SUMMARY: Phenopolis is an open-source web server providing an intuitive interface to genetic and phenotypic databases. It integrates analysis tools such as variant filtering and gene prioritization based on phenotype. The Phenopolis platform will accelerate clinical diagnosis, gene discovery and encourage wider adoption of the Human Phenotype Ontology in the study of rare genetic diseases. AVAILABILITY AND IMPLEMENTATION: A demo of the website is available at https://phenopolis.github.io . If you wish to install a local copy, source code and installation instruction are available at https://github.com/phenopolis . The software is implemented using Python, MongoDB, HTML/Javascript and various bash shell scripts. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. RESULTS: We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. AVAILABILITY AND IMPLEMENTATION: metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bionformatics online.

UNLABELLED: Non-invasive prenatal testing (NIPT) of fetal aneuploidy using cell-free fetal DNA is becoming part of routine clinical practice. RAPIDR (Reliable Accurate Prenatal non-Invasive Diagnosis R package) is an easy-to-use open-source R package that implements several published NIPT analysis methods. The input to RAPIDR is a set of sequence alignment files in the BAM format, and the outputs are calls for aneuploidy, including trisomies 13, 18, 21 and monosomy X as well as fetal sex. RAPIDR has been extensively tested with a large sample set as part of the RAPID project in the UK. The package contains quality control steps to make it robust for use in the clinical setting. AVAILABILITY AND IMPLEMENTATION: RAPIDR is implemented in R and can be freely downloaded via CRAN from here: http://cran.r-project.org/web/packages/RAPIDR/index.html. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Exome sequencing has proven to be an effective tool to discover the genetic basis of Mendelian disorders. It is well established that copy number variants (CNVs) contribute to the etiology of these disorders. However, calling CNVs from exome sequence data is challenging. A typical read depth strategy consists of using another sample (or a combination of samples) as a reference to control for the variability at the capture and sequencing steps. However, technical variability between samples complicates the analysis and can create spurious CNV calls. RESULTS: Here, we introduce ExomeDepth, a new CNV calling algorithm designed to control for this technical variability. ExomeDepth uses a robust model for the read count data and uses this model to build an optimized reference set in order to maximize the power to detect CNVs. As a result, ExomeDepth is effective across a wider range of exome datasets than the previously existing tools, even for small (e.g. one to two exons) and heterozygous deletions. We used this new approach to analyse exome data from 24 patients with primary immunodeficiencies. Depending on data quality and the exact target region, we find between 170 and 250 exonic CNV calls per sample. Our analysis identified two novel causative deletions in the genes GATA2 and DOCK8. AVAILABILITY: The code used in this analysis has been implemented into an R package called ExomeDepth and is available at the Comprehensive R Archive Network (CRAN).