ribofootPrinter: A precision python toolbox for analysis of ribosome profiling data

Abstract

Ribosome profiling is a valuable methodology for measuring changes in a cell’s translational program. The approach can report how efficiently mRNA coding sequences are translated and pinpoint positions along mRNAs where ribosomes slow down or arrest. It can also reveal when translation takes place outside coding regions, often with important regulatory consequences. While many useful software tools have emerged to facilitate analysis of these data, packages can become complex and challenging to adapt to specialized needs. In particular, the results of meta analysis of average footprint data around sequence motifs of interest can vary substantially depending on the normalization method that is utilized. We therefore introduce ribofootPrinter, a suite of Python tools designed to offer an accessible and modifiable set of code for analysis of ribosome profiling data. Footprint alignments are made to a simplified transcriptome, keeping the code intuitive, and multiple normalization options help facilitate interpretation of meta analysis, particularly outside coding regions. We believe this tool has promise to carry out sophisticated analysis yet offer simplicity to make it readily understandable and adaptable.