Explorative visual analytics on interval-based genomic data and their metadata

Background: With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless "sense-making" of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines. Results: This paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE). This tool serves the needs of the GenoMetric Query Language, an innovative cloud-based system for computing complex queries over heterogeneous processed data. It can also be used starting from any text files in standard BED, BroadPeak, NarrowPeak, GTF, or general tab-delimited format, containing numerical features of genomic regions; metadata can be provided as text files in tab-delimited attribute-value format. GeMSE allows interactive analytics, consisting of on-the-fly cycling among steps of data exploration, analysis and visualization that help biologists and bioinformaticians in making sense of heterogeneous genomic datasets. By means of an explorative interaction support, users can trace past activities and quickly recover their results, seamlessly going backward and forward in the analysis steps and comparative visualizations of heatmaps. Conclusions: GeMSE effective application and practical usefulness is demonstrated through significant use cases of biological interest. GeMSE is available at http://www.bioinformatics.deib.polimi.it/GeMSE/ , and its source code is available at https://github.com/Genometric/GeMSEunder GPLv3 open-source license

Spinocerebellar ataxias Ataxias espinocerebelares

Spinocerebellar ataxias (SCAs) constitute a heterogeneous group of neurodegenerative diseases characterized by progressive cerebellar ataxia in association with some or all of the following conditions: ophthalmoplegia, pyramidal signs, movement disorders, pigmentary retinopathy, peripheral neuropathy, cognitive dysfunction and dementia. OBJECTIVE: To carry out a clinical and genetic review of the main types of SCA. METHOD: The review was based on a search of the PUBMED and OMIM databases. RESULTS: Thirty types of SCAs are currently known, and 16 genes associated with the disease have been identified. The most common types are SCA type 3, or Machado-Joseph disease, SCA type 10 and SCA types 7, 2, 1 and 6. SCAs are genotypically and phenotypically very heterogeneous. A clinical algorithm can be used to distinguish between the different types of SCAs. CONCLUSIONS: Detailed clinical neurological examination of SCA patients can be of great help when assessing them, and the information thus gained can be used in an algorithm to screen patients before molecular tests to investigate the correct etiology of the disease are requested.<br>As ataxias espinocerebelares (AECs) compreendem um grupo heterogeneo de enfermidades neurodegenerativas, que se caracterizam pela presença de ataxia cerebelar progressiva, associada de forma variada com oftalmoplegia, sinais piramidais, distúrbios do movimento, retinopatia pigmentar, neuropatia periférica, disfunção cognitiva e demência. OBJETIVO: Realizar uma revisão clínico-genética dos principais tipos de AECs. MÉTODO: A revisão foi realizada através da pesquisa pelo sistema do PUBMED e do OMIM. RESULTADOS: Na atualidade existem cerca de 30 tipos de AECs, com a descoberta de 16 genes. Os tipos mais comuns são a AEC tipo 3, ou doença de Machado-Joseph, a AEC tipo 10, e as AECs tipo 7, 2 1, e 6. As AECs apresentam grande heterogeneidade genotípica e fenotípica. Pode-se utilizar um algoritmo clínico para a pesquisa dos diferentes tipos de AECs. CONCLUSÕES: O exame clínico neurológico minucioso nos pacientes com AECs pode auxiliar sobremaneira na avaliação clínica destes pacientes, utilizando-se desta forma de um algoritmo, com os dados clínicos, que pode servir como um instrumento de triagem para a solicitação dos testes de genética molecular, para a correta investigação etiológica