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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.

Source text:
(Aleman et al., 2014) A web tool for the design and management of panels of genes for targeted enrichment and massive sequencing for clinical applications. Nucleic Acids Res.

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BioGranat-IG / BioGranat Individuals-Grouping
A BioGranat plugin for the analysis of exome-sequencing data with the aim of identifying groups of genes in biological networks collectively responsible for causing a disease through genetic heterogeneity. BioGranat-IG is used for finding the smallest subnetwork that represents all or most individuals. This plugin uses the additional structure found in biological networks to analyses sequence data for multiple individuals and suggest possible sources of genetic heterogeneity.
A tool to assist geneticist for diagnosis of pathogenic variants from whole exome sequencing data. We established an extensive variant annotation data source for the identification of pathogenic variants. A dashboard was deployed to aid a multi-step, hierarchical review process leading to final clinical decisions on genetic variant assessment. In addition, a central database was built to archive all of the genetic testing data, notes, and comments throughout the review process, variant validation data by Sanger sequencing as well as the final clinical reports for future reference. The entire workflow including data entry, distribution of work assignments, variant evaluation and review, selection of variants for validation, report generation, and communications between various personnel is integrated into a single data management platform.
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.
Facilitates the matchmaking process, allowing clinicians to coordinate detailed comparisons for phenotypically similar cases. GeneYenta is focused on phenotype annotation, with explicit limitations on highly confidential data that create barriers to participation. The procedure for matching of patient phenotypes uses an ontology-based semantic case matching algorithm with attribute weighting. GeneYenta has been designed with the aim of helping clinicians and researchers studying rare, undiagnosed genetic diseases to make connections. It provides a light weight solution to this problem by allowing researchers to share non-personally identifying phenotype patient information with other researchers worldwide.
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.
A phenotype-driven analysis of encrypted exome data to facilitate the widespread implementation of exome sequencing as a clinical genetic screening test. PhenoVar automatically prioritizes diagnoses for validation based on both the phenotypic and genomic information of a proband. It calculates a patient-specific diagnostic score for each OMIM entry with known molecular basis. One of PhenoVar’s major advantages it that it optimizes prioritization of possible diagnoses taking into consideration the patient’s exome data without requiring an increase in the bioinformatics human resources available in the clinical setting.
Provides a one-stop analysis of these data and a comprehensive, interactive and easy-to-understand report with many advanced visualization features. RETA is an R package that includes various in-depth quality control measures, integrative coverage examination and visualization, detection of runs-of-homozygosity and interactive, straightforward analysis results presentation. It facilitates clinicians and scientists alike to better analyze and interpret this type of sequencing data for disease diagnoses.
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.
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.
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