The aim of this study was to elucidate the differential gene expression in the RNA sequencing transcriptome of isolated perfused udders collected from 4 slaughtered Holstein × Zebu crossbred dairy cows experimentally inoculated with Streptococcus agalactiae. We studied 3 different statistical tools (edgeR, baySeq, and Cuffdiff 2). In summary, 2 quarters of each udder were experimentally inoculated with Strep. agalactiae and the other 2 were used as a control. Mammary tissue biopsies were collected at times 0 and 3 h after infection. The total RNA was extracted and sequenced on an Illumina HiSeq 2000 (Illumina Inc., San Diego, CA). Transcripts were assembled from the reads aligned to the bovine UMD 3.1 reference genome, and the statistical analyses were performed using the previously mentioned tools (edgeR, baySeq, and Cuffdiff 2). Finally, the identified genes were submitted to pathway enrichment analysis. A total of 1,756, 1,161, and 3,389 genes with differential gene expression were identified when using edgeR, baySeq, and Cuffdiff 2, respectively. A total of 122 genes were identified by the overlapping of the 3 methods; however, only the platelet activation presented a significantly enriched pathway. From the results, we suggest the FCER1G, GNAI2, ORAI1, and VASP genes shared among the 3 methods in this pathway for posterior biological validation.
ABSTRACT. We propose and evaluate a novel approach for forecasting gene expression over non-observed times in longitudinal trials under a Bayesian viewpoint. One of the aims is to cluster genes that share similar expression patterns over time and then use this similarity to predict relative expression at time points of interest. Expression values of 106 genes expressed during the cell cycle of Saccharomyces cerevisiae were used and genes were partitioned into five distinct clusters of sizes 33, 32, 21, 16, and 4. After removing the last observed time point, the agreements of signals (upregulated or downregulated) considering the predicted expression level were 72.7, 81.3, 76.2, 68.8, and 50.0%, This approach is easily implemented with other time-series models and when Poisson and negative binomial probability distributions are assumed for the gene expression data.
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