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The rocks and shallows of deep RNA sequencing: Examples in the Vibrio cholerae RNome.
RNA. 2011 Jul;17(7):1357-66
Authors: Raabe CA, Hoe CH, Randau G, Brosius J, Tang TH, Rozhdestvensky TS
Abstract
New deep RNA sequencing methodologies in transcriptome analyses identified a wealth of novel nonprotein-coding RNAs (npcRNAs). Recently, deep sequencing was used to delineate the small npcRNA transcriptome of the human pathogen Vibrio cholerae and 627 novel npcRNA candidates were identified. Here, we report the detection of 223 npcRNA candidates in V. cholerae by different cDNA library construction and conventional sequencing methods. Remarkably, only 39 of the candidates were common to both surveys. We therefore examined possible biasing influences in the transcriptome analyses. Key steps, including tailing and adapter ligations for generating cDNA, contribute qualitatively and quantitatively to the discrepancies between data sets. In addition, the state of 5'-end phosphorylation influences the efficiency of adapter ligation and C-tailing at the 3'-end of the RNA. Finally, our data indicate that the inclusion of sample-specific molecular identifier sequences during ligation steps also leads to biases in cDNA representation. In summary, even deep sequencing is unlikely to identify all RNA species, and caution should be used for meta-analyses among alternatively generated data sets.
PMID: 21610211 [PubMed - indexed for MEDLINE]
More...
The rocks and shallows of deep RNA sequencing: Examples in the Vibrio cholerae RNome.
RNA. 2011 Jul;17(7):1357-66
Authors: Raabe CA, Hoe CH, Randau G, Brosius J, Tang TH, Rozhdestvensky TS
Abstract
New deep RNA sequencing methodologies in transcriptome analyses identified a wealth of novel nonprotein-coding RNAs (npcRNAs). Recently, deep sequencing was used to delineate the small npcRNA transcriptome of the human pathogen Vibrio cholerae and 627 novel npcRNA candidates were identified. Here, we report the detection of 223 npcRNA candidates in V. cholerae by different cDNA library construction and conventional sequencing methods. Remarkably, only 39 of the candidates were common to both surveys. We therefore examined possible biasing influences in the transcriptome analyses. Key steps, including tailing and adapter ligations for generating cDNA, contribute qualitatively and quantitatively to the discrepancies between data sets. In addition, the state of 5'-end phosphorylation influences the efficiency of adapter ligation and C-tailing at the 3'-end of the RNA. Finally, our data indicate that the inclusion of sample-specific molecular identifier sequences during ligation steps also leads to biases in cDNA representation. In summary, even deep sequencing is unlikely to identify all RNA species, and caution should be used for meta-analyses among alternatively generated data sets.
PMID: 21610211 [PubMed - indexed for MEDLINE]
More...