Quantification of translation uncovers the functions of the alternative transcriptome

Abstract

Deep sequencing methods have matured to comprehensively detect the full set of transcribed loci, but there is a gap to determine the function of the resulting highly complex transcriptomes. At the center of the gene expression cascade, translation is fundamental in defining the fate of much of the transcribed genome. We have developed a new approach (SaTAnn, Splice-aware Translatome Annotation) to annotate and quantify translation at the single open reading frame (ORF) level, that uses information from ribosome profiling to determine the translational state of each isoform in a comprehensive annotation.For most genes, one ORF represents the dominant translation product, but our approach also detects translation from ORFs belonging to multiple transcripts per gene, including targets of RNA surveillance mechanisms such as nonsense-mediated decay. Diversity in the translation output across human cell lines reveals the extent of gene-specific differences in protein production, which are supported by steady-state protein abundance estimates. Computational analysis of Ribo-seq data with SaTAnn (available at https://github.com/lcalviell/SaTAnn) provides a window into the functions of the heterogeneous transcriptome..

Integrated proteogenomic deep sequencing and analytics accurately identify non-canonical peptides in tumor immunopeptidomes

“…Reads with up to 20 multi-mapping positions were included, with multi-mapping reads beings separately treated in subsequent periodicity analysis. The RIboseQC pipeline v1.0 89 was used to deduce P-site positions from the Ribo-Seq reads, and the P-site data were then used as input into the ORFquant pipeline v0.9 90 in combination with custom R scripts 86 for ORF calling. The ORFquant pipeline searches for the periodic ribosomal footprint pattern characteristic of translated ORFs using a supplied database of transcripts, yielding a set of ORFs corresponding to known coding regions, as well as ORFs originating from UTRs, non-coding RNAs, intron retentions, and read-through events.…”

RNA Dysregulation: An Expanding Source of Cancer Immunotherapy Targets

“…Ribo-seq has been widely used to profile ribosome association with different RNA species and to quantify mRNA translational efficiency, providing insights into the role of translational control in regulating protein expression [64,65]. Ribo-seq is an especially powerful tool for identifying ORFs, as Ribo-seq signals within translated ORFs display unique characteristics [66][67][68]. Therefore, Ribo-seq can greatly constrain the search space and reduce the false positive rate during the in silico search for candidate TAs.…”

Truncated RNA-binding protein production by DUX4-induced systemic inhibition of nonsense-mediated RNA decay

“…To ask if and when aberrant RNAs are translated, we used ORFquant (Calviello, Hirsekorn, & Ohler, 2020), a new pipeline that identifies isoform-specific translation events from Ribo-seq data. We then used DEXSeq (Anders, Reyes, & Huber, 2012) to conduct exon-level differential analysis on the set of ORFquant-derived open reading frames, using Ribo-seq data.…”

“…Ribo-seq was performed as described previously (Calviello et al, 2020) using six 70% confluent 10 cm dishes of MB135-iDUX4 cells per condition. Briefly, cells were washed with ice-cold phosphate-buffered saline (PBS) supplemented with 100 μg/mL cycloheximide (Sigma-Aldrich), flash frozen on liquid nitrogen, and lysed in Lysis Buffer (PBS containing 1% (v/v) Triton X-100 and 25 U/mL TurboDNase (Ambion)).…”

“…Only reads mapping uniquely to coding sequence regions were used. In addition, ORFquant (Calviello et al, 2020) was used to derive de-novo isoform-specific translation events, by pooling the Ribo-seQC output from all Ribo-seq samples, using uniquely mapping reads. DEXSeq (Anders et al, 2012) was used to perform differential exon usage along the DUX4 time course data, using Ribo-seq counts on exonic bins and junctions belonging to different ORFquant-derived translated regions.…”