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pDeep3

installation

pip install . or pip install -e .

System requirements

python >= 3.5

tensorflow == 1.13.1 (tensorflow >= 1.13.0, tensorfow 1.x.x is only supported by python <= 3.7)

.NET Framework == 4.5.2 (or higher? to execute psmLabel)

Tuning and predicting using get_prediction

Now psmLabel, tuning and predicting are integrated in pDeep.cmd.tune_and_predict.py

pDeep.cmd.tune_and_predict.get_prediction(input_peptides, tune_psm = None, raw = None, instrument = 'QE', ce = 27)

@param input_peptides, could be a peptide list [(sequence1, mod1, charge1), (seq2, mod2, charge2), ...] to be predicted, or a file containing tab seperated head "peptide, modinfo, charge, protein".
@param tune_psm evidence.txt (MaxQuant), .spectra (pFind) or *.psm.txt/*.txt (with tab seperated heads "raw_name, scan, peptide, modinfo, charge, RTInSeconds") file for tuning pDeep and pDeepRT. If it is None, the model will not be tuned (default None).
@param raw, raw file for tuning pDeep and pDeepRT (default None).
@param instrument, instrument type for prediction (default "QE").
@param ce, collision energy for prediction (default 27).
@return prediction, pDeep.prediction.pDeepPrediction object, 
# example: 
# ion_types = ['b','y','b-ModLoss','y-ModLoss'] or ['b','y']
# ion_indices, used_ion_types = prediction.GetIonTypeIndices(ion_types)
# print(used_ion_types)
# intensities = GetIntensitiesByIndices('ACDMNLK', '2,Carbamidomethyl[C];4,Oxidation[M]', 3, ion_indices)

Example of input file input_peptides file:

raw_name	scan	peptide	modinfo	charge	RTinSeconds
raw_sample1	7932	TCEATHKTSTSPIVKSF	2,Carbamidomethyl[C]	2	666.4
raw_sample1	13419	KIDGMERQDGVLNSW		3	1145.2
raw_sample1	13440	KIDGMERCDGVLNSW	5,Oxidation[M];8,Carbamidomethyl[C]	2	1147.0
raw_sample2	10709	TCEATHKTSTSPIVKSF	16,Phospho[S]	2	901.3

Run:

from pDeep.cmd.tune_and_predict import get_prediction

input_peptides = [('ACDMNLK', '2,Carbamidomethyl[C];4,Oxidation[M]', 3)]
ion_types = ['b','y']
prediction = get_prediction(input_peptides) # no fine-tuning
ion_indices, used_ion_types = prediction.GetIonTypeIndices(ion_types)
print(used_ion_types) 
# output: ['b+','b++','y+','y++']
print(prediction.GetIntensitiesByIndices(*input_peptides[0], ion_indices))
# output: 
[
[ 0.0000000e+00  0.0000000e+00  0.0000000e+00  0.0000000e+00]
[ 3.5514534e-01  3.9369406e-12  5.2654832e-03  0.0000000e+00]
[ 4.4627541e-01  1.0988828e-09  2.0606143e-02  0.0000000e+00]
[ 1.2166708e-02 -0.0000000e+00  1.4021127e-01  0.0000000e+00]
[ 2.1781624e-08 -0.0000000e+00  2.6448551e-01  8.2786764e-09]
[ 0.0000000e+00 -0.0000000e+00  9.9956471e-01  1.1667855e-06]
]

Fine-tuning using prepared psm file:

prediction = get_prediction(input_peptides, tune_psm=r"e:\DIAData\PECAN\tune.psm.txt", raw=r"e:\DIAData\PECAN\20141010_DIA_20x5mz_700to800.raw")

Or fine-tuning using pFind3's pFind.spectra or pFind-Filtered.spectra:

prediction = get_prediction(input_peptides, tune_psm=r"e:\DIAData\PECAN\pFind.spectra", raw=r"e:\DIAData\PECAN\20141010_DIA_20x5mz_700to800.raw")

Or fine-tuning using MaxQuant's evidence.txt:

prediction = get_prediction(input_peptides, tune_psm=r"e:\DDATools\MaxQuant_1.6.12.0\test_data\combined\txt\evidence.txt", raw=r"e:\DDATools\MaxQuant_1.6.12.0\test_data\20141010_DIA_20x5mz_700to800.raw")

Run them seperately:

Preparing fine-tuning data using psmLabel

psmLabel now can directly access raw files by using Thermo RawFileReader.

