Deep Neural Networks for Analysing NMR time domain data. See:
This is the short version of this README. For more details, see more detailed explanations below.
git clone https://github.com/gogulan-k/FID-Net.git
cd FID-Net
# Following two command only if you don't have NMRPipe installed:
chmod +x install_nmrpipe.sh
install_nmrpipe.sh
mamba env update -f environment.yml
mamba activate fidnet
fidnet run-examplesFirst, clone the repository:
git clone https://github.com/gogulan-k/FID-Net.gitThe easiest way get a working environment with all packages that FID-Net needs, use conda or mamba and the provided environment.yml file:
cd FID-Net
mamba env update -f environment.ymland activate the environment:
mamba activate fidnetInstalling the environment also installs the "fidnet" package, making the fidnet command line tool available (see below).
The weights of the neural networks are not included in this python package, but will be downloaded on the fly when needed.
If you want to manually trigger downloading the weights for all different models at once, type:
fidnet download-weightsThe weights are downloaded by default to the gitignored directory:
<REPOSITORY ROOT>/data/weightsYou can change settings like these by adding a .env file or setting environment variables specifying the FIDNET_DATA_DIR or FIDNET_WEIGHTS_DIR:
# .env
FIDNET_WEIGHTS_DIR=/path/to/directory/with/weights.hd5To take a look at all such settings type:
fidnet settingsIf you have a working environment (and NMRPipe installed, if not see next section), you can test whether things are working by running all examples at once:
fidnet run-examplesThis will download example data, run all the different FID-Net functions (except the 3D HNCA decoupler, which takes a lot longer to run). If you just want to download the example data, without doing the processing by the models:
fidnet download-example-dataNMRPipe can not be installed using conda. If you don't have it installed yet, you can use the provided script to install it.
chmod +x install_nmrpipe.sh
install_nmrpipe.shNMRPipe gives some instructions about how to edit your .cshrc. It will look something like this:
if (-e <REPO_DIR>/bin/NMRPipe/com/nmrInit.linux212_64.com) then
source <REPO_DIR>/bin/NMRPipe/com/nmrInit.linux212_64.com
endifFollow those instructions, so that the NMRPipe command can be found.
Please refer to the --help in the command line tool. Each individual command has its own help, explaining what the input arguments are.
(fidnet) ➜ ~ fidnet --help
Usage: fidnet [OPTIONS] COMMAND [ARGS]...
Deep Neural Networks for Analysing NMR time domain data.
https://github.com/gogulan-k/FID-Net
╭─ Options ───────────────────────────────────────────────────────────────────╮
│ --install-completion Install completion for the current shell. │
│ --show-completion Show completion for the current shell, to │
│ copy it or customize the installation. │
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ──────────────────────────────────────────────────────────────────╮
│ ca_detect FID-Net 2D CA detect │
│ con_decouple FID-Net 2D CON decoupling │
│ ctcp_decouple FID-Net 2D CTCP decoupling │
│ methyl FID-Net Decouple and improve resolution │
│ of spectra for uniformly 13C-1H labelled │
│ proteins. │
│ hnca FID-Net 3D HNCA decoupling. │
│ reconstruct FID-Net 2D Non-Uniform Sampling (NUS) reconstruction |
| aromatic FID-Net2 for spectra for Aromatic Sidechains │
│ run-examples Run all the examples in one go. │
│ download-example-data Download example data to try out the different │
│ FID-Net functions. │
│ download-weights Download the weights for all FID-Net models. Running │
│ this is not strictly necessary as the weights are │
│ downloaded on the fly for individual models when │
│ they are not present yet. │
│ settings │
│ version Show the version of the nucleotides library. │
╰─────────────────────────────────────────────────────────────────────────────╯
Thanks!(fidnet) ➜ ~ fidnet ca_detect --help
Usage: fidnet ca_detect [OPTIONS]
FID-Net 2D CA detect
╭─ Options ───────────────────────────────────────────────────────────────────────╮
│ * --infile PATH Help text in the original was the same as for │
│ con_decouple │
│ [default: None] │
│ [required] │
│ --outfile PATH Path to the output file. │
│ [default: fidnet_ca_detect.ft1] │
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────────╯Note: the decoupler can only work with up to 512 complex points in the 13C dimension. Spectra containing more points than this will be truncated at 512 complex points.
