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TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

View ORCID ProfileAshley Mae Conard, Nathaniel Goodman, Yanhui Hu, Norbert Perrimon, Ritambhara Singh, Charles Lawrence, Erica Larschan
doi: https://doi.org/10.1101/2020.09.14.296418
Ashley Mae Conard
1Center for Computational and Molecular Biology, Brown University, Providence, RI, 02912
2Computer Science Department, Brown University, Providence, RI, 02912
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  • ORCID record for Ashley Mae Conard
  • For correspondence: ashley_conard@brown.edu erica_larschan@brown.edu
Nathaniel Goodman
2Computer Science Department, Brown University, Providence, RI, 02912
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Yanhui Hu
3Department of Genetics, Harvard Medical School, Boston, MA 02115
5Director of Bioinformatics DRSC/TRiP Functional Genomics Resources, Harvard Medical School, Boston, MA 02115
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Norbert Perrimon
3Department of Genetics, Harvard Medical School, Boston, MA 02115
4Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115
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Ritambhara Singh
1Center for Computational and Molecular Biology, Brown University, Providence, RI, 02912
2Computer Science Department, Brown University, Providence, RI, 02912
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Charles Lawrence
1Center for Computational and Molecular Biology, Brown University, Providence, RI, 02912
6Applied Math Department, Brown University, Providence, RI, 02912
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  • For correspondence: ashley_conard@brown.edu erica_larschan@brown.edu
Erica Larschan
1Center for Computational and Molecular Biology, Brown University, Providence, RI, 02912
7Department of Molecular Biology, Cellular Biology and Biochemistry, Brown University, Providence, RI, 02912
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  • For correspondence: ashley_conard@brown.edu erica_larschan@brown.edu
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Summary

Uncovering how transcription factors (TFs) regulate their targets at the DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) in normal and diseased states. RNA-seq has become a standard method to measure gene regulation using an established set of analysis steps. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods which integrate ordered RNA-seq data with transcription factor binding data are urgently needed. Here, we present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to predict causal regulatory mechanism networks between TFs from time series multi-omics data. We used TIMEOR to identify a new link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/ashleymaeconard/TIMEOR

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 September 15, 2020.
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TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data
Ashley Mae Conard, Nathaniel Goodman, Yanhui Hu, Norbert Perrimon, Ritambhara Singh, Charles Lawrence, Erica Larschan
bioRxiv 2020.09.14.296418; doi: https://doi.org/10.1101/2020.09.14.296418
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TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data
Ashley Mae Conard, Nathaniel Goodman, Yanhui Hu, Norbert Perrimon, Ritambhara Singh, Charles Lawrence, Erica Larschan
bioRxiv 2020.09.14.296418; doi: https://doi.org/10.1101/2020.09.14.296418

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