Lindgreen, S, Umu, SU, Lai, AS, Eldai, H, Liu, W, McGimpsey, S, Wheeler, NE, Biggs, PJ, Thomson, NR, Barquist, L, Poole, AM and Gardner, PP. 2014. Robust Identification of Noncoding RNA from Transcriptomes Requires Phylogenetically-Informed Sampling. [Online]. Figshare. Available from: https://doi.org/10.1371/journal.pcbi.1003907
Lindgreen, S, Umu, SU, Lai, AS, Eldai, H, Liu, W, McGimpsey, S, Wheeler, NE, Biggs, PJ, Thomson, NR, Barquist, L, Poole, AM and Gardner, PP. Robust Identification of Noncoding RNA from Transcriptomes Requires Phylogenetically-Informed Sampling. [Internet] LSHTM Data Compass. Figshare; 2014. Available from: https://doi.org/10.1371/journal.pcbi.1003907
Lindgreen, S, Umu, SU, Lai, AS, Eldai, H, Liu, W, McGimpsey, S, Wheeler, NE, Biggs, PJ, Thomson, NR, Barquist, L, Poole, AM and Gardner, PP (2014). Robust Identification of Noncoding RNA from Transcriptomes Requires Phylogenetically-Informed Sampling. [Data Collection]. Figshare. https://doi.org/10.1371/journal.pcbi.1003907
Description
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.
Data capture method | Experiment: Laboratory |
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Date (Published in a 3rd party system) | 2014 |
Language(s) of written materials | English |
Data Creators | Lindgreen, S, Umu, SU, Lai, AS, Eldai, H, Liu, W, McGimpsey, S, Wheeler, NE, Biggs, PJ, Thomson, NR, Barquist, L, Poole, AM and Gardner, PP |
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LSHTM Faculty/Department | Faculty of Infectious and Tropical Diseases > Dept of Pathogen Molecular Biology |
Participating Institutions | London School of Hygiene & Tropical Medicine, London, United Kingdom |
Depositor | Gareth Knight |
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Date Deposited | 04 Mar 2019 11:45 |
Last Modified | 04 Mar 2019 11:45 |
Publisher | Figshare |