TY - JOUR
T1 - AUREA
T2 - An open-source software system for accurate and user-friendly identification of relative expression molecular signatures
AU - Earls, John C.
AU - Eddy, James A.
AU - Funk, Cory C.
AU - Ko, Younhee
AU - Magis, Andrew T.
AU - Price, Nathan D.
N1 - Funding Information:
We would like to acknowledge Nat Goodman and Victor Cassen for their enormous assistance with the Babel client, and Daniel Q. Naiman, Xue Lin, and Bahman Afsari for all of their hard work developing the RXA R package, which simplified our development greatly (large portions of it are used in the TSP and TST modules) and Lita Earls for creating the AUREA graphic. We would also like to thank Julie Bletz for a critical reading of this manuscript. This work was funded by an NIH-NCI Howard Temin Pathway to Independence Award in Cancer Research, the Grand Duchy of Luxembourg Systems Medicine Consortium, and the Camille-Dreyfus Teacher Scholar Program (NDP).
PY - 2013/3/5
Y1 - 2013/3/5
N2 - Background: Public databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collectively, these data can be analyzed for such purposes as disease diagnosis or phenotype classification. One family of algorithms that has proven useful for disease classification is based on relative expression analysis and includes the Top-Scoring Pair (TSP), k-Top-Scoring Pairs (k-TSP), Top-Scoring Triplet (TST) and Differential Rank Conservation (DIRAC) algorithms. These relative expression analysis algorithms hold significant advantages for identifying interpretable molecular signatures for disease classification, and have been implemented previously on a variety of computational platforms with varying degrees of usability. To increase the user-base and maximize the utility of these methods, we developed the program AUREA (Adaptive Unified Relative Expression Analyzer)-a cross-platform tool that has a consistent application programming interface (API), an easy-to-use graphical user interface (GUI), fast running times and automated parameter discovery.Results: Herein, we describe AUREA, an efficient, cohesive, and user-friendly open-source software system that comprises a suite of methods for relative expression analysis. AUREA incorporates existing methods, while extending their capabilities and bringing uniformity to their interfaces. We demonstrate that combining these algorithms and adaptively tuning parameters on the training sets makes these algorithms more consistent in their performance and demonstrate the effectiveness of our adaptive parameter tuner by comparing accuracy across diverse datasets.Conclusions: We have integrated several relative expression analysis algorithms and provided a unified interface for their implementation while making data acquisition, parameter fixing, data merging, and results analysis 'point-and-click' simple. The unified interface and the adaptive parameter tuning of AUREA provide an effective framework in which to investigate the massive amounts of publically available data by both 'in silico' and 'bench' scientists. AUREA can be found at http://price.systemsbiology.net/AUREA/.
AB - Background: Public databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collectively, these data can be analyzed for such purposes as disease diagnosis or phenotype classification. One family of algorithms that has proven useful for disease classification is based on relative expression analysis and includes the Top-Scoring Pair (TSP), k-Top-Scoring Pairs (k-TSP), Top-Scoring Triplet (TST) and Differential Rank Conservation (DIRAC) algorithms. These relative expression analysis algorithms hold significant advantages for identifying interpretable molecular signatures for disease classification, and have been implemented previously on a variety of computational platforms with varying degrees of usability. To increase the user-base and maximize the utility of these methods, we developed the program AUREA (Adaptive Unified Relative Expression Analyzer)-a cross-platform tool that has a consistent application programming interface (API), an easy-to-use graphical user interface (GUI), fast running times and automated parameter discovery.Results: Herein, we describe AUREA, an efficient, cohesive, and user-friendly open-source software system that comprises a suite of methods for relative expression analysis. AUREA incorporates existing methods, while extending their capabilities and bringing uniformity to their interfaces. We demonstrate that combining these algorithms and adaptively tuning parameters on the training sets makes these algorithms more consistent in their performance and demonstrate the effectiveness of our adaptive parameter tuner by comparing accuracy across diverse datasets.Conclusions: We have integrated several relative expression analysis algorithms and provided a unified interface for their implementation while making data acquisition, parameter fixing, data merging, and results analysis 'point-and-click' simple. The unified interface and the adaptive parameter tuning of AUREA provide an effective framework in which to investigate the massive amounts of publically available data by both 'in silico' and 'bench' scientists. AUREA can be found at http://price.systemsbiology.net/AUREA/.
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U2 - 10.1186/1471-2105-14-78
DO - 10.1186/1471-2105-14-78
M3 - Article
C2 - 23496976
AN - SCOPUS:84874453603
SN - 1471-2105
VL - 14
JO - BMC Bioinformatics
JF - BMC Bioinformatics
M1 - 78
ER -