1. Rowland NMR Toolkit (RNMRTK) Overview
Originally developed as a platform for developing new NMR data processing methods
Used primarily for the purposes of running MaxEnt
Provides
Rich set of apodization (window) functions
DFT processing
General processing tools (phasing, reversing, truncating, etc.)
Robust, efficient Linear Prediction (LP) extrapolation
MaxEnt reconstruction (deconvolution, uniform or nonuniform sampling)
Efficient parallel processing
exports to nmrPipe, XEASY, Felix; NMRViewJ reads RNMRTK format.
Generates synthetic data, noise (testing/error analysis)
Free for Academic use and included in NMRbox
l1-norm real (LONER) has recently been added

2. RNMRTK – Suite of Programs
section – Manages the shared memory sections
rnmrtk – The main processing program in the Toolkit
Loading, apodization, DFT, phasing, saving, etc.
flip – Forward linear prediction
msa / msa2d / msa3d – Maximum entropy reconstruction in one, two, and three dimensions
inject – Add synthetic peaks to time domain data
select / zsample – Expands or compresses NUS data
seepln / contour – graphical display for 1D and 2D data
Many others …

3. RNMRTK – Shared memory section
With a suite of programs for performing tasks – How do you get data output from one command to the next command without needing to write out intermediate files?
Pipes (nmrPipe strategy)
Shared memory (RNMRTK strategy)
A shared memory section is a chunk of persistent memory where NMR data can be stored and used by different programs.
/dev/shm – A modern version of a shared memory implementation that gives the performance of shared memory, but without the overhead of managing traditional shared memory
section
RNMRTK program to manage (create / delete) shared memory sections
section -c
The size of the shared memory section is the product of the arguments multiplied by 4 (32 bit data) plus 512 extra bytes for a header.
Shared memory sections must be large enough to store all the data – including processing
section -d
Deletes the shared memory section

4. RNMRTK – Shared memory
The maximum size of shared memory sections is defined by kernel values shmmax and shmall. NMRbox takes care of these advanced settings.
Example:
Assume a data set size of 512 x 128 x 128 complex points
“section -c ”
Shared memory section is too small as data is complex
“section -c ”
Shared memory section just fits data, but cannot handled a ZF
“section -c ”
Shared memory section allows each dimension to be doubled
NOTES:
“section -d”
Shared memory sections stay persistent until forcefully deleted or the computer rebooted
“section -c 1024”
Shared memory sections will delete exisiting shared memory sections when created
No harm in having shared memory sections be too large

5. RNMRTK – rnmrtk
rnmrtk
Command for performing many of the traditional processing techniques
Row (column) oriented.
Many commands need to have the dimension set
Can be run:
one command at a time
in interactive mode
scripted

6. RNMRTK – Issuing commands
Arguments are entered as different classes:
floats (defined as a number with a decimal)
setpar SF
integers (defined as a number without a decimal)
zerofill 2048
string (defined as a character string without a period)
sinebell square 70.0
filename (defined as a character string with a decimal point)
loadvnmr ./fid
load hsqc.sec
The order of arguments entered is important, but only within a given class of argument
sstdc COS
sstdc COS 20
rnmrtk is case insensitive (except for filenames)

7. RNMRTK – Setting the dimension
DIM command sets the dimension for ROW oriented commands
rnmrtk << EOF
LOAD test.sec
DIM t1
FFT
EOF
Some commands define the dimension as arguments
msa2d t1 t2 ./parameter_file
Sensitivity Enhancement
The command sefix1 that is used to shuffle data collected with sensitivity enhancement. The dimension to shuffle is set in the command as an argument, but that dimension cannot be set beforehand with DIM
rnmrtk << EOF rnmrtk << EOF
DIM t2 DIM t3
SEFIX1 t2 SEFIX1 t2
EOF EOF
LOAD command does not need dimension set
FFT command must have dimension set

8. RNMRTK – Issuing commands
From the command line:
bash% rnmrtk loadvnmr ./fid
bash% rnmrtk setpar SF
bash%
* DIM command not persistent
In interactive mode
bash% rnmrtk
rnmrtk% loadvnmr ./fid
rnmrtk% DIM t2
rnmrtk% FFT
rnmrtk% exit
As a script
bash% processing_script.com
Typical script
#! bin/bash
section –c
rnmrtk << EOF
loadvnmr ./fid
setpar PPM
dim t2
zerofill 1024
fft 0.5
phase
realpart
EOF
dim t1
zerofill 128
save test.sec
Here doc
Cannot have spaces at the beginning of lines

9. RNMRTK – DFT “cd GOTH/large-HNCO” “more rnmrtk.com” DIM t1
SINEBELL SQUARE SHIFT 70.0 USE 128
ZEROFILL 256
FFT 0.50
PHASE
REALPART
SEEPAR
DIM t2
CONJ
PHASE
SAVE ft.sec
PUTNMRPIPE ft_f3f1.ft3 f3 f1
DIM F2
XSECT SUM
PUTNMRPIPE ft-f2_proj.ft2
LOAD ft.sec
DIM F1
PUTNMRPIPE ft-f1_proj.ft2
EOF
#! /bin/sh
section -c
rnmrtk << EOF
LOADVNMR t1 t2 TR120 D23 ./fid
SETPAR PPM
SETPAR PPM
SETPAR PPM
SEEPAR
EOF
DIM t3
SEFIX1 t2
SSTDC
BC 12.5 GM
ZEROFILL 1024
FFT 0.50
PHASE
SHRINK
REALPART

