title: Metatranscriptomes from diverse microbial communities: assessment of data reduction techniques for rigorous annotation.
abstract: 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.
authors.affiliation: School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, Norfolk NR4 7TJ, UK. a.toseland@uea.ac.uk.
authors.forename: Andrew
authors.initials: A
authors.lastname: Toseland
authors.forename: Simon
authors.initials: S
authors.lastname: Moxon
authors.forename: Thomas
authors.initials: T
authors.lastname: Mock
authors.forename: Vincent
authors.initials: V
authors.lastname: Moulton
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D016000:Cluster Analysis; D020451:Contig Mapping; D030541:Databases, Genetic; D019277:Entropy; D016678:Genome; D056186:Metagenomics; D059467:Transcriptome