Abstract
BackgroundAnalyzing global experimental data can be tedious and time-consuming. Thus, helping biologists see results as quickly and easily as possible can facilitate biological research, and is the purpose of the software we describe.ResultsWe present BirdsEyeView, a software system for visualizing experimental transcriptomic data using different views that users can switch among and compare. BirdsEyeView graphically maps data to three views: Cellular Map (currently a plant cell), Pathway Tree with dynamic mapping, and Gene Ontology http://www.geneontology.org Biological Processes and Molecular Functions. By displaying color-coded values for transcript levels across different views, BirdsEyeView can assist users in developing hypotheses about their experiment results.ConclusionsBirdsEyeView is a software system available as a Java Webstart package for visualizing transcriptomic data in the context of different biological views to assist biologists in investigating experimental results. BirdsEyeView can be obtained from http://metnetdb.org/MetNet_BirdsEyeView.htm.
Highlights
Rapid technological innovation is enabling new biological approaches, and accelerating biologists’ ability to collect large scale transcriptomics, metabolomics and proteomics data
Extraction of data and hypotheses about that data has become key to biological research
Patterns of experimental input will be shown in different views so that users can view the distribution of highly accumulated RNAs across compartments, pathways, and Gene Ontology Processes
Summary
Rapid technological innovation is enabling new biological approaches, and accelerating biologists’ ability to collect large scale transcriptomics, metabolomics and proteomics data. Extraction of data and hypotheses about that data has become key to biological research. Analysis of global and massive datasets has led to a number of discoveries on gene function Because the time it takes to investigate massive data manually and extract useful biological knowledge is often prohibitive, functional genomics tools for data analysis and visualization are critical and many approaches have been or are being developed. Some approaches apply only to single experiments or limited data, whereas others can handle multiple experiments and large. Analyzing global experimental data can be tedious and time-consuming. Helping biologists see results as quickly and as possible can facilitate biological research, and is the purpose of the software we describe
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