Data set for transcriptome analysis of pituitary galnd in cattle breeds

Transcriptome data presented in this article is associated with the research article entitled “Single nucleotide polymorphism discovery in bovine pituitary gland using RNA-seq technology” published in PLOS One [1]. Herein, we provide raw and analysed RNA-seq data of pituitary gland tissues from three cattle breeds, viz., Polish-HF, Polish Red and Hereford cattle breeds. Bioinformatics pipelines of high-quality RNA-seq data includes the FastQC tools for quality controls, Trimmomatic cutadapt tools for trimming RNA-seq data, and BWA version 0.7.5-r404 for mapping and alignment to the Bos taurus reference genome, SAMtools for SNPs identifications in bovine pituitary gland transcriptome. Raw FASTq files for the RNA-seq libraries of bovine pituitary gland were deposited in the NCBI Sequence Read Archive (SRA) and have been assigned BioProject accession PRJNA312148

Data set for transcriptome analysis of liver in cattle breeds

Transcriptome analysis using high-throughput next-generation sequencing (HT-NGS) technology provides the capability to understand global gene expression variations through a wide range of tissue samples in domesticated animals. We provide raw and analysed data for transcriptomic analysis of liver tissues from Polish-HF, Polish Red and Hereford cattle breeds, obtained by RNA-seq. High-quality sequencing data have been analysed using our bioinformatics pipeline which consists of FastQC for quality controls, Trimmomatic for trimming, and BWA version 0.7.5-r404 for alignment to the Bos taurus reference genome, SAMtools for SNPs identifications, and differentially expressed genes (DEGs) identification using DEseq and edgeR pipelines after adjustment for false-discovery rate (FDR) with adjusted two-sided p values <0.01 and the trimmed mean of M values (TMM) normalisation method. The data accompanying the published manuscript describing the SNPs and DEGs identification in the bovine liver transcriptome of cattle breeds. Raw FASTq files for the RNA-seq libraries are deposited in the NCBI Sequence Read Archive (SRA) and have been assigned BioProject accession PRJNA312148. Raw and processed RNA-seq data were deposited and made publicly available on the Gene Expression Omnibus (GEO; GSE114233)

Plasmacytoid dendritic cells appear inactive during sub-microscopic Plasmodium falciparum blood-stage infection, yet retain their ability to respond to TLR stimulation

Plasmacytoid dendritic cells (pDC) are activators of innate and adaptive immune responses that express HLA-DR, toll-like receptor (TLR) 7, TLR9 and produce type I interferons. The role of human pDC in malaria remains poorly characterised. pDC activation and cytokine production were assessed in 59 malaria-naive volunteers during experimental infection with 150 or 1,800 P. falciparum-parasitized red blood cells. Using RNA sequencing, longitudinal changes in pDC gene expression were examined in five adults before and at peak-infection. pDC responsiveness to TLR7 and TLR9 stimulation was assessed in-vitro. Circulating pDC remained transcriptionally stable with gene expression altered for 8 genes (FDR < 0.07). There was no upregulation of co-stimulatory molecules CD86, CD80, CD40, and reduced surface expression of HLA-DR and CD123 (IL-3R-α). pDC loss from the circulation was associated with active caspase-3, suggesting pDC apoptosis during primary infection. pDC remained responsive to TLR stimulation, producing IFN-α and upregulating HLA-DR, CD86, CD123 at peak-infection. In clinical malaria, pDC retained HLA-DR but reduced CD123 expression compared to convalescence. These data demonstrate pDC retain function during a first blood-stage P. falciparum exposure despite sub-microscopic parasitaemia downregulating HLA-DR. The lack of evident pDC activation in both early infection and malaria suggests little response of circulating pDC to infection

piPipes: a set of pipelines for piRNA and transposon analysis via small RNA-seq, RNA-seq, degradome- and CAGE-seq, ChIP-seq and genomic DNA sequencing

