scholarly journals Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists

2015 ◽  
Vol 9 ◽  
pp. BBI.S12467 ◽  
Author(s):  
Xiaoxi Dong ◽  
Anatoly Yambartsev ◽  
Stephen A. Ramsey ◽  
Lina D Thomas ◽  
Natalia Shulzhenko ◽  
...  
Keyword(s):  
Big Data ◽  
Network Analysis ◽  
Regulatory Networks ◽  
Life Sciences ◽  
Massively Parallel ◽  
Biological Knowledge ◽  
Software Packages ◽  
Analysis Workflow ◽  
Functional Pathways ◽  
Cancer Network

Omics technologies enable unbiased investigation of biological systems through massively parallel sequence acquisition or molecular measurements, bringing the life sciences into the era of Big Data. A central challenge posed by such omics datasets is how to transform these data into biological knowledge, for example, how to use these data to answer questions such as: Which functional pathways are involved in cell differentiation? Which genes should we target to stop cancer? Network analysis is a powerful and general approach to solve this problem consisting of two fundamental stages, network reconstruction, and network interrogation. Here we provide an overview of network analysis including a step-by-step guide on how to perform and use this approach to investigate a biological question. In this guide, we also include the software packages that we and others employ for each of the steps of a network analysis workflow.

scholarly journals Reverse enGENEering of regulatory networks from Big Data: a guide for a biologist

2014 ◽  
Author(s):  
Andrey Morgun ◽  
Xiaoxi Dong ◽  
Anatoly Yambartsev ◽  
Stephen Ramsey ◽  
lina Thomas ◽  
...  
Keyword(s):  
Big Data ◽  
Network Analysis ◽  
Regulatory Networks ◽  
Life Sciences ◽  
Massively Parallel ◽  
Biological Knowledge ◽  
Software Packages ◽  
Analysis Workflow ◽  
Functional Pathways ◽  
Cancer Network

Omics technologies enable unbiased investigation of biological systems through massively parallel sequence acquisition or molecular measurements, bringing the life sciences into the era of Big Data. A central challenge posed by such omics datasets is how to transform this data into biological knowledge. For example, how to use this data to answer questions such as: which functional pathways are involved in cell differentiation? Which genes should we target to stop cancer? Network analysis is a powerful and general approach to solve this problem consisting of two fundamental stages, network reconstruction and network interrogation. Herein, we provide an overview of network analysis including a step by step guide on how to perform and use this approach to investigate a biological question. In this guide, we also include the software packages that we and others employ for each of the steps of a network analysis workflow.

scholarly journals Image Analysis Workflow for 2-D Electrophoresis Gels Based on ImageJ

2011 ◽  
Vol 4 ◽  
pp. PRI.S7971 ◽  
Author(s):  
Massimo Natale ◽  
Bernardetta Maresca ◽  
Paolo Abrescia ◽  
Enrico M. Bucci
Keyword(s):  
Life Sciences ◽  
National Institutes Of Health ◽  
Plasma Samples ◽  
Two Dimensional Electrophoresis ◽  
Standard Data ◽  
Software Packages ◽  
Commercial Package ◽  
Analysis Workflow ◽  
Analysis Application ◽  
Dimensional Electrophoresis

A number of commercial software packages are currently available to perform digital two-dimensional electrophoresis (2D-GE) gel analysis. However, both the high cost of the commercial packages and the unavailability of a standard data analysis workflow, have prompted several groups to develop freeware systems to perform certain steps of gel analysis. Unfortunately, to the best of our knowledge none of them offer a package that performs all the steps envisaged in a 2D-GE gel analysis. Here we describe an ImageJ-based procedure, able to manage all the steps of a 2D-GE gel analysis. ImageJ is a free available image processing and analysis application developed by National Institutes of Health (NIH) and widely used in different life sciences fields as medical imaging, microscopy, western blotting and PAGE. Nevertheless no one has yet developed a procedure enabled to compare spots on 2D-GE gels. We collected all used ImageJ tools in a plug-in that allows us to perform the whole 2D-GE analysis. To test it, we performed a set of 2D-GE experiments on plasma samples from 9 patients victims of acute myocardial infarction and 8 controls, and we compared the results obtained by our procedure to those obtained using a widely diffuse commercial package, finding similar performances.

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