10. Maximum Entropy Reconstruction (MaxEnt)
MaxEnt can be performed in one dimension (msa), two dimensions (msa2d) and three dimensions (msa4d) – NOTE: # of dimension MaxEnt is acting on, not dimension of experiment.
The acquisition dimension is often processed with a DFT and not with MaxEnt
There are two modes for processing with MaxEnt
Constant AIM
Must define DEF and AIM
Constant LAMBDA
Must define DEF and LAMBDA
When all the dimensions are NOT processed with MaxEnt, such as when the acquisition dimension is processed with a DFT, you MUST use Constant Lambda mode
2D Data sets
Option 1: Process F2 with DFT, and F1 with msa (lambda mode)
Option 2: Process F2-F1 planes with msa2d (aim mode)
3D Data sets
Option 1: Process F3 with DFT and F1-F2 planes with msa2d (lambda mode)
Option 2: Process F3-F2-F1 cube with msa3d (aim mode)
4D Data sets
Option 1: Process F4 with DFT and F1-F2-F3 cubes with msa3d (lambda mode)

11. How to choose aim?
AIM should be set to a value close to the RMS of the noise.
Values too high will cause weak features of the spectrum to be lost
Values too low will cause even minute details of the noise to be included (will try to force an exact match to the noise)
Clear non-linearities are observed in peak intensities and are dependent on the value of aim.
170
110 50 1
unapodized DFT

12. How to choose def?
Large values of DEF lead to reconstructions resembling the DFT
Small values of DEF suppress noise but distort relative peak intensities
Like aim, setting DEF to values near the RMS is generally the most appropriate
DEF is less sensitive than AIM and can be altered by orders of magnitude
1000
100 1

13. DEF & AIM
High DEF, Low AIM
Very low DEF, Low AIM
Low DEF and Low AIM

14. LAMBDA
During a constant AIM calculation a value LAMBDA will be converged to.
LAMBDA is the weight applied to the entropy function relative to the constraint of matching to the experimental data
If processing 1D slices of a 2D; 2D planes of a 3D; or 3D cubes of a 4D, each calculation will converge to a different LAMBDA.
LAMBDA will affect the non-linearity and cause a “scaling” of the data
Let’s assume we are processing a 3D data set where the acquisition dimension is processed with a DFT and the 2D indirect planes are processed with MaxEnt
In constant AIM mode each plane will have a slightly different LAMBDA value and hence a slight different in scaling and non-linearity. This will cause distortions in the 3D peak shapes
To “fix” this issue MaxEnt can be run in constant LAMBDA mode
To determine LAMBDA, it is typical to process the data initially in constant AIM mode and average the converged LAMBDA values.

15. RNMRTK – msa2d – 2 MODES
Maximum Entropy Reconstruction in 2 dimensions
Reads parameters from a file and dimensions are specified on invocation line
Ex: msa2d t1 t2 msa2d.param
Constant Aim parameter file
DEBUG 1
NLOOPS 400
DEF 0.1
AIM 2.5
SCALEFIRST 0.5
SCHED ./sample_schedule
NUSE
NOUT
PHASE LW
JVALUE
Constant LAMBDA parameter file
DEBUG 1
NLOOPS 400
DEF 0.1
LAMBDA 1.0
SCALEFIRST 0.5
SCHED ./sample_schedule
NUSE
NOUT
PHASE LW
JVALUE
Max loops, convergence may be quicker
NUS
Uniform
Data will be extrapolated
For 3D NUS datasets the acquisition dimension is typically processed with conventional FT and then the indirect dimensions are processed with msa2d

16. Let’s try a MaxEnt Reconstruction
“cd large-HNCO”
“more msa2d_param”
“wc -l schedule3.scd”
There are 400 / 16,384 FIDs collected (2.4%)
./msa2d.com schedule3.scd test”
“nmrDraw -in test_nudft_proj.ft2”
“nmrDraw -in test_nudft.ft3”
“nmrDraw -in test_msa2d_proj.ft2”
“nmrDraw -in test_msa2d.ft3”
You can compare the results to the DFT using 100% of the FIDs
“nmrDraw -in ft-f1_proj.ft2”
“nmrDraw -in ft-f2_proj.ft2”
“nmrDraw -in ft_f3f1.ft3”

17. RNMRTK – msa2d Monitoring convergence Very important!
Ensure that the value of test is less than 0.1 unless fewer than NLOOPS required
Without deconvolution convergence typically occurs is around 40 iterations if proper DEF, AIM, and LAMBDA values were use.
High numbers of loops suggest msa2d is modeling noise
With deconvolution the number of loops may be significantly higher.
How to check values for Test and the maximum number of loops
“tail msa2d.txt”
“grep ^Test msa2d.txt | sort -n -k3”
“grep Loop msa2d.txt | sort -n -k2”