The recent advent of ribosome profiling ? sequencing of short ribosome-bound fragments of mRNA ? has offered an unprecedented opportunity to interrogate the sequence features responsible for modulating translational rates. Nevertheless, numerous analyses of the first riboprofiling dataset have produced equivocal and often incompatible results. Here we analyze three independent yeast riboprofiling data sets, including two with much higher coverage than previously available, and find that all three show substantial technical sequence biases that confound interpretations of ribosomal occupancy. After accounting for these biases, we find no effect of previously implicated factors on ribosomal pausing. Rather, we find that incorporation of proline, whose unique side-chain stalls peptide synthesis in vitro, also slows the ribosome in vivo. We also reanalyze a recent method that reported positively charged amino acids as the major determinant of ribosomal stalling and demonstrate that its assumptions lead to false signals of stalling in low-coverage data. Our results suggest that any analysis of riboprofiling data should account for sequencing biases and sparse coverage. To this end, we establish a robust methodology that enables analysis of ribosome profiling data without prior assumptions regarding which positions spanned by the ribosome cause stalling.

Accounting for biases in riboprofiling data indicates a major role for proline and not positive amino acids in stalling translation