Key differences among open and closed high-throughput platforms for microbial community analysisa

Step or parameterCharacteristic or considerationDescription of characteristic or consideration in indicated type of analysisbComments
Open formatClosed format
Sample preparation and analysisSample/target preparationComplicatedSimpleVery complicatedSimpleSimpleDNA/RNA quality is important for all approaches
Analysis of multiplex samples per assayLarge potentialMedium potentialMedium potentialLow (only one or two)Low (only one or two)FGAs and PGAs use 1 or 2 dyes for labeling, and it is difficult to multiplex samples in a single assay
PCR amplification or whole-genome analysisYesNoNoNo/yesYes/noAmplification introduces major problems for quantification
Potential uneven hybridizationNANANAYesYesSignal normalization is needed within and between arrays to correct signal differences due to systematic errors
Data processing and analysisRaw data processingRelatively easyDifficultDifficultEasyEasyA major challenge for SMS and MTS with large raw datasets
PhylogenyYesSomeSomeNo/yesYesGeoChip uses gyrB for phylogeny
Taxonomic resolutionStrain, species, genusStrain, speciesStrain, speciesStrain, speciesGenus, familyIt depends on molecular markers with high resolution for functional genes
Functional featuresNo/yesYesYesYesNoTGS can analyze DNA and RNA for functional genes
Signal thresholdYesNANAYesYesBoth PGAs and FGAs require a threshold to call positive signals, which is more or less arbitrary. Thus, some ambiguity exists for positive or negative spots.
Requires a priori knowledgeNo/yesNoNoYesYesClosed-format technologies are designed based on known sequences
Analysis of α diversityVery goodGoodVery poorFairFairHere, α diversity estimation is based on a single gene
Data comparison across samplesModerateDifficultDifficultEasyEasyRandom or undersampling is a major issue for open-format approaches
PerformanceCoverage/breadth (no. of different genes detected)Very lowHighHighHighVery lowTGS can analyze phylogenetic or functional genes
Sampling depth (no. of sequences or OTUs per gene)Very highLow/mediumLow/mediumMediumHighThe sampling depth for closed-format approaches depends on the number of probes used
Detection of rare species/genesMediumDifficultDifficultEasyEasyEasy for closed format as long as the appropriate probes are present
QuantificationLowNot knownNot knownHighLow/mediumNot rigorously tested for SMS and MTS; for PhyloChip, if RNA is used instead of DNA (no PCR step), quantification is high
Susceptibility to the artifacts associated with random sampling processMediumHighHighLowMedium/lowA major problem for sequencing approaches; PCR amplification may be involved in PhyloChip
Potential discovery of novel genes/speciesYesYesYesNoNo
Results skewed by dominant populationsYesYesYesNoNo
Sensitivity to (host) DNA/RNA contaminationNo/yesYesYesNoNoDifficult to remove host DNA/RNA contamination
Applicability and costMost promising applicationsIn-depth studies of microbial diversity or specific functional groups and discovery of novel genesSurveys of microbial genetic diversity of unknown communities and discovery of novel genesSurveys of functional activity of unknown microbial communities and discovery of novel genesComparisons of functional diversity and structure of microbial communities across many samplesComparisons of taxonomic or phylogenetic diversity and structure of microbial communities across many samplesThe choice of technology mainly depends on the biological questions and hypotheses to be addressed
Relative cost per assayMediumHighHighLowLowIt is challenging to make general statements of cost because they depend on technology platforms, depth of analysis, and approaches used for processing and analyzing data
Cost per sample ($)30–1501200–40001500–4500150–800150–1000This is only based on the cost of materials for target gene amplicon preparations and sequencing.
Cost for bioinformatic analysisMediumHighHighLowLow
  • a Since various technologies have different features, it is difficult to make straightforward, point-by-point direct comparison. Thus, our attempt is to highlight the major differences of various technologies in a general sense. We attempt to focus on the issues important to microbial ecology within the context of environmental applications and complex microbial communities like those in soil rather than list the differences of various technologies in a comprehensive manner.

  • b TGS, target gene (e.g., 16S rRNA, amoA, nifH) sequencing; SMS, shotgun metagenome sequencing; MTS, metatranscriptome sequencing; FGAs, functional gene arrays: the listed analysis is mostly based on GeoChip; PGAs, phylogenetic gene arrays: the listed analysis is mostly based on PhyloChip; NA, not applicable.