Run psmLabel:

cd psmLabel
psmLabel.exe psmLabel-sample.cfg

or in linux, run .NET applications using mono:

mono psmLabel.exe psmLabel-sample.cfg

Example of psmLabel-sample.cfg:

psm_type = none
mode = pDeep
num_psm_file = 1
psm_file1 = D:\plabel\psm_sample.txt
ms2_type = raw
num_ms2_file = 2
ms2_file1 = D:\plabel\raw_sample1.raw
ms2_file2 = D:\plabel\raw_sample2.raw
output_folder = D:\plabel\output
NH3_loss = true
H2O_loss = true
Mod_loss = true
num_ion_type = 2
iontype1 = b|N_term|0
iontype2 = y|C_term|0
num_new_aa = 0

Example of input file psm_sample.txt:

raw_name	scan	peptide	modinfo	charge
raw_sample1	7932	TCEATHKTSTSPIVKSF	2,Carbamidomethyl[C]	2
raw_sample1	13419	KIDGMERQDGVLNSW		3
raw_sample1	13440	KIDGMERCDGVLNSW	5,Oxidation[M];8,Carbamidomethyl[C]	2
raw_sample2	10709	TCEATHKTSTSPIVKSF	16,Phospho[S]	2

Check the names of modifications in pDeep/config/modification.py.

Users now can prepare a engine-independent input psm file (psm_sample.txt) for psmLabel.

After executing "psmLabel.exe psmLabel-sample.cfg", it will generate two result files at specified output_folder (D:\plabel\output in the example) named after the two raw files: raw_sample1.psmlabel and raw_sample2.psmlabel, these two files can be used to fine-tune/train and test the pDeep model.

fine-tune and predict

Usage:

python -m pDeep.cmd.tune_and_predict tmp/predict/pDeep-tune.cfg

There will be quite a few warnings when importing tensorflow, just ignore them.

Example of tmp/predict/pDeep-tune.cfg:

model = tmp/model/pretrain-180921-modloss-mod8D.ckpt
threads = 4

###### predict ######
mod_no_check = Carbamidomethyl[C]
mod_check = Oxidation[M],Phospho[Y],Phospho[S],Phospho[T]
min_mod_check = 0
max_mod_check = 3
# format: peptide filename | instrument | NCE
predict_input = tmp/predict/peptide.txt | QE | 28


###### Data for fine-tuning, no tuning if it is empty. Files are seperated by '|' ######
tune_psmlabels = tmp\data\Olsen-Chymo-QE-28\raw0\20150708_QE3_UPLC8_DBJ_QC_HELA_39frac_Chymotrypsin_19.psmlabel
n_tune_per_psmlabel = 100

# Data for testing, no testing if it is empty. Files are seperated by '|'
test_psmlabels = tmp\data\Olsen-Chymo-QE-28\raw1\20150708_QE3_UPLC8_DBJ_QC_HELA_39frac_Chymotrypsin_23_2.psmlabel | tmp\data\Olsen-Chymo-QE-28\raw2\20150708_QE3_UPLC8_DBJ_QC_HELA_39frac_Chymotrypsin_24.psmlabel

See pDeep.cmd.tune_and_predict.py for details.

Note that pDeep/cmd/tune_and_predict.py can also be imported and called by other python scripts.

import pDeep.cmd.tune_and_predict
pdeep_prediction = tune_and_predict.run("tmp/predict/pDeep-tune.cfg")
for peptide, intensities in pdeep_prediction.peptide_prediction_dict.items():
    print("b+ ions of %s ="%peptide, pdeep_prediction.GetIntensitiesByIonType(intensities, "b", 1)) # get b+ ions
for peptide, intensities in pdeep_prediction.peptide_prediction_dict.items():
    print("y+ ions of %s ="%peptide, pdeep_prediction.GetIntensitiesByIonType(intensities, "y", 1)) # get y+ ions

Generating spectral libraries

Example:

python -m pDeep.cmd.generate_predicted_speclib --input xxx.fasta --target_proteins Q1234,Q6789 --output library.pqp --varmod Oxidation[M],Phospho[S] --instrument QE --ce 28 --min_intensity 0.1 --least_n_peaks 6

"--min_intensity 0.1 --least_n_peaks 6" means that if there are less than 6 peaks larger than 0.1, top-6 peaks will be kept, otherwise all peaks larger than 0.1 will be kept. "--output library.pqp" for OpenSWATH, "--output library.dlib" for EncyclopeDIA, "--output library.csv" for Spectronaut.

Run

python -m pDeep.cmd.generate_predicted_speclib --help

to see detailed usage information.

Using EThcD model

Example:

python -m pDeep.cmd.generate_predicted_speclib --input tmp/predict/peptide.txt --output xxxx.msp --model EThcD --ion_type b,y,c,z --instrument Lumos --ce 28 --min_intensity 0.0001

Here, for .msp file, all non-zero ions should be generated, hence "--min_intensity 0.0001". Note that EThcD model was only trained by ProteomeTools, so the instrument and ce parameters must be: "--instrument Lumos --ce 28" even you use Fusion or other instruments (28 is the NCE of HCD).

Supporting TensorFlow2

tf2 models have been trained for HCD and EThcD, and pDeep will automatically load the tf2 model by checking tf.version. However, there is a bug in fine-tuning when using tf2 (v2.1.0), it seems that it has been fixed in the nightly built version (see tensorflow/tensorflow#34211).