We describe Bayesian Model Optimized Reference Correction (BaMORC), a software package that performs 13C chemical shifts reference correction for either assigned or unassigned peak lists derived from protein NMR spectra. BaMORC provides an intuitive command line interface that allows non-nuclear magnetic resonance (NMR) experts to detect and correct 13C chemical shift referencing errors of unassigned peak lists at the very beginning of NMR data analysis, further lowering the bar of expertise required for effective protein NMR analysis. Furthermore, BaMORC provides an application programming interface for integration into sophisticated protein NMR data analysis pipelines, both before and after the protein resonance assignment step.

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Published in Natural Product Communications, v. 14, issue 5, p. 1-7.

© The Author(s) 2019

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).

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Support for this research was provided by the National Science Foundation grant NSF 1419282 (Hunter N.B. Moseley) and National Institutes of Health grants NIH UL1TR001998-01 (Philip Kern) and NIH P30CA177558 (Mark Evers).

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Source code is available at https://github.com/MoseleyBioinformaticsLab/BaMORC. [The package has been submitted to CRAN and should be available from CRAN soon. We will add a sentence about its availability from CRAN and update installation instructions when the evaluation process is finished]. The code is published under a modified open source BSD-3 license. Academic researchers are free to use it without restriction, except for proper citation. This repository includes code for the BaMORC referencing correction pipeline. For the registration and grouping algorithm, refer to https://github.com/MoseleyBioinformaticsLab/ssc. For further information and assistance visit our laboratory website: http://bioinformatics.cesb.uky.edu.

Datasets are available at: https://doi.org/10.6084/m9.figshare.5270755.v1