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Nat Methods. 2018 Dec;15(12):1049-1052. doi: 10.1038/s41592-018-0218-5. Epub 2018 Nov 26.

Interpretation of an individual functional genomics experiment guided by massive public data.

Author information

1
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
2
Department of Computer Science, Princeton University, Princeton, NJ, USA.
3
School of Biological Sciences, Seoul National University, Seoul, Korea.
4
Flatiron Institute, Simons Foundation, New York, NY, USA.
5
Department of Neurology and Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
6
Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
7
Department of Neurology and Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY, USA. elena.zaslavsky@mssm.edu.
8
Department of Neurology and Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY, USA. stuart.sealfon@mssm.edu.

Abstract

A key unmet challenge in interpreting omics experiments is inferring biological meaning in the context of public functional genomics data. We developed a computational framework, Your Evidence Tailored Integration (YETI; http://yeti.princeton.edu/ ), which creates specialized functional interaction maps from large public datasets relevant to an individual omics experiment. Using this tailored integration, we predicted and experimentally confirmed an unexpected divergence in viral replication after seasonal or pandemic human influenza virus infection.

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PMID:
30478325
DOI:
10.1038/s41592-018-0218-5
[Indexed for MEDLINE]

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