that aim to enable the user to experiment extensively, while keeping analyses reproducible and organized. We present a framework based on simple principles that allow data analyses to be structured in a way that emphasizes reproducibility, organization and clarity, while being simple and intuitive so that adding and modifying analysis steps can be done naturally with little extra effort. The framework suppports version control of code, documentation and data, enabling collaboration between users.