├── .gitignore ├── LICENSE ├── README.md ├── amPEPpy ├── __init__.py ├── _version.py └── amPEP.py ├── images └── Figure_S2.png ├── paper_results ├── feature.importances.dropcol.oob.lengthbalanced.csv ├── feature_drop.lengthbalanced.txt ├── oob_classify.2012.lengthbalanced.csv ├── oob_classify.5.randomsubsample.csv ├── oob_count.balanced.seed5.txt └── oob_prop.balanced.seed2012.txt ├── pretrained_models └── amPEP.model ├── requirements.txt ├── setup.py └── training_data ├── M_model_train_AMP_sequence.numbered.fasta ├── M_model_train_nonAMP_sequence.numbered.fasta ├── M_model_train_nonAMP_sequence.numbered.proplen.subsample.fasta ├── M_model_train_nonAMP_sequence.numbered.randomsubsample.fasta ├── Subsampled_community_taxa.txt ├── nonAMPprop.txt └── nonAMPseqlen_rangeadded.tsv /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # amPEPpy: An antimicrobial peptide prediction tool 2 | 3 | ## About 4 | amPEPpy is a Python 3 application that implements a random forest classifier for predicting antimicrobial peptide sequences using the Distribution descriptor set from the [Global Protein Sequence Descriptors](https://dx.doi.org/10.1073%2Fpnas.92.19.8700). amPEPpy has improved portability, increased accuracy relative to similar methods, and includes utilities for easily training and optimizing random forest classifiers on novel training data. 5 | ## Table of Contents 6 | 7 | 8 | * [Install](#install) 9 | * [Quickstart tutorial](#quickstart-tutorial) 10 | * [Training random forest classifier](#training-random-forest-classifier) 11 | * [Classifying sequences using the trained classifier](#classifying-sequences-using-the-trained-classifier) 12 | * [Optimizing the random forest classifier, calculating feature importance, and feature selection](#optimizing-the-random-forest-classifier-calculating-feature-importance-and-feature-selection) 13 | * [Optimizing the number of decision trees within the random forest classifier](#optimizing-the-number-of-decision-trees-within-the-random-forest-classifier) 14 | * [Feature importance](#feature-importance) 15 | * [Excluding features from training and classifiying](#excluding-features-from-training-and-classifiying) 16 | * [Citing](#citing) 17 | * [Appendix](#appendix) 18 | * [Global options](#global-options) 19 | * [Train](#train) 20 | * [Predict](#predict) 21 | 22 | 23 | ## Install 24 | 1. Download amPEPpy and training data using the below bash command or the zip link: 25 | ```bash 26 | git clone https://github.com/tlawrence3/amPEPpy.git 27 | ``` 28 | 2. Change into the `amPEPpy` directory 29 | ```bash 30 | cd amPEPpy 31 | ``` 32 | 3. We recommend using [anaconda](https://www.anaconda.com/products/individual) and conda enviroments for installing and using amPEPpy. For MacOSX and Linux users we recommend installing the command line tools for anaconda. Windows users don't have this option and will need to open a powershell from the anaconda GUI to install and use `amPEPpy`. The following commands will create a conda environment with all the required packages and activate it. 33 | ```bash 34 | conda create -n amPEP python=3.8 pandas numpy biopython scikit-learn 35 | conda activate amPEP 36 | ``` 37 | If anaconda is unavailable for your system or you don't want to install it you can still install amPEPpy using pip with the command below: 38 | ```bash 39 | pip install . 40 | ``` 41 | 42 | 4. Now we can install amPEPpy and test the installation with the below commands: 43 | ```bash 44 | python setup.py install 45 | ampep -h 46 | ``` 47 | 48 | ## Quickstart tutorial 49 | Here are minimal steps required to get `amPEPpy` up and running to classify protein sequences as AMP or nonAMP 50 | 51 | ### Training random forest classifier 52 | Now that amPEPpy is installed we need to train the machine-learning algorithm. To do this we need a positive dataset (AMP sequences) and a negative dataset (nonAMP) sequences in fasta format. These are located in the `training_data` folder. 53 | 54 | To train the random forest classifier with the same settings as the manuscript use the below command. 55 | ```bash 56 | ampep train -p training_data/M_model_train_AMP_sequence.numbered.fasta -n training_data/M_model_train_nonAMP_sequence.numbered.proplen.subsample.fasta --seed 2012 57 | ``` 58 | 59 | This should create a file named `amPEP.model` which contains the saved random forest classifier. The `-p` flag is the path to your fasta file containing AMP sequences, `-n` flag is the path to a fasta file containing nonAMP sequences, and `--seed` may be provided for reproducibility and was set to `2012` for all of the analyses for the manuscript. The model described in the manuscript is available in the `pretrained_models` directory. 