RMSD

The Root Mean Squared Deviation (RMSD) of the baseplane noise in a spectrum.

Software to use

SPARKY. In the extension menu.

In F1 mode, draw boxes in the empty regions for all spectra. You have a different error estimate for each spectrum, which relax handles.

It is a good idea to use the Sparky rm function to get the RMSD of the spectrum as a maximal noise estimate in several areas free of peaks.
If there are peaks which may be in noisier regions (eg near the water line) it can be a good idea to give those peaks separate errors by measuring the RMSD near those peaks seperately.

Reference

For a region
http://www.cgl.ucsf.edu/home/sparky/manual/extensions.html#RegionRMSD

Whole spectrum
http://www.cgl.ucsf.edu/home/sparky/manual/views.html#Noise

Notes

The error is equal to the baseplane RMSD times the number of 2D spectra points in the box.

Reference

Do this for PEAK HIGHTS determination.

The empty region RMSD is smaller than the random coil region, due to power conservation in the Fourier transform.
You can see this by dropping all the way to the baseplane and carefully looking.
You should measure with boxes near the peaks, and make sure no peaks are in the box.
This estimate is much better than the full spectrum RMSD measures from the other programs.

Peak shifts

Be aware of peak shifts, due to temperature

showApod with NMRPipe

showApod test.ft2 | grep "REMARK Automated Noise Std Dev in Processed Data:" | awk '{print $9} '

This will print two numbers, first is noise estimate for the spectrum, second is noise estimate for the time-domain ... try it without the "-noverb" option to see more info.

showApod -in test.ft2 -noverb

Post by Frank Delaglio, Oct 29, 2009

Hi George,

A section of an earlier post about noise estimation is below.

The noise estimation is supposed to estimate the standard deviation
in the baseline of the spectrum, i.e. the areas of the spectrum
without substantial signal. It assumes:

1. The noise level in the spectrum is uniform

2. The noise has a Gaussian distribution, or roughly so.

3. Most of the spectrum is "empty", i.e. free of
substantial signal.

So, if the noise estimation is successful, it will give values
similar to those that you would find if you manually identify
a baseline region and take its standard deviation.

Along these lines, you can imagine that by looking at the histogram
of intensities in a signal-free region, you could also estimate its
standard deviation.

As the post below says, some noise estimation tools use the entire
spectral data set at once. However, this is NOT the same as
simply taking the standard deviation of the entire spectrum
at once, since the entire spectrum includes both signal AND
baseline.

Instead, the noise estimation techniques attempt to separate the
contributions from "signal" and "baseline". In our case,
we assume that most points are baseline points, such that if
we build a histogram of intensities, most of the values
corresponding to small intensities come from the baseline
rather than from the signals. This means that to a first
approximation, the part of the histogram describing the smallest
intensities can be used to characterize the baseline.

In practice, this noise estimation technique works better
when we perform many independent noise estimates on vectors
from the data, rather than perform one noise estimate using
all the data at once. This is because individual vectors from
a 2D or 3D dataset are more likely to contain a substantial fraction
of baseline points, and in fact many individual vectors will consist
entirely of baseline, in which case the histogram method will be
at its most effective.

Hope this explanation helps ...

big fd


From an earlier post about noise estimation:

---
The noise estimation details used by autoFit.tcl were recently
improved ... if you search the text of the script, the older
version will use the function "vEstNoise", and the newer
version will use the function "estSpecNoise".

The basic mechanism of noise detection is the same in both
cases; a histogram of the data intensities is analyzed, under
the assumption that most of the points in the spectrum are
in the baseline, so that the innermost part of the histogram
is due primarily to baseline noise. IF we assume that the
baseline noise is normally distributed, we can use the histogram
to estimate the standard deviation of the noise.

The older "vEstNoise" implementation analyzed an entire spectral
region at once to form a single noise estimate. In the case of 2D/3D
data, the newer "estSpecNoise" forms separate noise estimates for 
individual
vectors from the data, and uses the median.
---

References

Palmer, 3rd, A. G., Rance, M., and Wright, P. E. (1991). Intramolecular motions of a zinc finger DNA-binding domain from Xfin characterized by proton-detected natural abundance carbon-13 heteronuclear NMR spectroscopy. J. Am. Chem. Soc., 113(12), 4371-4380. (DOI: 10.1021/ja00012a001)

Farrow, N. A., Muhandiram, R., Singer, A. U., Pascal, S. M., Kay, C. M., Gish, G., Shoelson, S. E., Pawson, T., Forman-Kay, J. D., Kay, L. E. (1994). Backbone dynamics of a free and phosphopeptide-complexed Src homology 2 domain studied by 15N NMR relaxation. Biochemistry, 33(19), 5984-6003. (DOI: 10.1021/bi00185a040)

RMSD

Software to use

Reference

Notes

Reference

Peak shifts

showApod with NMRPipe

References

See also