Pub Date : 2013-10-01DOI: 10.1109/BioVis.2013.6664342
R. V. Brakel, M. Fiers, Christof Francke, M. A. Westenberg, H. V. D. Wetering
We propose a visual analysis approach that employs a matrix-based visualization technique to explore relations between annotation terms in biological data sets. Our flexible framework provides various ways to form combinations of data elements, which results in a co-occurrence matrix. Each cell in this matrix stores a list of items associated with the combination of the corresponding row and column element. By re-arranging the rows and columns of this matrix, and color-coding the cell contents, patterns become visible. Our prototype tool COMBat allows users to construct a new matrix on the fly by selecting subsets of items of interest, or filtering out uninteresting ones, and it provides various additional interaction techniques. We illustrate our approach with a few case studies concerning the identification of functional links between the presence of particular genes or genomic sequences and particular cellular processes.
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Pub Date : 2013-10-01DOI: 10.1109/BioVis.2013.6664350
Jonathan Dubois, A. Ghozlane, P. Thébault, I. Dutour, Romain Bourqui
The central dogma in molecular biology postulated that `DNA makes RNA makes protein', however this dogma has been recently extended to integrate new biological activities involving small bacterial noncoding RNAs, called sRNAs. Accordingly, increasing attention has been given to these molecules over the last decade and related experimental works have shown a wide range of functional activities for these molecules. In this paper, we present rNAV (for rna NAVigator), a new tool for the visual exploration and analysis of bacterial sRNA-mediated regulatory networks. rNAV has been designed to help bioinformaticians and biologists to identify, from lists of thousands of predictions, pertinent and reasonable sRNA target candidates for carrying out experimental validations. We propose a list of dedicated algorithms and interaction tools that facilitate the exploration of such networks. These algorithms can be gathered into pipelines which can then be saved and reused over several sessions. To support exploration awareness, rNAV also provides an exploration tree view that allows to navigate through the steps of the analysis but also to select the sub-networks to visualize and compare. These comparisons are facilitated by the integration of multiple and fully linked views. We demonstrate the usefulness of our approach by a case study on Escherichia coli bacteria performed by domain experts.
{"title":"Genome-wide detection of sRNA targets with rNAV","authors":"Jonathan Dubois, A. Ghozlane, P. Thébault, I. Dutour, Romain Bourqui","doi":"10.1109/BioVis.2013.6664350","DOIUrl":"https://doi.org/10.1109/BioVis.2013.6664350","url":null,"abstract":"The central dogma in molecular biology postulated that `DNA makes RNA makes protein', however this dogma has been recently extended to integrate new biological activities involving small bacterial noncoding RNAs, called sRNAs. Accordingly, increasing attention has been given to these molecules over the last decade and related experimental works have shown a wide range of functional activities for these molecules. In this paper, we present rNAV (for rna NAVigator), a new tool for the visual exploration and analysis of bacterial sRNA-mediated regulatory networks. rNAV has been designed to help bioinformaticians and biologists to identify, from lists of thousands of predictions, pertinent and reasonable sRNA target candidates for carrying out experimental validations. We propose a list of dedicated algorithms and interaction tools that facilitate the exploration of such networks. These algorithms can be gathered into pipelines which can then be saved and reused over several sessions. To support exploration awareness, rNAV also provides an exploration tree view that allows to navigate through the steps of the analysis but also to select the sub-networks to visualize and compare. These comparisons are facilitated by the integration of multiple and fully linked views. We demonstrate the usefulness of our approach by a case study on Escherichia coli bacteria performed by domain experts.","PeriodicalId":356842,"journal":{"name":"2013 IEEE Symposium on Biological Data Visualization (BioVis)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131962413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-10-01DOI: 10.1109/BioVis.2013.6664348
Christian Nowke, Maximilian Schmidt, Sacha Jennifer van Albada, Jochen M. Eppler, Rembrandt Bakker, Markus Diesrnann, B. Hentschel, T. Kuhlen
The aim of computational neuroscience is to gain insight into the dynamics and functionality of the nervous system by means of modeling and simulation. Current research leverages the power of High Performance Computing facilities to enable multi-scale simulations capturing both low-level neural activity and large-scalce interactions between brain regions. In this paper, we describe an interactive analysis tool that enables neuroscientists to explore data from such simulations. One of the driving challenges behind this work is the integration of macroscopic data at the level of brain regions with microscopic simulation results, such as the activity of individual neurons. While researchers validate their findings mainly by visualizing these data in a non-interactive fashion, state-of-the-art visualizations, tailored to the scientific question yet sufficiently general to accommodate different types of models, enable such analyses to be performed more efficiently. This work describes several visualization designs, conceived in close collaboration with domain experts, for the analysis of network models. We primarily focus on the exploration of neural activity data, inspecting connectivity of brain regions and populations, and visualizing activity flux across regions. We demonstrate the effectiveness of our approach in a case study conducted with domain experts.
