Nonribosomal Peptide Synthetases in Animals.

Nonribosomal peptide synthetases (NRPSs) are a class of cytosolic enzymes that synthesize a range of bio-active secondary metabolites including antibiotics and siderophores. They are widespread among both prokaryotes and eukaryotes but are considered rare among animals. Recently, several novel NRPS genes have been described in nematodes, schistosomes, and arthropods, which led us to investigate how prevalent NRPS genes are in the animal kingdom. We screened 1059 sequenced animal genomes and showed that NRPSs were present in 7 out of the 19 phyla analyzed. A phylogenetic analysis showed that the identified NRPSs form clades distinct from other adenylate-forming enzymes that contain similar domains such as fatty acid synthases. NRPSs show a remarkably scattered distribution over the animal kingdom. They are especially abundant in rotifers and nematodes. In rotifers, we found a large variety of domain architectures and predicted substrates. In the nematode Plectus sambesii, we identified the beta-lactam biosynthesis genes L-δ-(α-aminoadipoyl)-L-cysteinyl-D-valine synthetase, isopenicillin N synthase, and deacetoxycephalosporin C synthase that catalyze the formation of beta-lactam antibiotics in fungi and bacteria. These genes are also present in several species of Collembola, but not in other hexapods analyzed so far. In conclusion, our survey showed that NRPS genes are more abundant and widespread in animals than previously known.

A comparison of automatic cell identification methods for single-cell RNA sequencing data.

BACKGROUND: Single-cell transcriptomics is rapidly advancing our understanding of the cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on manual annotations to determine cell identities, which are time-consuming and irreproducible. The exponential growth in the number of cells and samples has prompted the adaptation and development of supervised classification methods for automatic cell identification. RESULTS: Here, we benchmarked 22 classification methods that automatically assign cell identities including single-cell-specific and general-purpose classifiers. The performance of the methods is evaluated using 27 publicly available single-cell RNA sequencing datasets of different sizes, technologies, species, and levels of complexity. We use 2 experimental setups to evaluate the performance of each method for within dataset predictions (intra-dataset) and across datasets (inter-dataset) based on accuracy, percentage of unclassified cells, and computation time. We further evaluate the methods' sensitivity to the input features, number of cells per population, and their performance across different annotation levels and datasets. We find that most classifiers perform well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose support vector machine classifier has overall the best performance across the different experiments. CONCLUSIONS: We present a comprehensive evaluation of automatic cell identification methods for single-cell RNA sequencing data. All the code used for the evaluation is available on GitHub ( https://github.com/tabdelaal/scRNAseq_Benchmark ). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support the extension of new methods and new datasets.