Ribosome profiling (also known as Ribo-seq) is a powerful technique used to study protein synthesis by analyzing the positions of ribosomes on messenger RNAs (mRNAs) at a genome-wide scale. It involves treating cells with nucleases to digest unprotected RNA, leaving behind only the fragments protected by ribosomes—called ribosome footprints. These fragments are then sequenced and mapped back to the genome, providing a snapshot of which mRNAs are being actively translated and at what efficiency. Ribosome profiling offers high-resolution insights into translational regulation, ribosome density, and can even identify novel translation events like upstream open reading frames (uORFs) or non-canonical start sites.

To investigate the role of the P-site loop in Rpl10, yeast cultures expressing FLAG-tagged Rpl10 mutant ribosomes with the loop deleted were grown under inducing conditions, harvested, and lysed. Ribosome-protected fragments were generated through RNase I digestion and isolated either by FLAG-based immunoprecipitation (IP) or directly from whole-cell lysates (Total). The resulting RNA fragments were size-selected and used to construct ribosome profiling libraries, which were subsequently sequenced using Illumina pipeline (Figure 3).

We next processed the sequencing FASTQ files using RiboFlow, a streamlined pipeline that generates ribo files from raw reads. RiboFlow performs adapter trimming, filters out non-coding RNAs (e.g., rRNA), aligns reads to the transcriptome, and retains high-quality alignments. It also includes an optional PCR deduplication step and compiles results from multiple experiments into a single ribo file.

nextflow RiboFlow.groovy -params-file project.yaml -profile docker_local

For downstream analysis, we used the R package RiboR to interact with the ribo files. RiboR allowed us to efficiently import data into the R environment and generate common visualizations. We used it to extract metagene profiles around start and stop codons, quantify read counts across transcript regions (5′ UTR, CDS, 3′ UTR), and analyze ribosome occupancy across a range of RPF lengths. Additional data such as transcript abundance, nucleotide-resolution occupancy, and associated metadata, were also retrieved using RiboR functions. We identified stall sites by locating amino acid positions with the highest ribosome occupancy and determined the corresponding amino acids at and adjacent to these positions. We then analyzed amino acid enrichment at stall sites in both IP and total ribosome samples. Additionally, we analyzed differential translation by Rpl10 mutant ribosomes using the DESeq2 pipeline on data derived from ribo files.