Robust Identification of Noncoding RNA from Transcriptomes Requires Phylogenetically-Informed Sampling

Please always quote using this URN: urn:nbn:de:bvb:20-opus-115259
  • 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 robustlyNoncoding 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.show moreshow less

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Metadaten
Author: Stinus Lindgreen, Sinan Uğur Umu, Alicia Sook-Wei Lai, Hisham Eldai, Wenting Liu, Stephanie McGimpsey, Nicole E. Wheeler, Patrick J. Biggs, Nick R. Thomson, Lars Barquist, Anthony M. Poole, Paul P. Gardner
URN:urn:nbn:de:bvb:20-opus-115259
Document Type:Journal article
Faculties:Medizinische Fakultät / Institut für Molekulare Infektionsbiologie
Language:English
Parent Title (English):PLOS Computational Biology
Year of Completion:2014
Volume:10
Issue:10
Pagenumber:e1003907
Source:PLoS Computational Biology 10(10): e1003907. doi:10.1371/journal.pcbi.1003907
DOI:https://doi.org/10.1371/journal.pcbi.1003907
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/25357249
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Tag:alignment; archaea; bacterial genomes; comparative genomics; dark-matter; homology search; insights; protein families database; sequence; small nucleolar RNAs
Release Date:2015/07/14
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung