Preprocessing introduction

Ribosome profiling (Ribo-seq) data are typically generated through a multi-step experimental pipeline, starting from library preparation to sequencing on high-throughput platforms. The raw data output from sequencing is usually in FASTQ format and requires several preprocessing steps before downstream analysis.

First, quality control should be performed on the raw FASTQ files using tools such as FastQC. To remove adapter contamination introduced during library preparation, trimming tools like Cutadapt or Trim Galore are commonly used. During size selection in the library construction process (e.g., gel purification), fragments within a specific length range are enriched. However, these can still include contaminating small RNAs such as rRNAs and tRNAs.

To eliminate these unwanted reads, it is essential to align sequences to a custom database of rRNA, tRNA, and other non-coding RNAs (downloadable from resources like NCBI and Ensembl) and remove matching reads. This filtering helps retain only high-confidence ribosome-protected footprints (RPFs), representing genuine translation events.

The cleaned reads can then be aligned to the reference genome or transcriptome using alignment tools such as STAR or HISAT2. Quantification of read counts across gene features can be performed using tools like featureCounts or HTSeq. After quantification, differential translation efficiency analysis between experimental conditions, enriched pathway analysis, and other integrative analyses can be carried out to interpret the biological significance of the translation landscape.