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Related Experiment Video

Updated: May 7, 2026

Pattern-based Search of Epigenomic Data Using GeNemo
06:38

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

Pattern search in BioPAX models.

Özgün Babur1, Bülent Arman Aksoy, Igor Rodchenkov

  • 1Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA, Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA and Banting and Best Department of Medical Research, The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.

Bioinformatics (Oxford, England)
|September 19, 2013
PubMed
Summary
This summary is machine-generated.

A new framework enables efficient searching of complex biological pathway data. This tool identifies numerous signaling relationships in public databases not found in current signaling databases, improving biological network analysis.

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Area of Science:

  • Systems Biology
  • Bioinformatics

Background:

  • Biological Pathway Exchange (BioPAX) is a standard for modeling complex cellular processes like metabolism and signaling.
  • Searching intricate BioPAX models for specific subnetworks is challenging due to their complexity.

Purpose of the Study:

  • To develop an open-source, extensible framework for defining and searching graph patterns within BioPAX models.
  • To facilitate the discovery of specific biological pathways and relationships.

Main Methods:

  • Developed a Java-based software framework for BioPAX pattern searching.
  • Applied a sample pattern to identify directed signaling relations between proteins.
  • Searched the Pathway Commons database and compared results with SPIKE and SignaLink databases.

Main Results:

  • The framework successfully identified directed signaling relations between proteins using a defined graph pattern.
  • Pattern searching in public pathway data revealed a significant number of signaling relations absent in specialized signaling databases (SPIKE, SignaLink).

Conclusions:

  • The developed framework provides an effective method for querying complex BioPAX data.
  • This approach enhances the discovery of biological signaling pathways by leveraging public data resources.