BioNetApp: An interactive visual data analysis platform for molecular expressions
<h3>Motivation</h3><p dir="ltr">Systems biology faces two key challenges when dealing with large amounts of disparate data produced by different experiments: the integration of results across different experiments, and the extraction of meaningful information from the dat...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2019
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| _version_ | 1864513514000875520 |
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| author | Ali M. Roumani (18615124) |
| author2 | Amgad Madkour (6392687) Mourad Ouzzani (3618794) Thomas McGrew (6392690) Esraa Omran (6392693) Xiang Zhang (19800) |
| author2_role | author author author author author |
| author_facet | Ali M. Roumani (18615124) Amgad Madkour (6392687) Mourad Ouzzani (3618794) Thomas McGrew (6392690) Esraa Omran (6392693) Xiang Zhang (19800) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ali M. Roumani (18615124) Amgad Madkour (6392687) Mourad Ouzzani (3618794) Thomas McGrew (6392690) Esraa Omran (6392693) Xiang Zhang (19800) |
| dc.date.none.fl_str_mv | 2019-02-22T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0211277 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/BioNetApp_An_interactive_visual_data_analysis_platform_for_molecular_expressions/25904419 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Biochemistry and cell biology Data mining Data visualization Graphs Network analysis Software tools |
| dc.title.none.fl_str_mv | BioNetApp: An interactive visual data analysis platform for molecular expressions |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Motivation</h3><p dir="ltr">Systems biology faces two key challenges when dealing with large amounts of disparate data produced by different experiments: the integration of results across different experiments, and the extraction of meaningful information from the data produced by these experiments. An ongoing challenge is to provide better tools that can mine data patterns that could not have been discovered through simple visualization. Such mining capabilities also need to be coupled with intuitive visualization to portray those findings. We introduce a software toolbox entitled BioNetApp to mine these patterns and visualize them across all experiments.</p><h3>Results</h3><p dir="ltr">BioNetApp is an interactive visual data mining software for analyzing high-volume molecular expression data obtained from multiple ‘omics experiments. By integrating visualization, statistical methods, and data mining techniques, BioNetApp can perform interactive correlative and comparative analysis along time-course studies of molecular expression data. Correlation analysis provides several visualization features such as Kamada-Kawai, Fruchterman-Reingold Spring embedding network layouts, in addition to single circle, multiple circle and heatmap layouts, whereas comparative analysis presents expression-data distributions across samples, groups, and time points with boxplot display, outlier detection, and data curve fitting. BioNetApp also provides data clustering based on molecular concentrations using Self Organizing Maps (SOM), K-Means, K-Medoids, and Farthest First algorithms.</p><h3>Conclusion</h3><p dir="ltr">BioNetApp has been utilized in a metabolomics study to investigate the metabolite abundance changes in alcohol induced fatty liver, where pair-wise analyses of metabolome concentration revealed correlation networks and interesting patterns in the metabolomics dataset. This study case demonstrates the effectiveness of the BioNetApp software as an interactive visual analysis tool for molecular expression data in systems biology. The BioNetApp software is freely available under GNU GPL license and can be downloaded (including the case-study data and user-manual) at: <a href="https://doi.org/10.5281/zenodo.2563129" target="_blank">https://doi.org/10.5281/zenodo.2563129</a>.</p><h2>Other Information</h2><p dir="ltr">Published in: PLOS ONE<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1371/journal.pone.0211277" target="_blank">https://dx.doi.org/10.1371/journal.pone.0211277</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d6a60c83a0dcb9a47ddc54cc469990c1 |
| identifier_str_mv | 10.1371/journal.pone.0211277 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25904419 |
| publishDate | 2019 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | BioNetApp: An interactive visual data analysis platform for molecular expressionsAli M. Roumani (18615124)Amgad Madkour (6392687)Mourad Ouzzani (3618794)Thomas McGrew (6392690)Esraa Omran (6392693)Xiang Zhang (19800)Biological sciencesBiochemistry and cell biologyData miningData visualizationGraphsNetwork analysisSoftware tools<h3>Motivation</h3><p dir="ltr">Systems biology faces two key challenges when dealing with large amounts of disparate data produced by different experiments: the integration of results across different experiments, and the extraction of meaningful information from the data produced by these experiments. An ongoing challenge is to provide better tools that can mine data patterns that could not have been discovered through simple visualization. Such mining capabilities also need to be coupled with intuitive visualization to portray those findings. We introduce a software toolbox entitled BioNetApp to mine these patterns and visualize them across all experiments.</p><h3>Results</h3><p dir="ltr">BioNetApp is an interactive visual data mining software for analyzing high-volume molecular expression data obtained from multiple ‘omics experiments. By integrating visualization, statistical methods, and data mining techniques, BioNetApp can perform interactive correlative and comparative analysis along time-course studies of molecular expression data. Correlation analysis provides several visualization features such as Kamada-Kawai, Fruchterman-Reingold Spring embedding network layouts, in addition to single circle, multiple circle and heatmap layouts, whereas comparative analysis presents expression-data distributions across samples, groups, and time points with boxplot display, outlier detection, and data curve fitting. BioNetApp also provides data clustering based on molecular concentrations using Self Organizing Maps (SOM), K-Means, K-Medoids, and Farthest First algorithms.</p><h3>Conclusion</h3><p dir="ltr">BioNetApp has been utilized in a metabolomics study to investigate the metabolite abundance changes in alcohol induced fatty liver, where pair-wise analyses of metabolome concentration revealed correlation networks and interesting patterns in the metabolomics dataset. This study case demonstrates the effectiveness of the BioNetApp software as an interactive visual analysis tool for molecular expression data in systems biology. The BioNetApp software is freely available under GNU GPL license and can be downloaded (including the case-study data and user-manual) at: <a href="https://doi.org/10.5281/zenodo.2563129" target="_blank">https://doi.org/10.5281/zenodo.2563129</a>.</p><h2>Other Information</h2><p dir="ltr">Published in: PLOS ONE<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1371/journal.pone.0211277" target="_blank">https://dx.doi.org/10.1371/journal.pone.0211277</a></p>2019-02-22T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1371/journal.pone.0211277https://figshare.com/articles/journal_contribution/BioNetApp_An_interactive_visual_data_analysis_platform_for_molecular_expressions/25904419CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/259044192019-02-22T03:00:00Z |
| spellingShingle | BioNetApp: An interactive visual data analysis platform for molecular expressions Ali M. Roumani (18615124) Biological sciences Biochemistry and cell biology Data mining Data visualization Graphs Network analysis Software tools |
| status_str | publishedVersion |
| title | BioNetApp: An interactive visual data analysis platform for molecular expressions |
| title_full | BioNetApp: An interactive visual data analysis platform for molecular expressions |
| title_fullStr | BioNetApp: An interactive visual data analysis platform for molecular expressions |
| title_full_unstemmed | BioNetApp: An interactive visual data analysis platform for molecular expressions |
| title_short | BioNetApp: An interactive visual data analysis platform for molecular expressions |
| title_sort | BioNetApp: An interactive visual data analysis platform for molecular expressions |
| topic | Biological sciences Biochemistry and cell biology Data mining Data visualization Graphs Network analysis Software tools |