In the world of molecular biology, a lot happens between DNA and proteins. A key part of that process is translation—when ribosomes read RNA messages and build proteins. But what if there are hidden messages in those RNA sequences that we’re missing?

A new study led by researchers at the University of Otago introduces an innovative computational tool called RIBOSS that helps researchers uncover these hidden messages—specifically, tiny stretches of RNA known as noncanonical open reading frames (ORFs) that may also be translated into proteins. These small ORFs often go unnoticed, especially in regions of RNA that aren’t currently marked as coding.

To understand which parts of RNA are actually being translated, scientists use a method called ribosome profiling—a technique that shows where ribosomes are sitting on RNA strands, which indicates active translation. Recent improvements in this technique allow researchers to see ribosome footprints with greater precision, even in plants and bacteria. However, one big challenge remains: many genomes still lack complete maps of where transcription starts and ends, which makes it hard to spot new ORFs in so-called “untranslated” regions.

That’s where RIBOSS comes in. It’s a flexible set of Python modules designed to find and evaluate these hidden ORFs across a range of species—including humans, Arabidopsis thaliana (a model plant), and Salmonella enterica (a bacterium). RIBOSS combines both short-read and long-read sequencing data, providing a more complete view of the RNA landscape.

Ribosome profiling analysis workflow using RIBOSS

RIBOSS core functions integrate existing bioinformatics tools to provide interactive and modular functionality. The analysis starts with mapping long and short RNA reads using minimap2 and bowtie2, respectively. RIBOSS transcriptome_assembly function performs reference-guided assembly using StringTie, accepting either long-read alignments or combined long- and short-read alignments. Operon and ORF prediction: ① RIBOSS operon_finder predicts operons (prokaryotes) and ORF_finder predicts ORFs (eukaryotes) from the three forward frames of assembled transcripts. Read mapping and transcript quantification: ② RIBOSS align_reads maps ribosome footprints and RNA-seq data to transcript isoforms using STAR, ③ while quantify_transcripts estimates isoform abundance using Salmon, and riboprofiler assigns footprints by reading frame using Ribomap. RIBOSS core analytical modules compare the translation of newly predicted ORFs with nearby annotated ORFs, enabling the identification of novel translated regions. RIBOSS also identifies blastp hits and identical protein groups for novel ORFs. Output files include the assembled transcriptome, publication-ready plots, and annotation tracks of significantly translated ORFs and P-site adjusted ribosome profile (viewable in common genome browsers, e.g. the UCSC Genome Browser, IGV, or Artemis).

This makes RIBOSS the first pipeline of its kind to integrate multiple sequencing approaches for identifying novel translation events. The tool is especially valuable for studying organisms with poorly annotated genomes and opens new doors in understanding how genes are expressed beyond the traditional annotations.

By discovering these overlooked regions of translation, RIBOSS could lead to new insights in genetics, disease research, and even crop improvement.

Availability – RIBOSS is freely available at https://github.com/lcscs12345/riboss.