Table 3 Comparison of available technologies for identification of microbial sequences in human tissues

RNA-based

Extraction of sequences from expression library data sets

Detection of actively expressed microbial genes regardless of DNA copy number

No detection of non-transcribed microbial genes

Computational Subtraction [14, 16]

Tags can be generated as long as genes are transcripted

Can miss detection if low microbial load

DTS [17, 21]

Tag numbers are not limited by the genome size

Subject to sequencing errors or tag site polymorphisms matching microbial sequences

Viral Detection DNA Microarray [8]

RDA [13]

Genomic DNA-based

Extraction of sequences from genomic library data sets

Detection of microbial DNA regardless of expression status of the genes

No detection of non-reverse transcribed RNA

DK-MICROBE

Potential to detect latent microbial genomic fragments which have been integrated into human genome

Can miss detection if low microbial load

Computational Subtraction [15]

Utilization of clinical samples not suitable for RNA extraction including paraffin-embedded fixed tissue

Subject to sequencing errors or tag site polymorphisms matching microbial sequences

(B) Tag-based vs. array hybridization

Array hybridization

Cross hybridization to homologous sequences of microorganisms on microarray

Currently lower cost

Array designed to target limited number of candidate microbes

Viral Detection DNA Microarray [8–11]

Method is highly versatile and generally high throughput

Risk of unspecific binding

Tag-based

Fractional representations through extraction of sequences from specific locations in microbial genomes

Sequence based non biased result

Limited by sequencing costs

DK-MICROBE

Greater chance to identify rare microbial associations

Subject to sequencing errors, tag site polymorphisms

Computational Subtraction [14–16]

No physical generation of organism-specific array

DTS [17, 21]

Results can be utilized and referenced for additional human genome analyses