An example antiphase, in-phase (AP-IP) spectrum of T4L99A (test.ft1) is provided in the example folder.
The input to the DNN must be in nmrPipe format. If using a Bruker spectrometer, the raw FID file (ser) must be converted to nmrpipe format using the DMX flag for FID-Net decoupling to perform correctly.
Prior to input into FID-Net, the direct dimension of the spectrum is phased but the imaginary part is not deleted. The spectrum is then transposed, apodized, zero-filled, phased and Fourier transformed in the indirect dimension. For best results excessive zero-filling in the indirect dimension should be avoided. Typically we would just use 'ZF -auto' in nmrPipe. The spectrum should then be transposed before entry into FID-Net.
The input to the DNN must be a 2D in-phase interferogram (i.e. processed in the indirect dimension but not the direct dimension as described above).
The output of the DNN can then be processed (apodized, zero-filled, Fourier transformed and the imaginary part deleted) to give the final result. An example (final_proc.com) is provided in the example folder.
(fidnet) ➜ ~ fidnet con_decouple --help
Usage: fidnet con_decouple [OPTIONS]
FID-Net 2D CON decoupling
╭─ Options ───────────────────────────────────────────────────────────────────────╮
│ * --infile PATH Input spectra. This is a 2D in phase CON spectra with │
│ the 13C dimension in the time domain. The 13C │
│ dimension should be phased but the imaginary part │
│ retained and should not be apodized, zero-filled or │
│ Fourier transformed. The 15N dimension should be │
│ apodized, zero-filled, phased (the imaginary part │
│ deleted) then Fourier transformed. The order of the │
│ input dimensions must be 15N, 13C. │
│ [default: None] │
│ [required] │
│ --outfile PATH Path to the output file. │
│ [default: fidnet_con_decoupled.ft1] │
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────────╯Note: the 2D CON decoupler can only work with up to 512 complex points in the 13C dimension. Spectra containing more points than this will be truncated at 512 complex points.
An example in-phase spectrum of ubiquitin (test001.ft1) is provided in the example folder.
The input to the DNN must be in nmrPipe format. If using a Bruker spectrometer, the raw FID file (ser) must be converted to nmrpipe format using the DMX flag for FID-Net decoupling to perform correctly.
Prior to input into FID-Net, the direct dimension of the spectrum is phased but the imaginary part is not deleted. The spectrum is then transposed, apodized, zero-filled, phased and Fourier transformed in the indirect dimension. For best results excessive zero-filling in the indirect dimension should be avoided. Typically we would just use 'ZF -auto' in nmrPipe. The spectrum should then be transposed before entry into FID-Net.
The input to the DNN must be a 2D in-phase interferogram (ie. processed in the indirect dimension but not the direct dimension as described above). If the data is 3D it must be converted to a set of 2D planes using the pipe2xyz utility or similar. An exemplar input is provided in the example folder (test001.ft1).
The output of the DNN can then be processed (apodized, zero-filled, Fourier transformed and the imaginary part deleted) to give the final result. An example (final_proc.com) is provided in the example folder.
(fidnet) ➜ ~ fidnet ctcp_decouple --help
Usage: fidnet ctcp_decouple [OPTIONS]
FID-Net 2D CTCP decoupling
╭─ Options ───────────────────────────────────────────────────────────────────────╮
│ * --infile PATH Input spectra. This is a 2D in-phase Ct-Cp spectra │
│ with the 13Ct dimension in the time domain. The 13Ct │
│ dimension should be phased but the imaginary part │
│ retained and should not be apodized, zero-filled or │
│ Fourier transformed. The 13Cp dimension should be │
│ apodized, zero-filled, phased (the imaginary part │
│ deleted) then Fourier transformed. The order of the │
│ input dimensions must be 13Cp, 13Ct. │
│ [default: None] │
│ [required] │
│ --outfile PATH Path to the output file. │
│ [default: fidnet_ctcp_decoupled.ft1] │
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────────╯Note: the 2D 13Ct-13Cp decoupler can only work with up to 512 complex points in the 13C dimension. Spectra containing more points than this will be truncated at 512 complex points.
