RIBOSS detects novel translational events by combining long- and short-read transcriptome and translatome profiling

Abstract

Ribosome profiling is a high-throughput sequencing technique that captures the positions of translating ribosomes on RNAs. Recent advancements in ribosome profiling include achieving highly-phased ribosome footprints for plant translatomes and more recently for bacterial translatomes. This substantially increases the specificity of detecting open reading frames (ORFs) that can be translated, such as small ORFs located upstream and downstream of the annotated ORFs. However, most genomes (e.g. bacterial genomes) lack the annotations for the transcription start and termination sites. This hinders the systematic discovery of novel ORFs in the untranslated regions in ribosome profiling data. Here we develop a new computational pipeline called RIBOSS. We use RIBOSS to leverage long-read and short-read data for de novo transcriptome assembly, and highly-phased ribosome profiling data for detecting novel translational events in the newly assembled transcriptome. We demonstrate the capability of RIBOSS using recently published metatranscriptome and translatome data for Salmonella enterica serovar Typhimurium. The RIBOSS Python modules are versatile and can be used to analyse prokaryotic or eukaryotic data. In sum, RIBOSS is the first computational pipeline to integrate long- and short-read sequencing technologies to investigate translation. RIBOSS is freely available at https://github.com/lcscs12345/riboss.