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MOSAIC: A Joint Modeling Methodology for Combined Circadian and Non-Circadian Analysis of Multi-Omics Data

Hannah De los Santos, Kristin P. Bennett, Jennifer M. Hurley
doi: https://doi.org/10.1101/2020.04.27.064147
Hannah De los Santos
1Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, U.S.A.
2Institute for Data Exploration and Applications, Rensselaer Polytechnic Institute, Troy, NY 12180, U.S.A.
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Kristin P. Bennett
1Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, U.S.A.
2Institute for Data Exploration and Applications, Rensselaer Polytechnic Institute, Troy, NY 12180, U.S.A.
3Department of Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, U.S.A.
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Jennifer M. Hurley
4Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, U.S.A.
5Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, U.S.A.
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  • For correspondence: hurlej2@rpi.edu
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Abstract

Motivation Circadian rhythms are approximately 24 hour endogenous cycles that control many biological functions. To identify these rhythms, biological samples are taken over circadian time and analyzed using a single omics type, such as transcriptomics or proteomics. By comparing data from these single omics approaches, it has been shown that transcriptional rhythms are not necessarily conserved at the protein level, implying extensive circadian post-transcriptional regulation. However, as proteomics methods are known to be noisier than transcriptomic methods, this suggests that previously identified arrhythmic proteins with rhythmic transcripts could have been missed due to noise and may not be due to post-transcriptional regulation.

Results To determine if one can use information from less-noisy transcriptomic data to inform rhythms in more-noisy proteomic data, and thus more accurately identify rhythms in the proteome, we have created the MOSAIC (Multi-Omics Selection with Amplitude Independent Criteria) application. MOSAIC combines model selection and joint modeling of multiple omics types to recover significant circadian and non-circadian trends. Using both synthetic data and proteomic data from Neurospora crassa, we showed that MOSAIC accurately recovers circadian rhythms at higher rates in not only the proteome but the transcriptome as well, outperforming existing methods for rhythm identification. In addition, by quantifying non-circadian trends in addition to circadian trends in data, our methodology allowed for the recognition of the diversity of circadian regulation as compared to non-circadian regulation.

Availability MOSAIC’s full interface is available at https://github.com/delosh653/MOSAIC.

Contact hurlej2{at}rpi.edu

Supplementary information Supplementary data are available.at Bioinformatics online.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/delosh653/MOSAIC

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted April 29, 2020.
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MOSAIC: A Joint Modeling Methodology for Combined Circadian and Non-Circadian Analysis of Multi-Omics Data
Hannah De los Santos, Kristin P. Bennett, Jennifer M. Hurley
bioRxiv 2020.04.27.064147; doi: https://doi.org/10.1101/2020.04.27.064147
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MOSAIC: A Joint Modeling Methodology for Combined Circadian and Non-Circadian Analysis of Multi-Omics Data
Hannah De los Santos, Kristin P. Bennett, Jennifer M. Hurley
bioRxiv 2020.04.27.064147; doi: https://doi.org/10.1101/2020.04.27.064147

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