Although the genuine domain content of a real microbial metatranscriptome is unknown, the results obtained from the Gilbert and Stewart metatranscriptomes were, in terms of data reduction and annotation rates, consistent with the medium and high diversity 454 simulations (see Additional file 1: Figure S4).
Gifford, S. M., Sharma, S., Rinta-Kanto, J. M. & Moran, M. A. Quantitative analysis of a deeply sequenced marine microbial metatranscriptome.
The products were then used for next-generation Roche GS FLX Titanium sequencing to obtain metatranscriptome data.
Here we present a study of a complex marine microbial metatranscriptome enriched to 99.92% mRNA [10].
Abundant transcripts identified in this study likely contribute to a core microbial metatranscriptome in the distal intestine.
A subset of specific transcripts was relatively abundant in all samples studied, indicating that we have begun to define a core neonatal gut microbial metatranscriptome.
Smith-Waterman analysis of the 142 sequences used the high-performance Sencel Paralign application (http://www.sencel.com/) for local sequence alignments applied a calculated e-value of ≤10−2, equivalent to the value used successfully for the annotation of a complex marine microbial metatranscriptome [30].
We applied the data reduction methods previously employed on simulated data to two real 454 metatranscriptome data sets: the mid-bloom, marine metatranscriptome from [ 4]; and the 110 m marine metatranscriptome from an oxygen minimum zone [ 14].
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.
As more microbial transcriptome data become available (e.g. through sequencing efforts such as the MMETSP (http://marinemicroeukaryotes.org/)), it should be possible to refine these models of transcript abundance to reflect increased levels of transcripts involved in core processes and thereby produce more realistic simulations of metatranscriptome data.
