Portable sequencing machines, such as the Oxford Nanopore MinION, are making the genome sequencing ubiquitous. This can be particularly interesting for identifying specific bacteria in air-filters or waters and for monitoring the microbioma composition in cultivated soils or in different animal samples, using a simple and portable approach. However, a main problem of these portable sequencing devices is that they stream huge amounts of data, which management can be actually challenging. Low-power System-on-Chip architectures represent a feasible way for designing a solution, based on the Fog computing paradigm, for processing locally the raw data, considering both the base calling step and the genome alignment part, and for sending only meaningful results over Internet. Cloud services can be then used to collect and integrate results in a Internet of Things framework, in order to trigger notifications or alarms and, in perspective, for more sophisticated applications based on statistical or machine learning approaches.

Low-power portable devices for metagenomics analysis: Fog computing makes bioinformatics ready for the Internet of Things

D'Agostino D.
2018

Abstract

Portable sequencing machines, such as the Oxford Nanopore MinION, are making the genome sequencing ubiquitous. This can be particularly interesting for identifying specific bacteria in air-filters or waters and for monitoring the microbioma composition in cultivated soils or in different animal samples, using a simple and portable approach. However, a main problem of these portable sequencing devices is that they stream huge amounts of data, which management can be actually challenging. Low-power System-on-Chip architectures represent a feasible way for designing a solution, based on the Fog computing paradigm, for processing locally the raw data, considering both the base calling step and the genome alignment part, and for sending only meaningful results over Internet. Cloud services can be then used to collect and integrate results in a Internet of Things framework, in order to trigger notifications or alarms and, in perspective, for more sophisticated applications based on statistical or machine learning approaches.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/1087400
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 17
  • ???jsp.display-item.citation.isi??? ND
social impact