DEGoldS: a workflow to assess the accuracy of differential expression analysis pipelines through gold-standard construction

Abstract

RNA sequencing (RNA-seq) is a high throughput sequencing method that has become one the most employed tools in transcriptomics. The implementation of optimal bioinformatic analyses required in RNA-seq experiments may be complicated due to the large amounts of data generated by the sequencing platforms, along with the intrinsic nature of these data types. In the last years many programs and pipelines have been developed for differential expression (DE) analyses, but their effectiveness can be reduced when working with non-model species lacking public genomic resources. Moreover, there is not a universal recipe for all the experiments and datasets and the modification of standard RNA-seq bioinformatic pipelines through parameter tuning and the use of alternative software may have a strong impact in the outcome of DE analysis. Therefore, although the selection of the most accurate DE pipeline configuration and the evaluation of how these changes could affect the final DE results in RNA-seq experiments is mandatory to reduce bias, the lack of gold-standard datasets with known expression patterns hampers its implementation. In the present manuscript we present DEGoldS, a workflow consisting on sequential Bash and R scripts to construct gold-standards for simulation-based benchmarking of user selected pipelines for DE analysis and the computation of the accuracy of the pipelines. We validated the workflow with a case study consisting on real RNA-seq libraries of radiata pine, an important forest tree species with no publicly available reference genome. The results showed that slight pipeline modifications produced remarkable differences in the outcome of DE analysis.