Abstract
Ribosome profiling (Ribo-seq) can potentially provide detailed information about ribosome position on transcripts and estimates of protein translation levels
in-vivo
. Hsp90 chaperones, which play a critical role in stress tolerance, have characteristic patterns of differential expression under non-stressed and heat shock conditions. By analyzing published Ribo-seq data for the Hsp90 chaperones in
S. cerevisiae
we find wide-ranging artifacts originating from “multi-mapping” reads (reads that cannot be uniquely assigned to one position), which constitute ~25% of typical
S. cerevisiae
Ribo-seq datasets and ~80% of the reads from HEK293 cells. Estimates of Hsp90 protein production as determined by Ribo-seq are reproducible but not robust, with inferred expression levels that can change ten-fold depending on how multi-mapping reads are processed. The differential expression of Hsp90 chaperones under non-stressed and heat shock conditions creates artificial peaks and valleys in their ribosome profiles that give a false impression of regulated translational pausing. Indeed, we find that multi-mapping can even create an appearance of reproducibility to the shape of the Hsp90 ribosome profiles from biological replicates. Adding further complexity, this artificial reproducibility is dependent on the computational method used to construct the ribosome profile. Given the ubiquity of multi-mapping reads in Ribo-seq experiments and the complexity of artifacts associated with multi-mapping, we developed a publicly available computational tool to identify transcripts most at risk for multi-mapping artifacts. In doing so, we identify biological pathways that are enriched in multi-mapping transcripts, meaning that particular biological pathways will be highly susceptible to multi-mapping artifacts.