{"title":"VisNEST — Interactive analysis of neural activity data","authors":"Christian Nowke, Maximilian Schmidt, Sacha Jennifer van Albada, Jochen M. Eppler, Rembrandt Bakker, Markus Diesrnann, B. Hentschel, T. Kuhlen","doi":"10.1109/BioVis.2013.6664348","DOIUrl":"https://doi.org/10.1109/BioVis.2013.6664348","url":null,"abstract":"The aim of computational neuroscience is to gain insight into the dynamics and functionality of the nervous system by means of modeling and simulation. Current research leverages the power of High Performance Computing facilities to enable multi-scale simulations capturing both low-level neural activity and large-scalce interactions between brain regions. In this paper, we describe an interactive analysis tool that enables neuroscientists to explore data from such simulations. One of the driving challenges behind this work is the integration of macroscopic data at the level of brain regions with microscopic simulation results, such as the activity of individual neurons. While researchers validate their findings mainly by visualizing these data in a non-interactive fashion, state-of-the-art visualizations, tailored to the scientific question yet sufficiently general to accommodate different types of models, enable such analyses to be performed more efficiently. This work describes several visualization designs, conceived in close collaboration with domain experts, for the analysis of network models. We primarily focus on the exploration of neural activity data, inspecting connectivity of brain regions and populations, and visualizing activity flux across regions. We demonstrate the effectiveness of our approach in a case study conducted with domain experts.","PeriodicalId":356842,"journal":{"name":"2013 IEEE Symposium on Biological Data Visualization (BioVis)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131064582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-10-01DOI: 10.1109/BioVis.2013.6664352
Keqin Wu, Jing Chen, W. Pruett, R. Hester
We present HumMod Browser, a multi-scale exploratory visualization tool that allows physiologists to explore human physiology simulation data with more than 6000 attributes. We first present a tag cloud technique to reveal the significance of time-varying attributes and then study how a chain of tag clouds can form an exploratory visuailzation that assist multiple dataset comparison and query. One purpose is to reduce the high cognitive workload of understanding complex interactions within the large attribute space. The HumMod Browser produced can give physiologists flexible control over the visualization displayed for quick understanding of complicated simulation results. The visualization is constructed through the metaphorical bubble interface to allow dynamic view controls and the data relationships and context informaiton unfold as physiologists querying groups of connected bubbles within the hierarchical or causal relationships. HumMod Browser contributions to the interaction design and provides multi-scale coordinated interactive exploration for a new type of physiological modeling data. Two case studies have been reported with real datasets containing more than 6000 physiology attributes, which provide supportive evidence on the usefulness of HumMod Browser in supporting effective large-attribute-space exploration.
{"title":"Hummod browser: An exploratory visualization tool for the analysis of whole-body physiology simulation data","authors":"Keqin Wu, Jing Chen, W. Pruett, R. Hester","doi":"10.1109/BioVis.2013.6664352","DOIUrl":"https://doi.org/10.1109/BioVis.2013.6664352","url":null,"abstract":"We present HumMod Browser, a multi-scale exploratory visualization tool that allows physiologists to explore human physiology simulation data with more than 6000 attributes. We first present a tag cloud technique to reveal the significance of time-varying attributes and then study how a chain of tag clouds can form an exploratory visuailzation that assist multiple dataset comparison and query. One purpose is to reduce the high cognitive workload of understanding complex interactions within the large attribute space. The HumMod Browser produced can give physiologists flexible control over the visualization displayed for quick understanding of complicated simulation results. The visualization is constructed through the metaphorical bubble interface to allow dynamic view controls and the data relationships and context informaiton unfold as physiologists querying groups of connected bubbles within the hierarchical or causal relationships. HumMod Browser contributions to the interaction design and provides multi-scale coordinated interactive exploration for a new type of physiological modeling data. Two case studies have been reported with real datasets containing more than 6000 physiology attributes, which provide supportive evidence on the usefulness of HumMod Browser in supporting effective large-attribute-space exploration.","PeriodicalId":356842,"journal":{"name":"2013 IEEE Symposium on Biological Data Visualization (BioVis)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128530356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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