MutationDistiller: user-driven identification of pathogenic DNA variants.

MutationDistiller is a freely available online tool for user-driven analyses of Whole Exome Sequencing data. It offers a user-friendly interface aimed at clinicians and researchers, who are not necessarily bioinformaticians. MutationDistiller combines MutationTaster's pathogenicity predictions with a phenotype-based approach. Phenotypic information is not limited to symptoms included in the Human Phenotype Ontology (HPO), but may also comprise clinical diagnoses and the suspected mode of inheritance. The search can be restricted to lists of candidate genes (e.g. virtual gene panels) and by tissue-specific gene expression. The inclusion of GeneOntology (GO) and metabolic pathways facilitates the discovery of hitherto unknown disease genes. In a novel approach, we trained MutationDistiller's HPO-based prioritization on authentic genotype-phenotype sets obtained from ClinVar and found it to match or outcompete current prioritization tools in terms of accuracy. In the output, the program provides a list of potential disease mutations ordered by the likelihood of the affected genes to cause the phenotype. MutationDistiller provides links to gene-related information from various resources. It has been extensively tested by clinicians and their suggestions have been valued in many iterative cycles of revisions. The tool, a comprehensive documentation and examples are freely available at https://www.mutationdistiller.org/.

RegulationSpotter: annotation and interpretation of extratranscriptic DNA variants.

RegulationSpotter is a web-based tool for the user-friendly annotation and interpretation of DNA variants located outside of protein-coding transcripts (extratranscriptic variants). It is designed for clinicians and researchers who wish to assess the potential impact of the considerable number of non-coding variants found in Whole Genome Sequencing runs. It annotates individual variants with underlying regulatory features in an intuitive way by assessing over 100 genome-wide annotations. Additionally, it calculates a score, which reflects the regulatory potential of the variant region. Its dichotomous classifications, 'functional' or 'non-functional', and a human-readable presentation of the underlying evidence allow a biologically meaningful interpretation of the score. The output shows key aspects of every variant and allows rapid access to more detailed information about its possible role in gene regulation. RegulationSpotter can either analyse single variants or complete VCF files. Variants located within protein-coding transcripts are automatically assessed by MutationTaster as well as by RegulationSpotter to account for possible intragenic regulatory effects. RegulationSpotter offers the possibility of using phenotypic data to focus on known disease genes or genomic elements interacting with them. RegulationSpotter is freely available at https://www.regulationspotter.org.

Phenotero: Annotate as you write.

In clinical genetics, the Human Phenotype Ontology as well as disease ontologies are often used for deep phenotyping of patients and coding of clinical diagnoses. However, assigning ontology classes to patient descriptions is often disconnected from writing patient reports or manuscripts in word processing software. This additional workload and the requirement to install dedicated software may discourage usage of ontologies for parts of the target audience. Here we present Phenotero, a freely available and simple solution to annotate patient phenotypes and diseases at the time of writing clinical reports or manuscripts. We adopt Zotero, a citation management software to create a tool which allows to reference classes from ontologies within text at the time of writing. We expect this approach to decrease the additional workload to a minimum while ensuring high quality associations with ontology classes. Standardized collection of phenotypic information at the time of describing the patient allows for streamlining the clinic workflow and efficient data entry. It will subsequently promote clinical and molecular diagnosis with the ultimate goal of better understanding genetic diseases. Thus, we believe that Phenotero eases the usage of ontologies and controlled vocabularies in the field of clinical genetics.

A systematic, large-scale comparison of transcription factor binding site models.

BACKGROUND: The modelling of gene regulation is a major challenge in biomedical research. This process is dominated by transcription factors (TFs) and mutations in their binding sites (TFBSs) may cause the misregulation of genes, eventually leading to disease. The consequences of DNA variants on TF binding are modelled in silico using binding matrices, but it remains unclear whether these are capable of accurately representing in vivo binding. In this study, we present a systematic comparison of binding models for 82 human TFs from three freely available sources: JASPAR matrices, HT-SELEX-generated models and matrices derived from protein binding microarrays (PBMs). We determined their ability to detect experimentally verified "real" in vivo TFBSs derived from ENCODE ChIP-seq data. As negative controls we chose random downstream exonic sequences, which are unlikely to harbour TFBS. All models were assessed by receiver operating characteristics (ROC) analysis. RESULTS: While the area-under-curve was low for most of the tested models with only 47 % reaching a score of 0.7 or higher, we noticed strong differences between the various position-specific scoring matrices with JASPAR and HT-SELEX models showing higher success rates than PBM-derived models. In addition, we found that while TFBS sequences showed a higher degree of conservation than randomly chosen sequences, there was a high variability between individual TFBSs. CONCLUSIONS: Our results show that only few of the matrix-based models used to predict potential TFBS are able to reliably detect experimentally confirmed TFBS. We compiled our findings in a freely accessible web application called ePOSSUM ( http:/mutationtaster.charite.de/ePOSSUM/ ) which uses a Bayes classifier to assess the impact of genetic alterations on TF binding in user-defined sequences. Additionally, ePOSSUM provides information on the reliability of the prediction using our test set of experimentally confirmed binding sites.