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

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 effe...

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Detalles Bibliográficos
Autores principales: Parca, Luca, Truglio, Mauro, Biagini, Tommaso, Castellana, Stefano, Petrizzelli, Francesco, Capocefalo, Daniele, Jordán, Ferenc, Carella, Massimo, Mazza, Tommaso
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7576925/
https://www.ncbi.nlm.nih.gov/pubmed/33084878
http://dx.doi.org/10.1093/gigascience/giaa115
Descripción
Sumario: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.