Background: Selecting proper parameter settings for bioinformatic software tools is challenging. Not only will each parameter have an individual effect on the outcome, but there are also potential interaction effects between parameters. Both of these effects may be difficult to predict. Making the situation even more complex, multiple tools may be run in a sequential pipeline where the final output depends on the parameter configuration of each tool in the pipeline. Because of the complexity and difficulty to predict outcome, parameters are in practice often left at default settings or set based on personal or peer experience obtained in a trial and error-fashion. To allow reliable and efficient selection of parameters for bioinformatic pipelines, a systematic approach is needed. Results: We present doepipeline, a novel approach for optimizing bioinformatic software parameters, based on core concepts of the Design of Experiments methodology and recent advances in subset designs. Optimal parameter settings are first approximated in a screening phase using a subset design that efficiently span the entire search space, and subsequently optimized in the following phase using response surface designs and OLS modeling. Doepipeline was used to optimize parameters in three use cases; 1) de-novo assembly, 2) scaffolding of a fragmented assembly, and 3) k-mer taxonomic classification of nanopore reads. In all three cases, doepipeline found parameter settings producing a better outcome with respect to the measured characteristic when compared to using default values. Our approach is implemented and available in the Python package doepipeline. Conclusions: Our proposed methodology provides a systematic and robust framework to optimize software parameter settings, in contrast to labor- and time-intensive manual parameter tweaking. The implementation in doepipeline makes our methodology accessible and user-friendly, and allows for automatic optimization of tools in a wide range of cases. The source code of doepipeline is available at https://github.com/clicumu/doepipeline and is installable through conda-forge.
Journal: BioRxiv
DOI: 10.1101/504050
Year: 2018