BamoRC: A software package for accurate and robust 13C reference correction of protein NMR spectra

Xi Chen; Andrey Smelter; Hunter N.B. Moseley

doi:10.1177/1934578X19849142

BamoRC: A software package for accurate and robust ¹³C reference correction of protein NMR spectra

Xi Chen, Andrey Smelter, Hunter N.B. Moseley

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Journal	Natural Product Communications
Volume	14
Issue number	5
DOIs	https://doi.org/10.1177/1934578X19849142
State	Published - May 2019

Bibliographical note

Funding Information:
The authors acknowledge support from the National Science Foundation grant and National Institutes of Health grants.

Funding Information:
with respect to the research, authorship, and/or publication of this article: 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).

Funding Information:
The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: 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).

Publisher Copyright:
© The Author(s) 2019

Keywords

Chemical shift reference correction
Protein NMR
Software package

ASJC Scopus subject areas

Pharmacology
Drug Discovery
Plant Science
Complementary and alternative medicine

Access to Document

10.1177/1934578X19849142

Cite this

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abstract = "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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author = "Xi Chen and Andrey Smelter and Moseley, {Hunter N.B.}",

note = "Funding Information: The authors acknowledge support from the National Science Foundation grant and National Institutes of Health grants. Funding Information: with respect to the research, authorship, and/or publication of this article: 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). Funding Information: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: 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). Publisher Copyright: {\textcopyright} The Author(s) 2019",

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