Ribosome profiling (Ribo-Seq) reveals genome-wide translation rates via the quantification of ribosome protected fragments (RPFs) of mRNAs. Several methods have recently been developed to detect differentially translated genes (DTGs) using Ribo-seq: Xtail, Ribodiff and Riborex. At their core, all of these approaches either utilize existing differential expression programs or use similar statistical assumptions to model the data. However, none of them allow for complex experimental design or the use of alternative statistical setups and crucially, they do not allow for correction of any batch effects. We tailored the open design of a well established tool, DEseq2 to identify DTGs directly which can then also be extended to accommodate covariates and other experimental setups, making it a more suitable tool for identifying DTGs. We performed a comprehensive benchmarking analysis on simulated and primary human fibroblast dataset and show that this approach outperforms all the other methods in presence of a batch effect. With increasing batch effect, the sensitivity of DESeq drops by 22.7%, whereas all other methods drop by greater than 80%, making them substantially less reliable. Since almost all high-throughput sequencing datasets contain batch effects, particularly heterogeneous samples such as human tissues or primary