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Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets

Hsiang Ho1, Tijana Milenković2, Vesna Memišević2, Jayavani Aruri3, Nataša Pržulj4 and Anand K Ganesan13*

  • * Corresponding author: Anand K Ganesan

  • † Equal contributors

Author Affiliations

1 Department of Biological Chemistry, University of California, Irvine, CA 92697-1700, USA

2 Department of Computer Science, University of California, Irvine, CA 92697-3435, USA

3 Department of Dermatology, University of California, Irvine, CA 92697-2400, USA

4 Department of Computing, Imperial College London SW7 2AZ, UK

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BMC Systems Biology 2010, 4:84  doi:10.1186/1752-0509-4-84

Published: 15 June 2010



RNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context of a recently completed RNAi screen to identify novel regulators of melanogenesis.


In this study, we utilize a PPI network topology-based approach to identify targets within our RNAi dataset that may be components of known melanogenesis regulatory pathways. Our computational approach identifies a set of screen targets that cluster topologically in a human PPI network with the known pigment regulator Endothelin receptor type B (EDNRB). Validation studies reveal that these genes impact pigment production and EDNRB signaling in pigmented melanoma cells (MNT-1) and normal melanocytes.


We present an approach that identifies novel components of well-characterized biological pathways from functional genomics datasets that could not have been identified by existing statistical and computational approaches.