Abstract
Detection of translation in so-called non-coding RNA provides an opportunity for identification of novel bioactive peptides and microproteins. The main methods used for these purposes are ribosome profiling and mass spectrometry. A number of publicly available datasets already exist for a substantial number of different cell types grown under various conditions, and public data mining is an attractive strategy for identification of translation in non-coding RNAs. Since the analysis of publicly available data requires intensive data processing, several data resources have been created recently for exploring processed publicly available data, such as OpenProt, GWIPS-viz, and Trips-Viz. In this work we provide a detailed demonstration of how to use the latter two tools for exploring experimental evidence for translation of RNAs hitherto classified as non-coding. For this purpose, we use a set of transcripts with substantially different patterns of ribosome footprint distributions. We discuss how certain features of these patterns can be used as evidence for or against genuine translation. During our analysis we concluded that the MTLN mRNA, previously misannotated as lncRNA LINC00116, likely encodes only a short proteoform expressed from shorter RNA transcript variants.
Highlights
In the section “Data exploration in the context of individual RNA sequences,” we examine a selection of transcripts that illustrate different patterns of ribosomal footprints aligned to them and evaluate these patterns for genuine translation, see Table 1
We further explored ribosome footprints aligned to small nucleolar RNA host gene 8 (SNHG8), zinc finger antisense 1 transcript (ZFAS1), and X Inactive Specific Transcript (XIST)
It can be seen that in addition to the long isoform, there are two additional short isoforms (ENST00000426713 and ENST00000611969), with annotated coding sequence (CDS) starts from the same start codon that we proposed on the analysis of data in Trips-Viz
Summary
In the section “Setup and Configurations,” we describe in detail how to use the relevant functionalities of Trips-Viz. In the section “Data exploration in the context of individual RNA sequences,” we examine a selection of transcripts that illustrate different patterns of ribosomal footprints aligned to them and evaluate these patterns for genuine translation, see Table 1. GWIPS-viz can be used to further explore whether the annotated transcript is supported by available RNA-seq and Ribo-seq data.
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