Detection of pathogenic splicing events from RNA-sequencing data using dasper

Although next-generation sequencing technologies have accelerated the discovery of novel gene-to-disease associations, many patients with suspected Mendelian diseases still leave the clinic without a genetic diagnosis. An estimated one third of these patients will have disorders caused by mutations impacting splicing. RNA-sequencing has been shown to be a promising diagnostic tool, however few methods have been developed to integrate RNA-sequencing data into the diagnostic pipeline. Here, we introduce dasper, an R/Bioconductor package that improves upon existing tools for detecting aberrant splicing by using machine learning to incorporate disruptions in exon-exon junction counts as well as coverage. dasper is designed for diagnostics, providing a rank-based report of how aberrant each splicing event looks, as well as including visualization functionality to facilitate interpretation. We validate dasper using 16 patient-derived fibroblast cell lines harbouring pathogenic variants known to impact splicing. We find that dasper is able to detect pathogenic splicing events with greater accuracy than existing LeafCutterMD or z-score approaches. Furthermore, by only applying a broad OMIM gene filter (without any variant-level filters), dasper is able to detect pathogenic splicing events within the top 10 most aberrant identified for each patient. Since using publicly available control data minimises costs associated with incorporating RNA-sequencing into diagnostic pipelines, we also investigate the use of 504 GTEx fibroblast samples as controls. We find that dasper leverages publicly available data effectively, ranking pathogenic splicing events in the top 25. Thus, we believe dasper can increase diagnostic yield for a pathogenic splicing variants and enable the efficient implementation of RNA-sequencing for diagnostics in clinical laboratories.

A gene filter for comparative analysis of single-cell RNA-sequencing trajectory datasets

Unsupervised feature selection, or gene filtering, is a common preprocessing step to reduce the dimensionality of single-cell RNA sequencing (scRNAseq) data sets. Existing gene filters operate on scRNAseq datasets in isolation from other datasets. When jointly analyzing multiple datasets, however, there is a need for gene filters that are tailored to comparative analysis. In this work, we present a method for ranking the relevance of genes for comparing trajectory datasets. Our method is unsupervised, i.e., the cell metadata are not assumed to be known. Using the top-ranking genes significantly improves performance compared to methods not tailored to comparative analysis. We demonstrate the effectiveness of our algorithm on previously published datasets from studies on preimplantation embryo development, neurogenesis and cardiogenesis.

Prevalence and Molecular Characterization of Tetracycline Resistance in Enterococcus Isolates from Food

ABSTRACT In the present study, a collection of 187 Enterococcus food isolates mainly originating from European cheeses were studied for the phenotypic and genotypic assessment of tetracycline (TC) resistance. A total of 45 isolates (24%) encompassing the species Enterococcus faecalis (n = 33), E. durans (n = 7), E. faecium (n = 3), E. casseliflavus (n = 1), and E. gallinarum (n = 1) displayed phenotypic resistance to TC with MIC ranges of 16 to 256 μg/ml. Eight of these strains exhibited multiresistance to TC, erythromycin, and chloramphenicol. By PCR detection, TC resistance could be linked to the presence of the tet(M) (n = 43), tet(L) (n = 16), and tet(S) (n = 1) genes. In 15 isolates, including all of those for which the MIC was 256 μg/ml, both tet(M) and tet(L) were found. Furthermore, all tet(M)-containing enterococci also harbored a member of the Tn916-Tn1545 conjugative transposon family, of which 12 erythromycin-resistant isolates also contained the erm(B) gene. Filter mating experiments revealed that 10 E. faecalis isolates, 3 E. durans isolates, and 1 E. faecium isolate could transfer either tet(M), tet(L), or both of these genes to E. faecalis recipient strain JH2-2. In most cases in which only tet(M) was transferred, no detectable plasmids were acquired by JH2-2 but instead all transconjugants contained a member of the Tn916-Tn1545 family. Sequencing analysis of PCR amplicons and evolutionary modeling showed that a subset of the transferable tet(M) genes belonged to four sequence homology groups (SHGs) showing an internal homology of ≥99.6%. Two of these SHGs contained tet(M) mosaic structures previously found in Tn916 elements and on Lactobacillus and Neisseria plasmids, respectively, whereas the other two SHGs probably represent new phylogenetic lineages of this gene.