A new method for 2D gel spot alignment: application to the analysis of large sample sets in clinical proteomics

<p>Abstract</p> <p>Background</p> <p>In current comparative proteomics studies, the large number of images generated by 2D gels is currently compared using spot matching algorithms. Unfortunately, differences in gel migration and sample variability make efficient spot alignment very difficult to obtain, and, as consequence most of the software alignments return noisy gel matching which needs to be manually adjusted by the user.</p> <p>Results</p> <p>We present Sili2DGel an algorithm for automatic spot alignment that uses data from recursive gel matching and returns meaningful Spot Alignment Positions (SAP) for a given set of gels. In the algorithm, the data are represented by a graph and SAP by specific subgraphs. The results are returned under various forms (clickable synthetic gel, text file, etc.). We have applied Sili2DGel to study the variability of the urinary proteome from 20 healthy subjects.</p> <p>Conclusion</p> <p>Sili2DGel performs noiseless automatic spot alignment for variability studies (as well as classical differential expression studies) of biological samples. It is very useful for typical clinical proteomic studies with large number of experiments.</p

TULIP 5

International audienceTulip is an information visualization framework dedicated to the analysis and visualization of relational data. Based on more than 16 years of research and development, Tulip is built on a suite of tools and techniques, that can be used to address a large variety of domain-specific problems. With \tulip, we aim to provide Python and/or C++ developers a complete library, supporting the design of interactive information visualization applications for relational data, that can be customized to address a wide range of visualization problems. In its current iteration, \tulip enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations. This development pipeline makes the framework efficient for creating research prototypes as well as developing end-user applications. The recent addition of a complete Python programming layer wraps up Tulip as an ideal tool for fast prototyping and treatment automation, allowing to focus on problem solving, and as a great system for teaching purposes at all education levels

Decoupling Source Trees into Build-Level Components

Reuse between software systems is often not optimal. An important reason is that while at the functional level well-known modularization principles are applied for structuring functionality in modules, this is not the case at the build level for structuring files in directories. This leads to a situation where files are entangled in directory hierarchies and build processes, making it hard to extract functionality and to make functionality suitable for reuse. Consequently, software may not come available for reuse at all, or only in rather large chunks of functionality, which may lead to extra software dependencies. In thi

Tulip III

International audienceAlthough this article presents a system and discusses its design, its content goes much further. In a sense, this paper is a position paper following 10 years of lessons learned working in graph visualization, developing new visualization techniques, and building systems for users. The strategy we have adopted is to develop, maintain, and improve the Tulip framework, aiming for an architecture with optimal data structure management from which target applications can be easily derived