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 PappalardoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)


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:, enables a fast and extensive overview of possible mutations inside an extended metabolic pathway.


Exome Scalable Vector Graphics (SVG) Pathway Whole Exome Sequencing (WES) Autism Spectrum Disorders (ASD) Single Nucleotide Polymorphism (SNP) 



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.

Authors’ Contribution.

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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Marzio Pennisi
    • 1
  • Gabriele Forzano
    • 2
  • Giulia Russo
    • 3
  • Barbara Tomasello
    • 4
  • Marco Favetta
    • 2
  • Marcella Renis
    • 4
  • Francesco Pappalardo
    • 4
    Email author
  1. 1.Department of Mathematics and Computer ScienceUniversity of CataniaCataniaItaly
  2. 2.BiONuMeRi ONLUSCataniaItaly
  3. 3.Department of Biomedicine and Biotechnological ScienceUniversity of CataniaCataniaItaly
  4. 4.Department of Drug SciencesUniversity of CataniaCataniaItaly

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