Summary: Many time-consuming analyses of next - generation sequencing data can be addressed with modern cloud computing. The Apache Hadoop-based solutions have become popular in genomics because of their scalability in a cloud infrastructure. So far, most of these tools have been used for batch data processing rather than interactive data querying. The SparkSeq software has been created to take advantage of a new MapReduce framework, Apache Spark, for next-generation sequencing data. SparkSeq is a general-purpose, flexible and easily extendable library for genomic cloud computing. It can be used to build genomic analysis pipelines in Scala and run them in an interactive way. SparkSeq opens up the possibility of customized ad hoc secondary analyses and iterative machine learning algorithms. This article demonstrates its scalability and overall fast performance by running the analyses of sequencing datasets. Tests of SparkSeq also prove that the use of cache and HDFS block size can be tuned for the optimal performance on multiple worker nodes. Availability and implementation: Available under open source Apache 2.0 license: https://bitbucket.org/mwiewiorka/sparkseq/ . Contact:marek.wiewiorka@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

The identification of quantitative trait loci (QTL) such as height and their underlying causative variants is still challenging and often requires large sample sizes. In humans hundreds of loci with small effects control the heritable portion of height variability. In domestic animals, typically only a few loci with comparatively large effects explain a major fraction of the heritability. We investigated height at withers in Shetland ponies and mapped a QTL to ECA 6 by genome-wide association (GWAS) using a small cohort of only 48 animals and the Illumina equine SNP70 BeadChip. Fine-mapping revealed a shared haplotype block of 793 kb in small Shetland ponies. The HMGA2 gene, known to be associated with height in horses and many other species, was located in the associated haplotype. After closing a gap in the equine reference genome we identified a non-synonymous variant in the first exon of HMGA2 in small Shetland ponies. The variant was predicted to affect the functionally important first AT-hook DNA binding domain of the HMGA2 protein (c.83G>A; p.G28E). We assessed the functional impact and found impaired DNA binding of a peptide with the mutant sequence in an electrophoretic mobility shift assay. This suggests that the HMGA2 variant also affects DNA binding in vivo and thus leads to reduced growth and a smaller stature in Shetland ponies. The identified HMGA2 variant also segregates in several other pony breeds but was not found in regular-sized horse breeds. We therefore conclude that we identified a quantitative trait nucleotide for height in horses.

The adaptive response to extreme endurance exercise might involve transcriptional and translational regulation by microRNAs (miRNAs). Therefore, the objective of the present study was to perform an integrated analysis of the blood transcriptome and miRNome (using microarrays) in the horse before and after a 160 km endurance competition. A total of 2,453 differentially expressed genes and 167 differentially expressed microRNAs were identified when comparing pre- and post-ride samples. We used a hypergeometric test and its generalization to gain a better understanding of the biological functions regulated by the differentially expressed microRNA. In particular, 44 differentially expressed microRNAs putatively regulated a total of 351 depleted differentially expressed genes involved variously in glucose metabolism, fatty acid oxidation, mitochondrion biogenesis, and immune response pathways. In an independent validation set of animals, graphical Gaussian models confirmed that miR-21-5p, miR-181b-5p and miR-505-5p are candidate regulatory molecules for the adaptation to endurance exercise in the horse. To the best of our knowledge, the present study is the first to provide a comprehensive, integrated overview of the microRNA-mRNA co-regulation networks that may have a key role in controlling post-transcriptomic regulation during endurance exercise.

MicroRNAs (miRNAs) regulate post-transcriptional gene expression and may be exported from cells via exosomes or in partnership with RNA-binding proteins. MiRNAs in body fluids can act in a hormone-like manner and play important roles in disease initiation and progression. Hence, miRNAs are promising candidates as biomarkers. To identify serum miRNA biomarkers in the equine model of asthma we investigated small RNA derived from the serum of 34 control and 37 asthmatic horses. These samples were used for next generation sequencing, novel miRNA identification and differential miRNA expression analysis. We identified 11 significantly differentially expressed miRNAs between case and control horses: eca-miR-128, eca-miR-744, eca-miR-197, eca-miR-103, eca-miR-107a, eca-miR-30d, eca-miR-140-3p, eca-miR-7, eca-miR-361-3p, eca-miR-148b-3p and eca-miR-215. Pathway enrichment using experimentally validated target genes of the human homologous miRNAs showed a significant enrichment in the regulation of epithelial-to-mesenchymal transition (key player in airway remodeling in asthma) and the phosphatidylinositol (3,4,5)-triphosphate (PIP3) signaling pathway (modulator of CD4⁺ T cell maturation and function). Downregulated miR-128 and miR-744 supports a Th2/Th17 type immune response in severe equine asthma.

Integrating single-cell imaging and gene expression data illustrates how cells differentially interpret pathogenic stimuli.

MiRNAs regulate multiple genes at the post-transcriptional level and therefore play an important role in many biological processes. It has been suggested that miRNA exported outside the cells contribute to inter-cellular communication. Consequently, circulating miRNAs are of particular interest and are promising biomarkers for many diseases. The number of miRNAs annotated in the horse genome is much lower compared to model organisms like human and mouse. We therefore aimed to identify novel equine miRNAs for tissue types and breed in serum. We analysed 71 small RNA-seq libraries derived from nine tissues (gluteus medius, platysma, masseter muscle, heart, liver, cartilage, bone, total blood and serum) using miRDeep2 and miRdentify tools. Known miRNAs represented between 2.3 and 62.9 % of the reads in 71 libraries. A total of 683 novel miRNAs were identified. Breed and tissue type affected the number of miRNAs detected and interestingly, affected its average intensity. A total of 50 miRNAs in serum proved to be potential biomarkers to differentiate specific breed types, of which miR-122, miR-200, miR-483 were over-expressed and miR-328 was under-expressed in ponies compared to Warmbloods. The different miRNAs profiles, as well as the differences in their expression levels provide a foundation for more hypotheses based on the novel miRNAs discovered. We identified 683 novel equine miRNAs expressed in seven solid tissues, blood and serum. Additionally, our approach evidenced that such data supported identification of specific miRNAs as markers of functions related to breeds or disease tissues.