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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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