MOTIVATION: Whole-exome sequencing (WES) is now being used in clinical practice for the diagnosis of the causal genes of Mendelian diseases. In order to make the diagnosis, however, the clinical phenotypes [e.g. Human Phenotype Ontology (HPO) terms] of a patient are needed for prioritizing the variants called from the WES data of the patient. Computational tools are therefore needed to standardize and accelerate this process. RESULTS: Here, we introduce a tool named PhenoPro for prioritizing the causal gene of Mendelian disease given both the HPO terms assigned to and the variants called from the WES data of a patient. PhenoPro has been benchmarked using both simulated patients and 287 real diagnosed patients of Chinese ancestry, and shows significant improvements over five previous tools. Moreover, the addition of an internal variant data of Chinese ancestry and the variant data from the patients' parents can further improve PhenoPro's performance. To make PhenoPro a fully automated tool, we also include a natural language processing component for automated HPO term assignment from clinical reports, and demonstrate that the natural language processing is as effective as manual HPO assignment using real clinical reports. In conclusion, PhenoPro can be used as a pre-screening tool to assist in the diagnosis of Mendelian disease genes. AVAILABILITY AND IMPLEMENTATION: The web server of PhenoPro is freely available at http://app.tianlab.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: RNA editing generates post-transcriptional sequence alterations. Detection of RNA editing sites (RESs) typically requires the filtering of SNVs called from RNA-seq data using an SNP database, an obstacle that is difficult to overcome for most organisms. RESULTS: Here, we present a novel method named SPRINT that identifies RESs without the need to filter out SNPs. SPRINT also integrates the detection of hyper RESs from remapped reads, and has been fully automated to any RNA-seq data with reference genome sequence available. We have rigorously validated SPRINT's effectiveness in detecting RESs using RNA-seq data of samples in which genes encoding RNA editing enzymes are knock down or over-expressed, and have also demonstrated its superiority over current methods. We have applied SPRINT to investigate RNA editing across tissues and species, and also in the development of mouse embryonic central nervous system. A web resource (http://sprint.tianlab.cn) of RESs identified by SPRINT has been constructed. AVAILABILITY AND IMPLEMENTATION: The software and related data are available at http://sprint.tianlab.cn. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Genes with indispensable functions are identified as essential; however, the traditional gene-level studies of essentiality have several limitations. In this study, we characterized gene essentiality from a new perspective of protein domains, the independent structural or functional units of a polypeptide chain. RESULTS: To identify such essential domains, we have developed an Expectation-Maximization (EM) algorithm-based Essential Domain Prediction (EDP) Model. With simulated datasets, the model provided convergent results given different initial values and offered accurate predictions even with noise. We then applied the EDP model to six microbial species and predicted 1879 domains to be essential in at least one species, ranging 10-23% in each species. The predicted essential domains were more conserved than either non-essential domains or essential genes. Comparing essential domains in prokaryotes and eukaryotes revealed an evolutionary distance consistent with that inferred from ribosomal RNA. When utilizing these essential domains to reproduce the annotation of essential genes, we received accurate results that suggest protein domains are more basic units for the essentiality of genes. Furthermore, we presented several examples to illustrate how the combination of essential and non-essential domains can lead to genes with divergent essentiality. In summary, we have described the first systematic analysis on gene essentiality on the level of domains. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.