Simultaneous sequencing of coding and noncoding RNA reveals a human transcriptome dominated by a small number of highly expressed noncoding genes

Abstract

Comparing the abundance of one RNA molecule to another is crucial for understanding cellular functions but most sequencing techniques can target only specific subsets of RNA. In this study, we used a new fragmented ribodepleted TGIRT sequencing method that uses a thermostable group II intron reverse transcriptase (TGIRT) to generate a portrait of the human transcriptome depicting the quantitative relationship of all classes of nonribosomal RNA longer than 60 nt. Comparison between different sequencing methods indicated that FRT is more accurate in ranking both mRNA and noncoding RNA than viral reverse transcriptase-based sequencing methods, even those that specifically target these species. Measurements of RNA abundance in different cell lines using this method correlate with biochemical estimates, confirming tRNA as the most abundant nonribosomal RNA biotype. However, the single most abundant transcript is 7SL RNA, a component of the signal recognition particle. tructuredonoding (sncRNAs) associated with the same biological process are expressed at similar levels, with the exception of RNAs with multiple functions like U1 snRNA. In general, sncRNAs forming RNPs are hundreds to thousands of times more abundant than their mRNA counterparts. Surprisingly, only 50 sncRNA genes produce half of the non-rRNA transcripts detected in two different cell lines. Together the results indicate that the human transcriptome is dominated by a small number of highly expressed sncRNAs specializing in functions related to translation and splicing.

The cellular landscape of mid‐size noncoding RNA

“…mncRNA copy number ranges from a handful of copies, for some snoRNAs, to more than a hundred copies for certain tRNAs, and for 5S rRNAs, in various organisms including cows, rats and plants (Bermudez-Santana et al, 2010;Haeusler & Engelke, 2006;Michaud, Cognat, Duchene, & Marechal-Drouard, 2011;Wong, Abrahamson, & Nazar, 1984). Although many of the mncRNA copies might not be functional, a large number of these copies generate detectable RNA and in some cases, they are strongly expressed (Boivin, Deschamps-Francoeur, Couture, et al, 2018;Hoeppner, Denisenko, Gardner, Schmeier, & Poole, 2018).…”

“…In contrast, the 50–200 nucleotides size range includes several highly structured and highly expressed RNA families including transfer RNAs (tRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs; Boivin, Deschamps‐Francoeur, & Scott, ; Brosnan & Voinnet, ; Haeusler & Engelke, ; Iben & Maraia, ). These abundant highly structured families of “small” noncoding RNA share fundamental characteristics in terms of structure, maturation, and function that hinder their detection by RNA‐sequencing (Boivin et al, ). Some members of these small noncoding RNA families and other additional RNA groups (including some snoRNAs and snRNAs, as well as the 7SL, 7SK, and RNaseP RNAs) are longer than 200 nucleotides and are often categorized as long noncoding RNAs.…”

Improved TGIRT-seq methods for comprehensive transcriptome profiling with decreased adapter dimer formation and bias correction

“…GsI-IIC RT (TGIRT-III) has been used for a variety of applications, including comprehensive profiling of whole-cell, exosomal and plasma RNAs Qin et al 2016;Shurtleff et al 2017;Boivin et al 2018); quantitative tRNA-seq based on the ability of the TGIRT enzyme to obtain full-length end-to-end reads of tRNAs with or without demethylase treatment (Shen et al 2015;Zheng et al 2015;Qin et al 2016); determination of tRNA aminoacylation levels (Evans et al 2017); high-throughput mapping of posttranscriptional modifications by distinctive patterns of misincorporation (Katibah et al 2014;Zheng et al 2015;Shen et al 2015;Shurtleff et al 2017;Li et al 2017;Safra et al 2017); identification of protein-bound RNAs by RIP-Seq or CLIP (Katibah et al 2014;Zarnegar et al 2016); and RNA-structure mapping by DMS-MaPseq (Zubradt et al 2017;Wang et al 2018) or SHAPE (Mohr et al 2018). A study comparing TGIRT-seq to benchmark TruSeq v3 datasets of rRNA depleted (ribodepleted) fragmented Universal Human Reference (UHR) RNA with External RNA Control Consortium (ERCC) spike-ins showed that TGIRT-seq: (i) better recapitulates the relative abundance of mRNAs and ERCC spike-ins; (ii) is more strand-specific;…”

CoCo: RNA-seq Read Assignment Correction for Nested Genes and Multimapped Reads

“…However, in this study, we show that the precision, quality and depth of transcriptome analysis can be greatly enhanced by carefully choosing and tuning read assignment tools using existing sequencing data. Modification of the read assignment pipeline for the analysis of a TGIRT-seq fragmented dataset, which provides an accurate view of the whole transcriptome (Boivin, et al, 2018) increased the proportion of assigned reads by 15% and modified the number of assigned reads for 50% of all expressed genes (Fig. 4, Supplementary Fig.12).…”