Project description:Microarray technology has enabled the measurement of comprehensive transcriptomic information. However, each data entry may reflect trivial individual differences among samples and also contain technical noise. Therefore, the certainty of each observed difference should be confirmed at earlier steps of the analyses, and statistical tests are frequently used for this purpose. Since a microarray measures a huge number of genes and the results are processed simultaneously, concerns regarding problems of multiplicity have been raised to the tests. To deal with these problems, several methodologies have been proposed, making the tests very conservative. Indeed, arbitrary tuning of the test threshold has also been introduced to relax the test conditions. However, the appropriateness of the multiplicity problems as well as the compensation methods has not been confirmed. The appropriateness of the compensation methods was checked by means of coincidence of the premises of the methodologies with the observed characteristics found in real data of two typical platforms of microarray analysis. Normality was observed in within-group data variations, supporting applications of parametric tests. However, genes displayed their own tendencies in the magnitude of variations, and the distributions of P-values were rather complex and varied; these characteristics are inconsistent with the premises of the compensation methodologies. Additionally, the appropriateness of the proposed multiplicities is reconsidered. When we observed differences in the transcriptome, the family-wise error rate should not be considered, since analyses at higher levels would not be influenced by a few false positives among the huge numbers of true information. Likely, concerns for a false discovery rate are not suitable for the point null hypotheses on expression levels, since the rate of true null hypotheses should be rare in contradiction to the premise of the methodology. Although compensation methods have been recommended to deal with the problem of multiplicity, the compensations are actually inappropriate for many of the applications of transcriptome analyses. Compensations are not only unnecessary, but will increase the occurrence of false negative errors, and arbitrary adjustment of the threshold damages the objectivity of the tests. Rather, the results of parametric tests should be evaluated directly. This SuperSeries is composed of the SubSeries listed below.
Project description:Pyruvate fermentation pathway and energetics of Desulfovibrio alaskensis strain G20 under syntrophic coculture and fermentative monoculture conditions Expression data for Desulfovibrio alaskensis strain G20 grown in chemostats on pyruvate under respiratory conditions (sulfate-limited and pyruvate-limited monoculture, dilution rate 0.047 and 0.027 h-1), fermentative conditions (monoculture, dilution rate 0.036 h-1), and syntrophic conditions (coculture with Methanococcus maripaludis or Methanospirillum hungatei, dilution rate of 0.047 and 0.027 h-1)
Project description:The continued emergence of SARS-CoV-2 variants is one of several factors that may cause false negative viral PCR test results. Such tests are also susceptible to false positive results due to trace contamination from high viral titer samples. Host immune response markers provide an orthogonal indication of infection that can mitigate these concerns when combined with direct viral detection. Here, we leverage nasopharyngeal swab RNA-seq data from patients with COVID-19, other viral acute respiratory illnesses and non-viral conditions (n=318) to develop support vector machine classifiers that rely on a parsimonious 2-gene host signature to diagnose COVID-19. We find that optimal classifiers include an interferon-stimulated gene that is strongly induced in COVID-19 compared with non-viral conditions, such as IFI6, and a second immune-response gene that is more strongly induced in other viral infections, such as GBP5. The IFI6+GBP5 classifier achieves an area under the receiver operating characteristic curve (AUC) greater than 0.9 when evaluated on an independent RNA-seq cohort (n=553). We further provide proof-of-concept demonstration that the classifier can be implemented in a clinically relevant RT-qPCR assay. Finally, we show that its performance is robust across common SARS-CoV-2 variants and is unaffected by cross-contamination, demonstrating its utility for improving accuracy of COVID-19 diagnostics.
Project description:The marine bacterium Rhodococcus erythropolis PR4 was demonstrated to be able for assimilation/biodegradation of hydrocarbons. Not just the chromosome but two large plasmids provide versatile enzyme sets involved in many metabolic pathways. In order to identify the key elements involved in biodegradation of the model compound, hexadecane, and diesel oil, we performed whole transcriptome analysis on cells grown in the presence of n-hexadecane and diesel oil. Sodium acetate grown cells were used as control. The final goal of the project is a comparative transcriptomic analysis of Rhodococcus erythropolis PR4 cells grown on acetate, on the model compound: hexadecane and the real substrate: diesel oil.