An example in-phase spectrum of ubiquitin (test001.ft1) is provided in the example folder.
The input to the DNN must be in nmrPipe format. If using a Bruker spectrometer, the raw FID file (ser) must be converted to nmrpipe format using the DMX flag for FID-Net decoupling to perform correctly.
Prior to input into FID-Net, the direct dimension of the spectrum is phased but the imaginary part is not deleted. The spectrum is then transposed, apodized, zero-filled, phased and Fourier transformed in the indirect dimension. For best results excessive zero-filling in the indirect dimension should be avoided. Typically we would just use 'ZF -auto' in nmrPipe. The spectrum should then be transposed before entry into FID-Net.
The input to the DNN must be a 2D in-phase interferogram (ie. processed in the indirect dimension but not the direct dimension as described above). If the data is 3D it must be converted to a set of 2D planes using the pipe2xyz utility or similar. An exemplar input is provided in the example folder (test001.ft1).
The output of the DNN can then be processed (apodized, zero-filled, Fourier transformed and the imaginary part deleted) to give the final result. An example (final_proc.com) is provided in the example folder.
(fidnet) ➜ ~ fidnet reconstruct --help
Usage: fidnet reconstruct [OPTIONS]
FID-Net 2D Non-Uniform Sampling (NUS) reconstruction
╭─ Options ───────────────────────────────────────────────────────────────────────╮
│ * --infile PATH this is the measured 2D │
│ non-uniformly sampled spectra. │
│ It should be processed in the │
│ direct dimension, phased and │
│ transposed. The indirect │
│ dimension should not be │
│ processed in any way. The │
│ unsampled points in the │
│ indirect dimension should not │
│ be replaced with zeros (for │
│ example by using the nusExpand │
│ tool) this is taken care of by │
│ the program itself. The │
│ maximum number of complex │
│ points in the indirect │
│ dimension that can be included │
│ in the network is 256. │
│ Thespectrum will be truncated │
│ after this. │
│ [default: None] │
│ [required] │
│ * --sampling-schedule PATH this is the sampling schedule │
│ used. This is simply a list │
│ ofintegers (oneinteger per │
│ line) giving the complex │
│ points that are measured in │
│ the NUS experiment. │
│ [default: None] │
│ [required] │
│ * --max-points INTEGER this is the number of complex │
│ points in the final output. │
│ I.e.the sparsity is given by │
│ the number of values in the │
│ samplingschedule divided by │
│ this value. │
│ [default: None] │
│ [required] │
│ --outfile PATH name of the output file │
│ [default: │
│ fidnet_nus_reconstructed.ft1] │
│ --f1180 --no-f1180 f1180 flag (y/n) only │
│ important for matplotlib │
│ output and │
│ fidnet_reconstructed.ft2 │
│ [default: f1180] │
│ --shift --no-shift frequency shift flag (y/n) │
│ only important for matplotlib │
│ output and std.ft2 │
│ [default: no-shift] │
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────────╯This code is for reconstructing 2D NUS NMR spectra using the FID-Net architecture. To use the code, the file containing the weights for the trained network must be downloaded.
The output of the network is an nmrPipe file with the indirect dimension reconstructed in the time domain. The indirect dimension can now be processed (apodized, zero-filled, phased and Fourier transformed) to yield the final reconstructed spectrum. The analysis also outputs std.ft2, providing a measure of confidence in the outputs. This is also in nmrPipe format and is pre-processed and Fourier transformed according to default parameters. If these are incorrect a Hilbert transform and inverse Fourier transform can be applied to put this back into the time domain before reprocessing.
