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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 “sens...

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Autores principales: Jalili, Vahid, Matteucci, Matteo, Masseroli, Marco, Ceri, Stefano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5715631/
https://www.ncbi.nlm.nih.gov/pubmed/29202689
http://dx.doi.org/10.1186/s12859-017-1945-9
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author Jalili, Vahid
Matteucci, Matteo
Masseroli, Marco
Ceri, Stefano
author_facet Jalili, Vahid
Matteucci, Matteo
Masseroli, Marco
Ceri, Stefano
author_sort Jalili, Vahid
collection PubMed
description 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/GeMSE under GPLv3 open-source license.
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spelling pubmed-57156312017-12-08 Explorative visual analytics on interval-based genomic data and their metadata Jalili, Vahid Matteucci, Matteo Masseroli, Marco Ceri, Stefano BMC Bioinformatics Software 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/GeMSE under GPLv3 open-source license. BioMed Central 2017-12-04 /pmc/articles/PMC5715631/ /pubmed/29202689 http://dx.doi.org/10.1186/s12859-017-1945-9 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Jalili, Vahid
Matteucci, Matteo
Masseroli, Marco
Ceri, Stefano
Explorative visual analytics on interval-based genomic data and their metadata
title Explorative visual analytics on interval-based genomic data and their metadata
title_full Explorative visual analytics on interval-based genomic data and their metadata
title_fullStr Explorative visual analytics on interval-based genomic data and their metadata
title_full_unstemmed Explorative visual analytics on interval-based genomic data and their metadata
title_short Explorative visual analytics on interval-based genomic data and their metadata
title_sort explorative visual analytics on interval-based genomic data and their metadata
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5715631/
https://www.ncbi.nlm.nih.gov/pubmed/29202689
http://dx.doi.org/10.1186/s12859-017-1945-9
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