Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling

TL;DR

This study demonstrates that identifying noncoding RNAs from transcriptome data critically depends on phylogenetically-informed sampling, revealing limitations of current methods and emphasizing the need for phylogeny-aware experimental design.

Contribution

It shows that phylogeny-aware sampling is essential for robust noncoding RNA identification from transcriptomes, highlighting the narrow phylogenetic window for comparative methods.

Findings

Nearly a thousand candidate noncoding RNAs identified

Phylogenetic sampling strongly influences detection success

Only one phylogenetic cluster allowed effective separation of noncoding RNAs from noise

Abstract

Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.