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MoMo: Discovery of statistically significant post-translational modification motifs

Alice Cheng, Charles E. Grant, William S. Noble, View ORCID ProfileTimothy L. Bailey
doi: https://doi.org/10.1101/410050
Alice Cheng
1Department of Genome Sciences, University of Washington, Seattle, Washington, USA
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Charles E. Grant
1Department of Genome Sciences, University of Washington, Seattle, Washington, USA
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William S. Noble
1Department of Genome Sciences, University of Washington, Seattle, Washington, USA
2Department of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
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Timothy L. Bailey
3Department of Pharmacology, University of Nevada, Reno, Nevada, 89557, USA
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  • ORCID record for Timothy L. Bailey
  • For correspondence: timothybailey@unr.edu
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Abstract

Motivation Post-translational modifications (PTMs) of proteins are associated with many significant biological functions and can be identified in high throughput using tandem mass spectrometry. Many PTMs are associated with short sequence patterns called “motifs” that help localize the modifying enzyme. Accordingly, many algorithms have been designed to identify these motifs from mass spectrometry data. Accurate statistical confidence estimates for discovered motifs are critically important for proper interpretation and in the design of downstream experimental validation.

Results We describe a method for assigning statistical confidence estimates to PTM motifs, and we demonstrate that this method provides accurate p-values on both simulated and real data. Our methods are implemented in MoMo, a software tool for discovering motifs among sets of PTMs that we make available as a web server and as downloadable source code. MoMo reimplements the two most widely used PTM motif discovery algorithms—motif-x and MoDL—while offering many enhancements. Relative to motif-x, MoMo offers improved statistical confidence estimates and more accurate calculation of motif scores. The MoMo web server offers more proteome databases, more input formats, larger inputs and longer running times than the motif-x web server. Finally, our study demonstrates that the confidence estimates produced by motif-x are inaccurate. This inaccuracy stems in part from the common practice of drawing “background” peptides from an unshuffled proteome database. Our results thus suggest that many of the hundreds of papers that use motif-x to find motifs may be reporting results that lack statistical support.

Availability http://meme-suite.org

Contact timothybailey{at}unr.edu

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 06, 2018.
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MoMo: Discovery of statistically significant post-translational modification motifs
Alice Cheng, Charles E. Grant, William S. Noble, Timothy L. Bailey
bioRxiv 410050; doi: https://doi.org/10.1101/410050
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MoMo: Discovery of statistically significant post-translational modification motifs
Alice Cheng, Charles E. Grant, William S. Noble, Timothy L. Bailey
bioRxiv 410050; doi: https://doi.org/10.1101/410050

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