Published May 24, 2017 | Version Supplemental Material + Accepted Version
Journal Article Open

PROBer Provides a General Toolkit for Analyzing Sequencing-Based Toeprinting Assays

  • 1. ROR icon University of California, Berkeley
  • 2. ROR icon University of California, Davis
  • 3. ROR icon California Institute of Technology

Abstract

A number of sequencing-based transcriptase drop-off assays have recently been developed to probe post-transcriptional dynamics of RNA-protein interaction, RNA structure, and RNA modification. Although these assays survey a diverse set of epitranscriptomic marks, we use the term toeprinting assays since they share methodological similarities. Their interpretation is predicated on addressing a similar computational challenge: how to learn isoform-specific chemical modification profiles in the face of complex read multi-mapping. We introduce PROBer, a statistical model and associated software, that addresses this challenge for the analysis of toeprinting assays. PROBer takes sequencing data as input and outputs estimated transcript abundances and isoform-specific modification profiles. Results on both simulated and biological data demonstrate that PROBer significantly outperforms individual methods tailored for specific toeprinting assays. Since the space of toeprinting assays is ever expanding and these assays are likely to be performed and analyzed together, we believe PROBer's unified data analysis solution will be valuable to the RNA community.

Additional Information

© 2017 Elsevier Inc. Received 6 July 2016, Revised 19 December 2016, Accepted 13 April 2017, Available online 10 May 2017. We thank Yiliang Ding, Yin Tang, Joel McManus, and Thomas Carlile for discussions and clarifications on the StructureFold, Mod-seeker, and Pseudo-seq methods. We thank Yeon Lee, Julian König, Eric Van Nostrand, Gabriel Pratt, and Gene Yeo for discussions on the iCLIP and eCLIP protocols. This work is supported by NIH grants R01 HG006129 to L.P. and R00 HG006860 to S.A., and by the Center for RNA Systems Biology at UC Berkeley (NIH P50GM102706 grant) to B.L. A.T. was partially supported by NIH Molecular Biophysics Training grant (NIH GM08295).

Attached Files

Accepted Version - nihms875397.pdf

Supplemental Material - 1-s2.0-S2405471217301394-mmc1.pdf

Supplemental Material - 1-s2.0-S2405471217301394-mmc2.pdf

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Additional details

Identifiers

PMCID
PMC5758053
Eprint ID
77353
DOI
10.1016/j.cels.2017.04.007
Resolver ID
CaltechAUTHORS:20170510-142406445

Funding

NIH
R01 HG006129
NIH
R00 HG006860
NIH
P50GM102706
NIH Predoctoral Fellowship
GM08295

Dates

Created
2017-05-12
Created from EPrint's datestamp field
Updated
2022-03-28
Created from EPrint's last_modified field