Metatranscriptomes from diverse microbial communities: assessment of data reduction techniques for rigorous annotation

Toseland, Andrew, Moxon, Simon ORCID:, Mock, Thomas ORCID: and Moulton, Vincent ORCID: (2014) Metatranscriptomes from diverse microbial communities: assessment of data reduction techniques for rigorous annotation. BMC Genomics, 15. ISSN 1471-2164

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Background Metatranscriptome sequence data can contain highly redundant sequences from diverse populations of microbes and so data reduction techniques are often applied before taxonomic and functional annotation. For metagenomic data, it has been observed that the variable coverage and presence of closely related organisms can lead to fragmented assemblies containing chimeric contigs that may reduce the accuracy of downstream analyses and some advocate the use of alternate data reduction techniques. However, it is unclear how such data reduction techniques impact the annotation of metatranscriptome data and thus affect the interpretation of the results. Results To investigate the effect of such techniques on the annotation of metatranscriptome data we assess two commonly employed methods: clustering and de-novo assembly. To do this, we also developed an approach to simulate 454 and Illumina metatranscriptome data sets with varying degrees of taxonomic diversity. For the Illumina simulations, we found that a two-step approach of assembly followed by clustering of contigs and unassembled sequences produced the most accurate reflection of the real protein domain content of the sample. For the 454 simulations, the combined annotation of contigs and unassembled reads produced the most accurate protein domain annotations. Conclusions Based on these data we recommend that assembly be attempted, and that unassembled reads be included in the final annotation for metatranscriptome data, even from highly diverse environments as the resulting annotations should lead to a more accurate reflection of the transcriptional behaviour of the microbial population under investigation.

Item Type: Article
Additional Information: © 2014 Toseland et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.
Uncontrolled Keywords: metratranscriptomics,sequence processing,data reduction,clustering,assembly
Faculty \ School: Faculty of Science > School of Computing Sciences
Faculty of Science > School of Environmental Sciences
Faculty of Science > School of Biological Sciences
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Depositing User: Pure Connector
Date Deposited: 04 Nov 2014 12:20
Last Modified: 18 Apr 2023 21:30
DOI: 10.1186/1471-2164-15-901

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