MOTIVATION: PIWI-interacting RNAs (piRNAs), 23-36 nt small silencing RNAs, repress transposon expression in the metazoan germ line, thereby protecting the genome. Although high-throughput sequencing has made it possible to examine the genome and transcriptome at unprecedented resolution, extracting useful information from gigabytes of sequencing data still requires substantial computational skills. Additionally, researchers may analyze and interpret the same data differently, generating results that are difficult to reconcile. To address these issues, we developed a coordinated set of pipelines, \u27piPipes\u27, to analyze piRNA and transposon-derived RNAs from a variety of high-throughput sequencing libraries, including small RNA, RNA, degradome or 7-methyl guanosine cap analysis of gene expression (CAGE), chromatin immunoprecipitation (ChIP) and genomic DNA-seq. piPipes can also produce figures and tables suitable for publication. By facilitating data analysis, piPipes provides an opportunity to standardize computational methods in the piRNA field. SUPPLEMENTARY INFORMATION: Supplementary information, including flowcharts and example figures for each pipeline, are available at Bioinformatics online. AVAILABILITY AND IMPLEMENTATION: piPipes is implemented in Bash, C++, Python, Perl and R. piPipes is free, open-source software distributed under the GPLv3 license and is available at http://bowhan.github.io/piPipes/. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

The expansion of thymopoiesis in neonatal mice is dependent on expression of high mobility group a 2 protein (Hmga2).

Cell number in the mouse thymus increases steadily during the first two weeks after birth. It then plateaus and begins to decline by seven weeks after birth. The factors governing these dramatic changes in cell production are not well understood. The data herein correlate levels of High mobility group A 2 protein (Hmga2) expression with these temporal changes in thymopoiesis. Hmga2 is expressed at high levels in murine fetal and neonatal early T cell progenitors (ETP), which are the most immature intrathymic precursors, and becomes almost undetectable in these progenitors after 5 weeks of age. Hmga2 expression is critical for the initial, exponential expansion of thymopoiesis, as Hmga2 deficient mice have a deficit of ETPs within days after birth, and total thymocyte number is repressed compared to wild type littermates. Finally, our data raise the possibility that similar events occur in humans, because Hmga2 expression is high in human fetal thymic progenitors and falls in these cells during early infancy

Finite mixtures of matrix-variate Poisson-log normal distributions for three-way count data

Three-way data structures, characterized by three entities, the units, the variables and the occasions, are frequent in biological studies. In RNA sequencing, three-way data structures are obtained when high-throughput transcriptome sequencing data are collected for n genes across p conditions at r occasions. Matrix-variate distributions offer a natural way to model three-way data and mixtures of matrix-variate distributions can be used to cluster three-way data. Clustering of gene expression data is carried out as means to discovering gene co-expression networks. In this work, a mixture of matrix-variate Poisson-log normal distributions is proposed for clustering read counts from RNA sequencing. By considering the matrix-variate structure, full information on the conditions and occasions of the RNA sequencing dataset is simultaneously considered, and the number of covariance parameters to be estimated is reduced. A Markov chain Monte Carlo expectation-maximization algorithm is used for parameter estimation and information criteria are used for model selection. The models are applied to both real and simulated data, giving favourable clustering results

Neurotranscriptome profiles of multiple zebrafish strains

Behavioral displays or physiological responses are often influenced by intrinsic and extrinsic mechanisms in the context of the organism\u27s evolutionary history. Understanding differences in transcriptome profiles can give insight into adaptive or pathological responses.We utilize high throughput sequencing (RNA-sequencing) to characterize the neurotranscriptome profiles in both males and females across four strains of zebrafish (Danio rerio). Strains varied by previously documented differences in stress and anxiety-like behavioral responses, and generations removed from wild-caught individuals. Here we describe detailed methodologies and quality controls in generating the rawRNA-sequencing reads that are publically available in NCBI\u27s Gene Expression Omnibus database (GSE61108)