There is an example file for HDAC in the example folder, together with the sampling schedule.
(fidnet) ➜ ~ fidnet hnca --help
Usage: fidnet hnca [OPTIONS]
FID-Net 3D HNCA decoupling.
╭─ Options ───────────────────────────────────────────────────────────────────────╮
│ * --infile PATH Input spectra. This is a 3D HNCA orHN(CO)CA spectra │
│ with the 13C dimension in the time domain.The 15N and │
│ 1H dimensions should be phased and Fourier │
│ transformed.The order of the input dimensions must be │
│ 1H,15N, 13C. │
│ [default: None] │
│ [required] │
│ --outfile PATH out file [default: fidnet_hnca_decoupled.ft2] │
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────────╯This code is for decoupling 3D HNCA and HN(COCA) spectra using the FID-Net architecture. Note: the 3D HNCA decoupler can only work with up to 256 complex points in the 13C dimension. Spectra containing more points than this will be truncated at 256 complex points.
(fidnet) ➜ ~ fidnet methyl --help
Usage: fidnet methyl [OPTIONS]
FID-Net Decouple and improve resolution of spectra for uniformly 13C-1H labelled
proteins.
╭─ Options ───────────────────────────────────────────────────────────────────────╮
│ * --infile PATH Input spectra. This is a 2D 13C-1Hspectra │
│ (time domain data) fora uniformly labelled │
│ 13C-1H labelled protein.If using literally │
│ 'example',an example file is used │
│ [default: None] │
│ [required] │
│ --outdir PATH folder where resultswill be saved. │
│ [default: fidnet_out] │
│ --outfile PATH filename for finalprocessed spectrum. │
│ [default: fidnet_methyl_CH.ft2] │
│ --min-1h FLOAT minimum 1H ppm [default: -1.0] │
│ --max-1h FLOAT maximum 1H ppm [default: 2.5] │
│ --p0 FLOAT 1H phase correction [default: 0.0] │
│ --alt --no-alt NMRPipe: dimension is left/right swapped │
│ [default: no-alt] │
│ --neg --no-neg NMRPipe: dimension is reversed │
│ [default: no-neg] │
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────────╯This code is for improving the resolution of protein spectra from uniformly 13C-1H proteins. The code requires two DNNs based on FID-Net architecture. The first network removes one 13C-13C scalar coupling and sharpens peaks in the 13C dimension. The second network sharpens peaks in the 1H dimension.
The example folder contains data for uniformly 13C-1H labelled HDAC8.
(fidnet) ➜ ~ fidnet aromatic --help
Usage: fidnet aromatic [OPTIONS]
FID-Net2 ransforms NMR spectra recorded on simple uniformly 13C labelled samples to
yield high-quality 1H-13C correlation spectra of the aromatic side chains.
Spectra should be recorded with the dedicated pulse programme
╭─ Options ───────────────────────────────────────────────────────────────────────╮
│ * --infile PATH Input spectra. This should be a pseudo-3D │
│ NMR pipe file that has been recorded using │
│ the dedicated pulse sequence (see folder) │
│ │
│ [default: None] │
│ [required] │
│ --outfile PATH filename for final processed spectrum. │
│ [default: aromatic_output.ft2] │
│ --UseGPU BOOL True to use GPU. │
| [default: True] |
│ --GPUIDX INT GPU number to use │
| [default: None] |
│ --offset1h FLOAT Set the offset for the sine-squared window |
| function in the 1H dimension. Default is |
| 0.40, which was used during training │
│ [default: 0.4] |
│ --offset13c FLOAT Set the offset for the sine-squared window |
| function in the 1H dimension. Default is |
| 0.40, which was used during training │
│ [default: 0.4] |
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────────╯You can install pre-commit hooks that do some checks before you commit your code:
pip install -e ".[dev]"
pre-commit install