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Genome Res. 2015 Feb;25(2):257-67. doi: 10.1101/gr.178194.114. Epub 2014 Nov 5.

Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.

Author information

1
Department of Systems Biology, Center for Computational Biology and Bioinformatics, Department of Biomedical Informatics, Columbia University, New York, New York 10032, USA; Texas Children's Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA;
2
Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;
3
MOE Key Laboratory of Bioinformatics, Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China;
4
Department of Systems Biology, Center for Computational Biology and Bioinformatics, Department of Biomedical Informatics, Columbia University, New York, New York 10032, USA;
5
Institute for Cancer Genetics, Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York 10032, USA;
6
Department of Systems Biology.
7
Laboratory of RNA Molecular Biology, Rockefeller University, New York, New York 10065, USA;
8
Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA;
9
Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA; califano@c2b2.columbia.edu sumazin@bcm.edu.
10
Department of Systems Biology, Center for Computational Biology and Bioinformatics, Department of Biomedical Informatics, Columbia University, New York, New York 10032, USA; Institute for Cancer Genetics, Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York 10032, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032, USA califano@c2b2.columbia.edu sumazin@bcm.edu.
11
Texas Children's Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA;

Abstract

We introduce a method for simultaneous prediction of microRNA-target interactions and their mediated competitive endogenous RNA (ceRNA) interactions. Using high-throughput validation assays in breast cancer cell lines, we show that our integrative approach significantly improves on microRNA-target prediction accuracy as assessed by both mRNA and protein level measurements. Our biochemical assays support nearly 500 microRNA-target interactions with evidence for regulation in breast cancer tumors. Moreover, these assays constitute the most extensive validation platform for computationally inferred networks of microRNA-target interactions in breast cancer tumors, providing a useful benchmark to ascertain future improvements.

PMID:
25378249
PMCID:
PMC4315299
DOI:
10.1101/gr.178194.114
[Indexed for MEDLINE]
Free PMC Article

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