uORF-Tools—Workflow for the determination of translation-regulatory upstream open reading frames


	Implementation and workflow



	Implementation

uORF-Tools is provided as a free and open workflow and can be downloaded from https://github.com/Biochemistry1-FFM/uORF-Tools. It is based on Snakemake [22] and automatically installs all tool dependencies in a version-controlled manner via bioconda [23]. The workflow can be run locally or in a cluster environment.


	Workflow

uORF-Tools is designed to receive bam files of ribosome profiling data sets as input (Fig 1). In addition, the workflow requires a genome fasta file and an annotation gtf file.
Initially, uORF-Tools generates a new genome annotation file, which is used in the subsequent steps of the workflow. For practical reasons, this annotation file contains only the validated or manually annotated (confidence levels 1 and 2 in Gencode) ( www.gencodegenes.org) longest protein coding transcript variants. Based on the provided input bam files and the generated genome annotation file, an experiment-specific uORF annotation file is then generated using Ribo-TISH [21]. Specifically, Ribo-TISH identifies translation initiation sites within ribo-seq data and uses this information to determine ORFs, i.e. regular ORFs as well as uORFs. Default settings in uORF-Tools use the canonical start codon ATG only, yet users can allow for the use of alternative start codons as well. Furthermore, as uORFs are generally considered to be short, peptide-coding ORFs, a maximal length of 400 nt was set as upper size limit within the uORF-Tools pipeline for the identification of uORFs. The minimal size limit was set to 9 nt to ensure that the potential uORFs contain at least one codon on top of the required start and stop codons [24]. To allow for an even broader characterization of potentially active uORFs, a comprehensive human uORF annotation file (based on hg38), based on 35 ribo-seq data sets, is provided with the package (for details seeS1 Table). Among other information, this file contains the exact coordinates of all uORFs (designated as ORFs in the annotation file), as well as their lengths. To use this comprehensive instead of the experiment-specific annotation file, the former needs to be selected by including its file path (uORF-Tools/comprehensive_annotation/uORF_annotation_hg38.csv) in the config.yaml file before starting the uORF-Tools workflow. Using uORF and genome annotation files, uORF-Tools creates one count file containing all reads that correspond to coding sequences (CDS) of the longest protein coding transcripts, i.e. main ORFs, and another count file which contains only reads that correspond to uORFs. To control for differences in library sizes, the count data are subsequently normalized using size factors calculated for all input libraries with DESeq2 [25]. To determine the relative translation of a main ORF, counts of the main ORF are normalized to the corresponding uORF counts. In order to assess if the main ORF-to-uORF ratios are altered in response to a stimulus, the impact of uORFs on downstream translation is determined by comparing the main ORF-to-uORF ratios between different conditions. A stimulus-dependent increase in the ratios indicates enhanced translation of the main ORF, i.e. reduced repression by the respective uORF, conversely a decrease in the ratios indicates that an inhibitory uORF becomes more active. Of note, no translational efficiencies are determined and needed in the uORF-Tools pipeline, since both main ORF and uORF ribo-seq reads would be normalized to the same transcript abundance, which would be eliminated during the calculation of the main ORF-to-uORF ratios. We therefore decided to compare ribosome profiling reads only to minimize computing requirements. Along the same lines, uORF-Tools is designed to take bam files, i.e. processed ribosome profiling data. Nevertheless, we also provide a pre-processing pipeline (S1 File) to allow for the use of yet unprocessed fastq files.
