Interface to the Biological Magnetic Resonance Data Bank ('BMRB')

A comprehensive interface to the Biological Magnetic Resonance Data Bank ('BMRB') < https://bmrb.io>, mirroring all functionality of the 'PyBMRB' 'Python' package (v3.0). Provides four modules: (1) 'ChemicalShift' for fetching per-entry chemical shift data from the 'BMRB' 'REST' 'API' or local 'NMR-STAR' files; (2) 'ChemicalShiftStatistics' for database-wide statistical queries; (3) Spectra for simulating '1H-15N' 'HSQC', '1H-13C' 'HSQC', '1H-1H' 'TOCSY', and generic 2D 'NMR' spectra as interactive plots; and (4) Histogram for chemical shift distribution visualizations with optional filtering by pH, temperature, and ambiguity code. All interactive plots are rendered via 'plotly'. Uses the 'BMRB' REST API v2 < https://api.bmrb.io/v2/>.

RBMRB

RBMRB is a comprehensive R interface to the Biological Magnetic Resonance Data Bank (BMRB), mirroring all functionality of PyBMRB 3.0.

Installation

# From CRAN
install.packages("RBMRB")

# Development version from GitHub
# install.packages("remotes")
remotes::install_github("bmrb-io/RBMRB")

Quick start

library(RBMRB)

# Verify connectivity
bmrb_test()

# Fetch chemical shifts
cs <- cs_from_bmrb(15060)
cs_peek(cs)

# Simulate spectra (interactive Plotly)
n15hsqc(bmrb_ids = 15060, legend = "residue")
c13hsqc(bmrb_ids = 15060)
tocsy(bmrb_ids   = 15060)

# Compare two entries
n15hsqc(bmrb_ids = c(17076, 17077), draw_trace = TRUE)

# Chemical shift histograms
cs_hist("ALA", "CA")
cs_hist2d("ALA", "CA", "CB")
conditional_cs_hist("SER", "N", filtering_rules = list(CA = 55.5))

# Export peak list (CSV or Sparky)
pl <- create_n15hsqc_peaklist(bmrb_ids = 15060)
export_peak_list(pl, "peaks.csv")
export_peak_list(pl, "peaks.sparky", output_format = "sparky")

Modules

Citation

If you use RBMRB in published research, please cite:

Ulrich EL et al. (2008). BioMagResBank. Nucleic Acids Research 36, D402–D408. https://doi.org/10.1093/nar/gkm957

Baskaran K et al. (2021). PyBMRB: Data visualization tool for BioMagResBank. Proc. 20th Python in Science Conf., 59–62.