In our study, we faced the computational challenge of detecting a rare microbial community in a dominant pool of host genetic material. We utilized transcript-based libraries because there is an amplification of protein-coding sequences during transcription, which increased our detection ability and provided support that the identified microorganisms were viable. Further, the database for protein-coding regions is more extensive than that for noncoding regions for non-reference organisms. Thus, focusing on transcripts should facilitate classification of novel organisms and those without complete genome coverage. Indeed, our study demonstrates that at the moderate sequencing depth employed, there were more assignable sequencing tags to protein-coding regions utilizing cDNA compared to genomic DNA, which consequently increased our ability to detect microbial taxa. In addition, transcriptome sequencing yields bacterial ribosomal RNA, which is highly expressed in metabolically active microorganisms and is well documented as a taxonomic tool for bacteria. Because single protein-coding or rRNA transcript-tags from a putative microorganism were frequently encountered, our confidence in taxonomic assignment increased by employing bioinformatics methods to classify organisms based on both types of transcripts. Amplicon 16S rRNA sequencing increased the sensitivity to detect members of some bacterial classes. However, primer specific methods do not provide as comprehensive a perspective on the microbiota due to a possible amplification bias towards more abundant taxa or those exhibiting higher primer specificity. Therefore, neither our metatranscriptomic nor amplicon sequencing approaches should be considered quantitative. We note that in samples that are highly enriched for actively replicating microbial organisms, such as environmental samples or gastrointestinal tract specimens, cDNA-based approaches can yield an abundance of small RNA produced by complex microbial communities, which can facilitate studies on microbial ecology but be less useful for identification of individual microbes [49] . In addition established metagenomics or metatranscriptomic [11, 50, 51] approaches that utilize sample fractionation methods for microbial enrichment will likely provide a more comprehensive profile of the community structure. These methods were not applicable to our samples, which included phagocytized microorganisms and viable microbes that were not robustly proliferating. Nevertheless, as deeper sequencing of cDNA libraries using newer high-throughput sequencing methods becomes more accessible, it could complement the Roche-454 pyrosequencing data, potentially covering the entire viable microbial community.