SUMMARY: Recently, a number of powerful computational tools for dissecting tumor-immune cell interactions from next-generation sequencing data have been developed. However, the assembly of analytical pipelines and execution of multi-step workflows are laborious and involve a large number of intermediate steps with many dependencies and parameter settings. Here we present TIminer, an easy-to-use computational pipeline for mining tumor-immune cell interactions from next-generation sequencing data. TIminer enables integrative immunogenomic analyses, including: human leukocyte antigens typing, neoantigen prediction, characterization of immune infiltrates and quantification of tumor immunogenicity. AVAILABILITY AND IMPLEMENTATION: TIminer is freely available at http://icbi.i-med.ac.at/software/timiner/timiner.shtml. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Although the study of static and dynamic type systems plays a major role in research, relatively little is known about the impact of type systems on software development. Perhaps one of the more common arguments for static type systems in languages such as Java or C++ is that they require developers to annotate their code with type names, which is thus claimed to improve the documentation of software. In contrast, one common argument against static type systems is that they decrease flexibility, which may make them harder to use. While these arguments are found in the literature, rigorous empirical evidence is lacking. We report on a controlled experiment where 27 subjects performed programming tasks on an undocumented API with a static type system (requiring type annotations) as well as a dynamic type system (which does not). Our results show that for some tasks, programmers had faster completion times using a static type system, while for others, the opposite held. We conduct an exploratory study to try and theorize why.