The accurate mapping of reads that span splice junctions is a critical component of all analytic techniques that work with RNA-seq data. We introduce a second generation splice detection algorithm, MapSplice, whose focus is high sensitivity and specificity in the detection of splices as well as CPU and memory efficiency. MapSplice can be applied to both short (<75 bp) and long reads (≥75 bp). MapSplice is not dependent on splice site features or intron length, consequently it can detect novel canonical as well as non-canonical splices. MapSplice leverages the quality and diversity of read alignments of a given splice to increase accuracy. We demonstrate that MapSplice achieves higher sensitivity and specificity than TopHat and SpliceMap on a set of simulated RNA-seq data. Experimental studies also support the accuracy of the algorithm. Splice junctions derived from eight breast cancer RNA-seq datasets recapitulated the extensiveness of alternative splicing on a global level as well as the differences between molecular subtypes of breast cancer. These combined results indicate that MapSplice is a highly accurate algorithm for the alignment of RNA-seq reads to splice junctions. Software download URL: http://www.netlab.uky.edu/p/bioinfo/MapSplice.

Summary: The horse, like the majority of animal species, has a limited amount of species‐specific expressed sequence data available in public databases. As a result, structural models for the majority of genes defined in the equine genome are predictions based onab initiosequence analysis or the projection of gene structures from other mammalian species. The current study used Illumina‐based sequencing of messenger RNA (RNA‐seq) to help refine structural annotation of equine protein‐coding genes and for a preliminary assessment of gene expression patterns. Sequencing of mRNA from eight equine tissues generated 293 758 105 sequence tags of 35 bases each, equalling 10.28 gbp of total sequence data. The tag alignments represent approximately 207× coverage of the equine mRNA transcriptome and confirmed transcriptional activity for roughly 90% of the protein‐coding gene structures predicted by Ensembl and NCBI. Tag coverage was sufficient to refine the structural annotation for 11 356 of these predicted genes, while also identifying an additional 456 transcripts with exon/intron features that are not listed by either Ensembl or NCBI. Genomic locus data and intervals for the protein‐coding genes predicted by the Ensembl and NCBI annotation pipelines were combined with 75 116 RNA‐seq‐derived transcriptional units to generate a consensus equine protein‐coding gene set of 20 302 defined loci. Gene ontology annotation was used to compare the functional and structural categories of genes expressed in either a tissue‐restricted pattern or broadly across all tissue samples.

Fertility traits, such as heifer pregnancy, are economically important in cattle production systems, and are therefore, used in genetic selection programs. The aim of this study was to identify single nucleotide polymorphisms (SNPs) using RNA-sequencing (RNA-Seq) data from ovary, uterus, endometrium, pituitary gland, hypothalamus, liver, longissimus dorsi muscle, and adipose tissue in 62 candidate genes associated with heifer puberty in cattle. RNA-Seq reads were assembled to the bovine reference genome (UMD 3.1.1) and analyzed in five cattle breeds; Brangus, Brahman, Nellore, Angus, and Holstein. Two approaches used the Brangus data for SNP discovery 1) pooling all samples, and 2) within each individual sample. These approaches revealed 1157 SNPs. These were compared with those identified in the pooled samples of the other breeds. Overall, 172 SNPs within 13 genes (CPNE5, FAM19A4, FOXN4, KLF1, LOC777593, MGC157266, NEBL, NRXN3, PEPT-1, PPP3CA, SCG5, TSG101, and TSHR) were concordant in the five breeds. Using Ensembl's Variant Effector Predictor, we determined that 12% of SNPs were in exons (71% synonymous, 29% nonsynonymous), 1% were in untranslated regions (UTRs), 86% were in introns, and 1% were in intergenic regions. Since these SNPs were discovered in RNA, the variants were predicted to be within exons or UTRs. Overall, 160 novel transcripts in 42 candidate genes and five novel genes overlapping five candidate genes were observed. In conclusion, 1157 SNPs were identified in 62 candidate genes associated with puberty in Brangus cattle, of which, 172 were concordant in the five cattle breeds. Novel transcripts and genes were also identified.

Previously, we have shown that horses could be divided into susceptible and resistant groups based on an in vitro assay using dual-color flow cytometric analysis of CD3+ T cells infected with equine arteritis virus (EAV). Here, we demonstrate that the differences in in vitro susceptibility of equine CD3+ T lymphocytes to EAV infection have a genetic basis. To investigate the possible hereditary basis for this trait, we conducted a genome-wide association study (GWAS) to compare susceptible and resistant phenotypes. Testing of 267 DNA samples from four horse breeds that had a susceptible or a resistant CD3+ T lymphocyte phenotype using both Illumina Equine SNP50 BeadChip and Sequenom's MassARRAY system identified a common, genetically dominant haplotype associated with the susceptible phenotype in a region of equine chromosome 11 (ECA11), positions 49572804 to 49643932. The presence of a common haplotype indicates that the trait occurred in a common ancestor of all four breeds, suggesting that it may be segregated among other modern horse breeds. Biological pathway analysis revealed several cellular genes within this region of ECA11 encoding proteins associated with virus attachment and entry, cytoskeletal organization, and NF-κB pathways that may be associated with the trait responsible for the in vitro susceptibility/resistance of CD3+ T lymphocytes to EAV infection. The data presented in this study demonstrated a strong association of genetic markers with the trait, representing de facto proof that the trait is under genetic control. To our knowledge, this is the first GWAS of an equine infectious disease and the first GWAS of equine viral arteritis.