A Regression-Based Analysis of Ribosome-Profiling Data Reveals a Conserved Complexity to Mammalian Translation

摘要: A fundamental goal of genomics is to identify the complete set expressed proteins. Automated annotation strategies rely on assumptions about protein-coding sequences (CDSs), e.g., they are conserved, do not overlap, and exceed a minimum length. However, an increasing number newly discovered proteins violate these rules. Here we present experimental analytical framework, based ribosome profiling linear regression, for systematic identification quantification translation. Application this approach lipopolysaccharide-stimulated mouse dendritic cells HCMV-infected human fibroblasts identifies thousands novel CDSs, including micropeptides variants known proteins, that bear hallmarks canonical translation exhibit levels dynamics comparable annotated CDSs. Remarkably, many events identified in both even when peptide sequence conserved. Our work thus reveals unexpected complexity mammalian suited provide conserved regulatory or protein-based functions.