BIOESOnet: A Tool for the Generation of Personalized Human Metabolic Pathways from 23andMe Exome Data

Marzio Pennisi, Gabriele Forzano, Giulia Russo, Barbara Tomasello, Marco Favetta, Marcella Renis, Francesco Pappalardo

Risultato della ricerca: Capitolo in libro/report/atti di convegnoContributo a conferenzapeer review

Abstract

The lowering of costs of whole exome sequencing (WES) services registered in the last two years has greatly increased the demand for managing different metabolic diseases, including autism spectrum disorders (ASD). WES allows the detection of a large part of exome single nucleotide polymorphisms (SNPs), whose expression can be in some cases modulated by epigenetics, life style and microbioma changes. However, such raw data usually needs to be manipulated in order to allow useful interpretation and analysis. We present BIOESOnet, a tool for the filtering and visualization of exome 23andMe raw data into a customized methylation pathway. The tool, available at: http://www.bionumeri.org/joomla/restricted-area/onecarbon-tool, enables a fast and extensive overview of possible mutations inside an extended metabolic pathway.

Lingua originaleInglese
Titolo della pubblicazione ospiteIntelligent Computing Theories and Application - 14th International Conference, ICIC 2018, Proceedings
EditorKang-Hyun Jo, De-Shuang Huang, Xiao-Long Zhang
EditoreSpringer Verlag
Pagine345-352
Numero di pagine8
ISBN (stampa)9783319959320
DOI
Stato di pubblicazionePubblicato - 2018
Pubblicato esternamente
Evento14th International Conference on Intelligent Computing, ICIC 2018 - Wuhan, China
Durata: 15 ago 201818 ago 2018

Serie di pubblicazioni

NomeLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10955 LNCS
ISSN (stampa)0302-9743
ISSN (elettronico)1611-3349

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???event.eventtypes.event.conference???14th International Conference on Intelligent Computing, ICIC 2018
Paese/TerritorioChina
CittàWuhan
Periodo15/08/1818/08/18

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