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.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
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Acknowledgements
The raw results of the sample used in this work, related to one child with ASD diagnosis, were donated to BiONuMeRi by parents who had spontaneously acquired 23andMe kit for exome analysis. Moreover, the authors wish to thank Dr. Guanglan Zhang for her helpful contribution.
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MP: designed the tool, analysed data and wrote the manuscript. GF: designed the tool, analysed and provided data. GR: gave biological knowledge and wrote the manuscript. BT: gave biological knowledge and wrote the manuscript. MF: gave useful insights and wrote the manuscript. MR: supervised the whole project and drafted the manuscript. FP: supervised the whole project and drafted the manuscript.
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Pennisi, M. et al. (2018). BIOESOnet: A Tool for the Generation of Personalized Human Metabolic Pathways from 23andMe Exome Data. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_42
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DOI: https://doi.org/10.1007/978-3-319-95933-7_42
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