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

Lindgreen S, Umu SU, Lai AS, Eldai H, Liu W, McGimpsey S, Wheeler NE, Biggs PJ, Thomson NR, Barquist L, Poole AM, Gardner PP - PLoS Comput. Biol. (2014)

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, University of Copenhagen, Copenhagen, Denmark; School of Biological Sciences, University of Canterbury, Christchurch, New Zealand.

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 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.

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Related in: MedlinePlus

Public RNA-seq datasets that lie in the Goldilocks Zone.Ten strains with corresponding, publicly available RNA-seq data and phylogenetic distances in the Goldilocks Zone (Figure 3) have been identified. The maximum likelihood tree from a SSU rRNA alignment shows the relationships between these taxa. They fall into three clades, containing members of the families: Enterobacteriaceae and Xanthomonadaceae, and the genus: Pseudomonas. The nodes connecting taxa within the Goldilocks Zone are coloured gold, taxa that are too close are coloured red and those that are too divergent are coloured cyan. Each strain is annotated with gold boxes where there was stranded information, or if the majority of core mRNAs and ncRNAs (see Methods) were expressed (see Table S3 for the raw data).
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pcbi-1003907-g004: Public RNA-seq datasets that lie in the Goldilocks Zone.Ten strains with corresponding, publicly available RNA-seq data and phylogenetic distances in the Goldilocks Zone (Figure 3) have been identified. The maximum likelihood tree from a SSU rRNA alignment shows the relationships between these taxa. They fall into three clades, containing members of the families: Enterobacteriaceae and Xanthomonadaceae, and the genus: Pseudomonas. The nodes connecting taxa within the Goldilocks Zone are coloured gold, taxa that are too close are coloured red and those that are too divergent are coloured cyan. Each strain is annotated with gold boxes where there was stranded information, or if the majority of core mRNAs and ncRNAs (see Methods) were expressed (see Table S3 for the raw data).

Mentions: All of the available full length Bacterial and Archaeal genomes were annotated using Rfam and Pfam models. For each Pfam/Rfam family, RNA-seq species or taxonomic group the “phylogenetic distance” is calculated using the maximum SSU rRNA F84 distance (see Methods for details). A. For the Pfam and the Rfam families we compare the levels of conservation as a function of phylogenetic distance using annotations of 2,562 bacterial genomes. E.g. of RNA families are conserved between species from the same family, whereas of protein families are conserved within the same taxonomic range. B. The barplot shows the distribution of all pairwise distances between the RNA-seq datasets. Eleven pairs (boxed) are in the Goldilocks Zone (See Figure 4 for further analysis). C. The ranges of phylogenetic distances for comparing species from different taxonomic groups.


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

Lindgreen S, Umu SU, Lai AS, Eldai H, Liu W, McGimpsey S, Wheeler NE, Biggs PJ, Thomson NR, Barquist L, Poole AM, Gardner PP - PLoS Comput. Biol. (2014)

Public RNA-seq datasets that lie in the Goldilocks Zone.Ten strains with corresponding, publicly available RNA-seq data and phylogenetic distances in the Goldilocks Zone (Figure 3) have been identified. The maximum likelihood tree from a SSU rRNA alignment shows the relationships between these taxa. They fall into three clades, containing members of the families: Enterobacteriaceae and Xanthomonadaceae, and the genus: Pseudomonas. The nodes connecting taxa within the Goldilocks Zone are coloured gold, taxa that are too close are coloured red and those that are too divergent are coloured cyan. Each strain is annotated with gold boxes where there was stranded information, or if the majority of core mRNAs and ncRNAs (see Methods) were expressed (see Table S3 for the raw data).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4214555&req=5

pcbi-1003907-g004: Public RNA-seq datasets that lie in the Goldilocks Zone.Ten strains with corresponding, publicly available RNA-seq data and phylogenetic distances in the Goldilocks Zone (Figure 3) have been identified. The maximum likelihood tree from a SSU rRNA alignment shows the relationships between these taxa. They fall into three clades, containing members of the families: Enterobacteriaceae and Xanthomonadaceae, and the genus: Pseudomonas. The nodes connecting taxa within the Goldilocks Zone are coloured gold, taxa that are too close are coloured red and those that are too divergent are coloured cyan. Each strain is annotated with gold boxes where there was stranded information, or if the majority of core mRNAs and ncRNAs (see Methods) were expressed (see Table S3 for the raw data).
Mentions: All of the available full length Bacterial and Archaeal genomes were annotated using Rfam and Pfam models. For each Pfam/Rfam family, RNA-seq species or taxonomic group the “phylogenetic distance” is calculated using the maximum SSU rRNA F84 distance (see Methods for details). A. For the Pfam and the Rfam families we compare the levels of conservation as a function of phylogenetic distance using annotations of 2,562 bacterial genomes. E.g. of RNA families are conserved between species from the same family, whereas of protein families are conserved within the same taxonomic range. B. The barplot shows the distribution of all pairwise distances between the RNA-seq datasets. Eleven pairs (boxed) are in the Goldilocks Zone (See Figure 4 for further analysis). C. The ranges of phylogenetic distances for comparing species from different taxonomic groups.

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, University of Copenhagen, Copenhagen, Denmark; School of Biological Sciences, University of Canterbury, Christchurch, New Zealand.

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 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 MeSH
Related in: MedlinePlus