GenomicDistributions: fast analysis of genomic intervals with Bioconductor.

BACKGROUND: Epigenome analysis relies on defined sets of genomic regions output by widely used assays such as ChIP-seq and ATAC-seq. Statistical analysis and visualization of genomic region sets is essential to answer biological questions in gene regulation. As the epigenomics community continues generating data, there will be an increasing need for software tools that can efficiently deal with more abundant and larger genomic region sets. Here, we introduce GenomicDistributions, an R package for fast and easy summarization and visualization of genomic region data. RESULTS: GenomicDistributions offers a broad selection of functions to calculate properties of genomic region sets, such as feature distances, genomic partition overlaps, and more. GenomicDistributions functions are meticulously optimized for best-in-class speed and generally outperform comparable functions in existing R packages. GenomicDistributions also offers plotting functions that produce editable ggplot objects. All GenomicDistributions functions follow a uniform naming scheme and can handle either single or multiple region set inputs. CONCLUSIONS: GenomicDistributions offers a fast and scalable tool for exploratory genomic region set analysis and visualization. GenomicDistributions excels in user-friendliness, flexibility of outputs, breadth of functions, and computational performance. GenomicDistributions is available from Bioconductor ( https://bioconductor.org/packages/release/bioc/html/GenomicDistributions.html ).

In vivo characterization of inflammatory biomarkers in swine and the impact of flunixin meglumine administration.

Non-steroidal anti-inflammatory drugs (NSAID) are a family of chemicals that function to reduce pain, fever, and inflammation, and they are commonly used in people and animals for this purpose. Currently there are no NSAIDs approved for the management of inflammation in swine due to a lack of validated animal models and suitable biomarkers to assess efficacy. A previous in vitro study examining biomarkers of inflammation identified fourteen genes that were significantly altered in response to Escherichia coli lipopolysaccharide (LPS)-induced inflammation. In the present study, five of those fourteen genes were tested in vivo to determine if the same effects observed in vitro were also observed in vivo. Plasma levels of prostaglandin E(2) (PGE(2)), an essential mediator of fever and inflammation, were also determined. Two groups of swine were stimulated with LPS with the second group also treated with flunixin meglumine. Blood was collected at 0, 1, 3, 6, 8, 24, and 48 h post LPS-stimulation. The RNA was extracted from the blood and quantitative real-time-PCR (qRT-PCR) was utilized to determine the expression patterns of CD1, CD4, serum amyloid A2 (SAA2), Caspase 1, and monocyte chemoattractant protein 1 (MCP-1). The LPS-stimulated animals demonstrated a statistically significant alteration in expression of SAA2 and CD1 at 3h post-stimulation. Flunixin meglumine treated animals' demonstrated reduced expression of CD1 in comparison to the LPS-stimulated swine at 24 and 48 h post LPS-stimulation. Flunixin meglumine treated animals exhibited reduced expression of SAA2 at 48 h post-stimulation compared to LPS-stimulated swine. Swine treated with LPS demonstrated statistically significant increases in plasma PGE(2) at 1h post-stimulation. Swine treated with flunixin meglumine had no increase in plasma PGE(2) levels at any time. These results demonstrate that PGE(2) production, along with two out of five genes (SAA2 and CD1) have the potential to serve as early biomarkers of inflammation as well as indicators of NSAID efficacy.

In vitro identification and verification of inflammatory biomarkers in swine.

Currently there are no non-steroidal anti-inflammatory drugs (NSAIDs) approved for the control of inflammation in swine due to a lack of validated animal models and suitable biomarkers to assess drug efficacy. This study investigates the differential expression of genes altered in response to Escherichia coli lipopolysaccharide (LPS) induced inflammation which may serve as indicators of NSAID efficacy. Unstimulated whole blood from swine was mixed with tissue culture media, stimulated with LPS, and RNA extracted at the following time points 0h, 1h, 3h, 24h and 48h. Total RNA was extracted and analyzed using a commercial swine DNA microarray. The DNA microarray was utilized as a screen to determine potential biomarkers, focusing on the genes that exhibited the greatest degree of differential expression. A master list of 57 genes was formed based on the differential expression as a result of the stimulation. Following analysis, 12 genes whose expressions were significantly altered (8 up- and 4 down-regulated) were chosen for verification via quantitative RT-PCR (qRT-PCR). The qRT-PCR analysis confirmed the differential expression of 11 of the 12 genes chosen via the microarray analyses. Specifically, traditional genes such as SAA, G-CSF, and IL-10 were up-regulated, while CD4 was down-regulated; all of the genes were altered by 24h or 48h post-stimulation. We demonstrate here that expression of these 11 genes is altered as a direct result of LPS stimulation and consequently inflammation.