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.
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.
Permits to manage and browse VCF files in an efficient and easy way. myVCF is simple to use, offers a graphical interface, allowing the use by non-experts on almost every platform, and export the data in multiple formats. It merges the functionality expressed in command-line tools, including multiple project management, exporting results and tables, with an easy and clear visualization through web pages in a simple browser.
Allows to identify variant sites and determine genotypes. CGES uses a two-stage voting scheme among four algorithm implementations and provides an approach to exome sequence variant calling. The CGES method accepts different variants coming from any variant-calling algorithm. The results of this tool supply a rationale for development of ensemble methodology in the analysis of next generation sequencing (NGS) data.
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.
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 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 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.
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.
Identify sex-specific marker sequences. SEXCMD allows users to (i) design sex-specific marker sequences, (ii) train using a known dataset, and (iii) make an optimal sex marker sequence selection. The pipeline can be run to genome and transcriptome sequencing without aligning next generation sequencing (NGS) reads onto a reference genome but by using tens of sex-specific marker sequences from syntenic regions of the sex chromosomes.
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.
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.
Emphasizes global gene level coverage and inter-individual variation in breadth of coverage for genes. WEScover permits users to retrieve breadth and depth of coverage across population scale whole exome sequencing (WES) datasets. It conducts a test statistic and p-value for a one-way analysis of variance to check differences between means of populations. This tool provides a list of genetic tests with indications allowing users to find optimal genetic tests per phenotype and/or genes of interest.
Provides rapid and accurate identification of causal mutations in rare diseases. TGex is a knowledge-driven Next Generation Sequencing (NGS) cloud-based analysis and interpretation solution based on the GeneCards Suite Knowledgebase. It uses VarElect, the NGS Phenotyper, to score and prioritize variant-genes based on disease/phenotype of interest. TGex provides analysts with all the tools required for filtering and ranking variants, together with the ability to quickly assess evidence for the association between candidate genes and relevant phenotypes, all in one easy-to-use, consolidated view.
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.
Provides filtering strategies for medical whole exome sequencing (WES). EVA integrates and stores annotated exome variation data and allows users to combine the main filters dealing with common variations, molecular types, inheritance mode and multiple samples. It offers browsing of annotated data and filtered results in various interactive tables, graphical visualizations and statistical charts.