XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data

Abstract

To further validate the design, reliability, and versatility of the XPRESSpipe pipeline, we processed raw TCGA sequence data using XPRESSpipe and compared the output count values to those publicly available through TCGA [1]. Spearman ρ values for the selected samples ranged from 0.979-0.980 when pseudogenes were excluded (Figure 1), indicating XPRESSpipe performs with similar accuracy to the TCGA RNA-Seq processing standards. The differences in reported counts can be accounted for by a couple of key differences. For instance, the XPRESSpipe-processed files are aligned to the Homo sapiens GRChv98 reference transcriptome, while the original count data are aligned to the GRChv79 reference transcriptome. The use of a different transcriptome reference can result in variance in the final quantified data for several genes (Figure 2) as significant advances have been made in our understanding of transcribed regions of the human genome between versions.Another source of dissimilarity in data processing appears to arise if an Ensembl canonical transcripts-only reference is used during quantification. TCGA-processed data used an unmodified transcriptome reference file (all transcripts); therefore, the use of this modified (Ensembl canonical transcripts only) GTF will produce varied quantification for some genes as quantifications are constrained to a single transcript version of a given gene and a read will not be quantified if mapping to an exon not used by the canonical transcript. Even using XPRESSpipe settings closest to the TCGA pipeline and using the same genome and transcriptome version resulted in some variation (Figure 2, plot enclosed in maroon). By performing a more detailed analysis of these differences, it is clear that virtually all genes exhibiting variance between the processing methods are pseudogenes, with the TCGA pipeline accepting and quantifying more pseudogenes at the time of initial analysis of this dataset. This can be indicative of the difficulty surrounding the recognition of these reads as multi-mapping to both the original gene and pseudogene (Figure 3,4,5; interactive plots accompanying Figure 5 can be accessed at [2].

Guidelines for Performing Ribosome Profiling in Plants Including Structural Analysis of rRNA Fragments

“…Indeed, oligo cocktails containing 60 oligos report only a 50 % rRNA reduction (Chotewutmontri et al, 2018) which is less efficient than our 29 oligos. Since 2-5 rRNA fragments can account for more than 90% of a Ribo-seq library (Berg et al, 2020), a minimal cocktail containing only 5-10 oligos may already provide sufficient benefits for most applications. Next, the same procedure was applied to formulate a tobacco-specific rRNA depletion cocktail (Table S2), which was also very effective at reducing rRNA from ∼75% to ∼40 % (Figure 6C).…”

Sustained hyperglycemia specifically targets translation of mRNAs for insulin secretion

“…RPF and RNA libraries were sequenced using Illumina NS500 single-end 75 bp reads. Data analyses employed the XPRESSyourself pipeline (54). Briefly, trimmed reads were aligned to the genome (Ensemble release version 102) with the two-pass option that removes rRNA alignments and PCR duplicates and counts reads that map to the exons or truncated coding sequences of the longest transcripts of protein-coding genes.…”

Metaboverse enables automated discovery and visualization of diverse metabolic regulatory patterns

“…Sequence FASTQ files were processed using XPRESSpipe (version 0.6.0) (ref. 68). Batch and log files are available at ref.…”