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.

Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis / Parca, L.; Truglio, M.; Biagini, T.; Castellana, S.; Petrizzelli, F.; Capocefalo, D.; Jordan, F.; Carella, M.; Mazza, T.. - In: GIGASCIENCE. - ISSN 2047-217X. - 9:10(2020). [10.1093/GIGASCIENCE/GIAA115]

Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis

Jordan F.;
2020

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.
Pyntacle: a parallel computing-enabled framework for large-scale network biology analysis / Parca, L.; Truglio, M.; Biagini, T.; Castellana, S.; Petrizzelli, F.; Capocefalo, D.; Jordan, F.; Carella, M.; Mazza, T.. - In: GIGASCIENCE. - ISSN 2047-217X. - 9:10(2020). [10.1093/GIGASCIENCE/GIAA115]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2915833
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