Luca Parca1, Mauro Truglio1, Tommaso Biagini1, Stefano Castellana1, Francesco Petrizzelli1,2, Daniele Capocefalo1, Ferenc Jordán3, Massimo Carella4, Tommaso Mazza1. 1. IRCCS Casa Sollievo della Sofferenza, Laboratory of Bioinformatics, Viale Cappuccini 1, 71013, San Giovanni Rotondo (FG), Italy. 2. Department of Experimental Medicine, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy. 3. Balaton Limnological Institute, Centre for Ecological Research Klebelsberg Kuno 3, 8237 Tihany, Hungary. 4. IRCCS Casa Sollievo della Sofferenza, Laboratory of Medical Genetics, Viale Padre Pio 7d, 71013, San Giovanni Rotondo (FG), Italy.
Abstract
BACKGROUND: Some natural systems are big in size, complex, and often characterized by convoluted mechanisms of interaction, such as epistasis, pleiotropy, and trophism, which cannot be immediately ascribed to individual natural events or biological entities but that are often derived from group effects. However, the determination of important groups of entities, such as genes or proteins, in complex systems is considered a computationally hard task. RESULTS: We present Pyntacle, a high-performance framework designed to exploit parallel computing and graph theory to efficiently identify critical groups in big networks and in scenarios that cannot be tackled with traditional network analysis approaches. CONCLUSIONS: We showcase potential applications of Pyntacle with transcriptomics and structural biology data, thereby highlighting the outstanding improvement in terms of computational resources over existing tools.
BACKGROUND: Some natural systems are big in size, complex, and often characterized by convoluted mechanisms of interaction, such as epistasis, pleiotropy, and trophism, which cannot be immediately ascribed to individual natural events or biological entities but that are often derived from group effects. However, the determination of important groups of entities, such as genes or proteins, in complex systems is considered a computationally hard task. RESULTS: We present Pyntacle, a high-performance framework designed to exploit parallel computing and graph theory to efficiently identify critical groups in big networks and in scenarios that cannot be tackled with traditional network analysis approaches. CONCLUSIONS: We showcase potential applications of Pyntacle with transcriptomics and structural biology data, thereby highlighting the outstanding improvement in terms of computational resources over existing tools.
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