Decision-support software tools | Whole-exome sequencing data analysis
Disease targeted sequencing is gaining importance as a powerful and cost-effective application of high throughput sequencing technologies to the diagnosis. However, the lack of proper tools to process the data hinders its extensive adoption.
A deep phenotyping tool designed for collecting clinical symptoms and physical findings observed in patients. PhenoTips provides a series of features that help reduce the clinician’s workload during the clinical examination, and facilitates the safe sharing of de-identified data among medical institutions. It provides an easy-to-use web interface and standardized database back-end for collecting clinical findings. The main goals of this software are (i) to efficiently capture patient data in standard formats, facilitate effortless exchange of anonymized data, and enable automated searches in annotated gene and disease databases; and (ii) to provide advanced functionalities to reduce the clinician’s workload, permitting seamless use of this application within the clinician’s routine.
A free web-based phenotype-dependent NGS variant prioritizer, which leverages the wealth of information in GeneCards and its affiliated databases. VarElect employs GeneCards’ powerful search and scoring capacities, and its algorithm affords inferring direct as well as indirect links between sequenced genes and disease/symptom/phenotype keywords. The indirect links benefit from GeneCards’ excellent capacity to relate genes to each other via numerous annotations. VarElect thus provides a robust facility for ranking genes and pointing out their likelihood to be related to a patient’s disease.
A search engine for rare diseases, customised for diagnosis by clinicians in terms of the selection of curated data resources. FindZebra ranks documents decreasingly by their estimated relevance to the user query. Motivated by the goal of facilitating medical diagnostic search, FindZebra also offers the options of (i) clustering the retrieved documents by UMLS medical concepts derived from the document title, and (ii) ranking UMLS concepts as opposed to documents. Both options aim to facilitate cases where the search engine retrieves several documents covering the same disease. Results indicate that a specialized search engine can improve the diagnostic quality without compromising the ease of use of the currently widely popular standard web search. FindZebra outperforms Google overall for this task and especially when restricted to the sites of our collection, suggests that Google ranking algorithm is suboptimal for the task at hand.
Enables tightly integrated comparative variant analysis and visualization of thousands of next generation sequencing (NGS) data samples and millions of variants. BasePlayer is a highly efficient and user-friendly software for biological discovery in large-scale NGS data. It transforms an ordinary desktop computer into a large-scale genomic research platform, enabling also a non-technical user to perform complex comparative variant analyses, population frequency filtering and genome level annotations under intuitive, scalable and highly-responsive user interface to facilitate everyday genetic research as well as the search of novel discoveries.
Constructs patient-specific reports. MTB-Report returns a filtered list of patient’s actionable variants with respect to potential treatment options. It permits users to discover the clinical implications of genomic variants and facilitates the utilization of next generation sequencing (NGS) in clinical practice. This tool matches tumor genomes to targeted therapies. It is useful for genomic-based treatment decisions.
A prototype rare disease DDX generator. RDD automatically predicts the most likely rare diseases based on the known set of symptoms provided by the user. The goal of RDD is to estimate which are the most likely rare diseases a patient might suffer from, based on the symptoms shown by that patient and on the symptoms that are associated to each rare disease in the ORPHANET dataset.
An integrative framework for biomedical and biomolecular relationships among genes and genetic diseases. PhenUMA allows to retrieve information related with a set of genes, diseases or phenotypes of interest. One of its most innovative features is to combine the benefits of semantic similarity methods with the information taken from databases of genetic diseases and biological interactions. It builds, analyzes and visualizes networks based on both functional and phenotypic relationships. PhenUMA allows users to obtain coherent disease and gene clusters related to a particular disease, gene or set of phenotypes for research purposes. This tool helps in the discovery of alternative pathological roles of genes, biological functions and diseases.