Project description:The transcriptome of Actinobacillus pleuropneumoniae 4h static biofilms was compared to the transcriptome of 6h static biofilm 4 samples were analyzed which included 3 biological replicate, 1 technical replicate and 1 dye swap
Project description:Compare the physiological state between static, aerobic, and respiratory growth of Lactococcus lactis subsp. lactis CHCC2862 using whole genome transcriptomes. NOTE: the biological replicate array GSM243206 is dye-swapped relative to GSM202337 (unlike the two other biological replicate arrays GSM243203 and GSM24205). Keywords: Physiological response to aerobic and respiratory growth relative to static.
Project description:Compare the physiological state between static, aerobic, and respiratory growth of Lactococcus lactis subsp. lactis CHCC2862 using whole genome transcriptomes. NOTE: the biological replicate array GSM243206 is dye-swapped relative to GSM202337 (unlike the two other biological replicate arrays GSM243203 and GSM24205). Keywords: Physiological response to aerobic and respiratory growth relative to static. Static stationary cultures of CHCC2862 (Chr. Hansen Culture Collection, Hørsholm, Denmark) were inoculated into fresh pre-heated medium at the relevant conditions. OD600 was followed over time. At OD 1.0 samples were harvested for RNA isolation.
Project description:The below table includes a smaller list of data that was analyzed by dChip and filtered by pvalue such that a file with about 4600 genes was obtained, which allowed for ease of use from 40,000 genes. Keywords: static vs simulated microgravity
Project description:It is well known, but frequently overlooked, that low- and high-throughput molecular data may contain batch effects, i.e., systematic technical variation. Confounding of experimental batches with the variable(s) of interest is especially concerning, as a batch effect may then be interpreted as a biologically significant finding. An integral step towards reducing false discovery in molecular data analysis includes inspection for batch effects and application of computational tools to reduce this signal if present. In a 30-sample pilot Illumina Infinium HumanMethylation450 (450k array) experiment, we identified two sources of batch effects: array row and chip. Here, we demonstrate two approaches taken to process the 450k data in which an R function, ComBat, was applied to adjust for this non-biological signal. In the “initial analysis”, the application of ComBat to an unbalanced study design resulted in 9,683 and 19,192 significant (FDR<0.05) DNA methylation differences, despite none present prior to correction. Suspicious of this dramatic change, a “revised processing” included changes to our analysis as well as a greater number of samples, and successfully reduced batch effects without introducing false signal. Our work supports conclusions made by an article previously published in this journal: though the ultimate antidote to batch effects is thoughtful study design, every DNA methylation microarray analysis should inspect, assess and, if necessary, adjust for batch effects. The analysis experience presented here can serve as a reminder to the broader community to establish research questions a priori, ensure that they match with study design and encourage communication between technicians and analysts.
Project description:Time-course transcriptomic profilling of the oleaginous yeast Yarrowia lipolytica, during a controlled fed-batch. A nitrogen limitation was applied during the course of the fed-batch to initiate de novo biolipid synthesis.
