RBMRB is a comprehensive R interface to the Biological Magnetic Resonance Data Bank (BMRB), mirroring all functionality of PyBMRB 3.0.
# From CRAN
install.packages("RBMRB")
# Development version from GitHub
# install.packages("remotes")
remotes::install_github("bmrb-io/RBMRB")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")| Module | Functions | Description |
|---|---|---|
| ChemicalShift | cs_from_bmrb(), cs_from_file() |
Per-entry chemical shifts |
| ChemicalShiftStatistics | get_cs_data(), get_2d_cs_data() |
Database-wide statistics |
| Spectra | n15hsqc(), c13hsqc(),
tocsy(), generic_2d() |
Spectrum simulation |
| Histogram | cs_hist(), cs_hist2d(),
conditional_cs_hist() |
Distributions |
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.