We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the United States Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed, for these and qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

The potential of DNA microarray technology in high-throughput detection of bacteria and quantitative assessment of their community structures is widely acknowledged but has not been fully realised yet. A generally applicable set of techniques, based on readily available technologies and materials, was developed for the design, production and application of diagnostic microbial microarrays. A microarray targeting the particulate methane monooxygenase (pmoA) gene was developed for the detection and quantification of methanotrophs and functionally related bacteria. A microarray consisting of a set of 59 probes that covers the whole known diversity of these bacteria was validated with a representative set of extant strains and environmental clones. The potential of the pmoA microarray was tested with environmental samples. The results were in good agreement with those of clone library sequence analyses. The approach can currently detect less dominant bacteria down to 5% of the total community targeted. Initial tests assessing the quantification potential of this system with artificial PCR mixtures showed very good correlation with the expected results with standard deviations in the range of 0.4-17.2%. Quantification of environmental samples with this method requires the design of a reference mixture consisting of very close relatives of the strains within the sample and is currently limited by biases inherent in environmental DNA extraction and universal PCR amplification.

Landfill sites are responsible for 6-12% of global methane emission. Methanotrophs play a very important role in decreasing landfill site methane emissions. We investigated the methane oxidation capacity and methanotroph diversity in lysimeters simulating landfill sites with different plant vegetations. Methane oxidation rates were 35 g methane m-2 day-1 or higher for planted lysimeters and 18 g methane m-2 day-1 or less for bare soil controls. Best methane oxidation, as displayed by gas depth profiles, was found under a vegetation of grass and alfalfa. Methanotroph communities were analysed at high throughput and resolution using a microbial diagnostic microarray targeting the particulate methane monooxygenase (pmoA) gene of methanotrophs and functionally related bacteria. Members of the genera Methylocystis and Methylocaldum were found to be the dominant members in landfill site simulating lysimeters. Soil bacterial communities in biogas free control lysimeters, which were less abundant in methanotrophs, were dominated by Methylocaldum. Type Ia methanotrophs were found only in the top layers of bare soil lysimeters with relatively high oxygen and low methane concentrations. A competetive advantage of type II methanotrophs over type Ia methanotrophs was indicated under all plant covers investigated. Analysis of average and individual results from parallel samples was used to identify general trends and variations in methanotroph community structures in relation to depth, methane supply and plant cover. The applicability of the technology for the detection of environmental perturbations was proven by an erroneous result, where an unexpected community composition detected with the microarray indicated a potential gas leakage in the lysimeter being investigated.

The active methanotroph community was investigated for the first time in heather (Calluna)-covered moorlands and Sphagnum/Eriophorum-covered UK peatlands. Direct extraction of mRNA from these soils facilitated detection of expression of methane monooxygenase genes, which revealed that particulate methane monooxygenase and not soluble methane monooxygenase was probably responsible for CH(4) oxidation in situ, because only pmoA transcripts (encoding a subunit of particulate methane monooxygenase) were readily detectable. Differences in methanotroph community structures were observed between the Calluna-covered moorland and Sphagnum/Eriophorum-covered gully habitats. As with many other Sphagnum-covered peatlands, the Sphagnum/Eriophorum-covered gullies were dominated by Methylocystis. Methylocella and Methylocapsa-related species were also present. Methylobacter-related species were found as demonstrated by the use of a pmoA-based diagnostic microarray. In Calluna-covered moorlands, in addition to Methylocella and Methylocystis, a unique group of peat-associated type I methanotrophs (Gammaproteobacteria) and a group of uncultivated type II methanotrophs (Alphaproteobacteria) were also found. The pmoA sequences of the latter were only distantly related to Methylocapsa and also to the RA-14 group of methanotrophs, which are believed to be involved in oxidation of atmospheric concentrations of CH(4). Soil samples were also labelled with (13)CH(4), and subsequent analysis of the (13)C-labelled phospholipid fatty acids (PLFAs) showed that 16:1 omega 7, 18:1 omega 7 and 18:1 omega 9 were the major labelled PLFAs. The presence of (13)C-labelled 18:1 omega 9, which was not a major PLFA of any extant methanotrophs, indicated the presence of novel methanotrophs in this peatland.

There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard 'dashboard' of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.