60 | 61 | ### Classifying sequences using the trained classifier 62 | Use the below command to classify amino acid sequences in fasta format using the trained random forest. As an example we will use our positive training data. 63 | 64 | ```bash 65 | ampep predict -m amPEP.model -i training_data/M_model_train_AMP_sequence.numbered.fasta -o results.tsv --seed 2012 66 | ``` 67 | 68 | This should result in a file named `results.tsv` that contains the classification results for the positive dataset. The `-m` flag is the path to the model file created during training, `-i` flag is the path to a fasta file containing amino acid sequences to classifier, and the `-o` flag. 69 | 70 | ## Tutorial for optimizing the random forest classifier on novel training data 71 | After finishing the quickstart tutorial the steps belows will take you through the steps we used to optimize our random forest classifier. These steps can be used to optimize a new classifier on novel training data. In the example below we will use the training data from our publication, however, if you are interested in training on a larger dataset see the [ADAPTABLE](http://gec.u-picardie.fr/adaptable/cgi-bin/index-browse.sh) database. 72 | 73 | ### Optimizing the number of decision trees within the random forest classifier 74 | First we need to determine the number of decision trees produces the lowest [out-of-bag error](https://towardsdatascience.com/what-is-out-of-bag-oob-score-in-random-forest-a7fa23d710). The below command will calculate out-of-bag error for random forest classifiers containing between 23 and 175 decision trees saving the results to `oob_error_results.tsv`. 75 | ```bash 76 | ampep train --test-trees --min-trees 23 --max-trees 175 \ 77 | -p training_data/M_model_train_AMP_sequence.numbered.fasta \ 78 | -n training_data/M_model_train_nonAMP_sequence.numbered.proplen.subsample.fasta \ 79 | --seed 2012 > oob_error_results.tsv 80 | ``` 81 | 82 | After running this command you should have file in the current directory named `oob_error_results.tsv`. This file contains tab-separated values for each random forest classifier's out-of-bag error. We recommend selecting the smallest number of estimators that results in the lowest out-of-bag error. Below is an example of the `oob_error_results.tsv` file: 83 | 84 | ``` 85 | n_estimators oob_error 86 | 23 0.19033047735618114 87 | 24 0.1900244798041616 88 | 25 0.19033047735618114 89 | 26 0.18925948592411257 90 | 27 0.18665850673194617 91 | ``` 92 | 93 | ### Calculating feature importance 94 | We calculate a feature's importance by change in out-of-bag accuracy between a random forest classifier trained with all features and one trained with the feature removed. A positive value indicates that the classifier performs better when the feature is included and a negative value indicates the classifer performs better when the feature is removed. The below command will calculate the importance of each feature and write the results to `feature.importances.dropcol.oob.csv`. 95 | ```bash 96 | ampep train --feature-importance --num-trees \ 97 | -p training_data/M_model_train_AMP_sequence.numbered.fasta \ 98 | -n training_data/M_model_train_nonAMP_sequence.numbered.proplen.subsample.fasta \ 99 | --seed 2012 100 | ``` 101 | 102 | Below is an example of the results contained in `feature.importances.dropcol.oob.csv` after running the above command: 103 | 104 | ``` 105 | Feature,Importance 106 | normalized.van.der.waals.2.25,0.008720930232558155 107 | polarity.2.50,0.008108935128518957 108 | hydrophobicity.1.100,0.00795593635250913 109 | polarizability.1.25,0.0 110 | polarizability.1.0,-0.0009179926560587415 111 | ``` 112 | 113 | The result file contains two columns `Feature` which is the name of the feature from the Distribution descriptor set from the [Global Protein Sequence Descriptors](https://dx.doi.org/10.1073%2Fpnas.92.19.8700) and the second column is the difference in the out-of-bag accuracy between a random forest classifier trained with all features and one trained with this feature removed. 114 | 115 | ### Excluding features from training and classifying 116 | Based on the feature importance analysis above it appears that removing the `polarizability.1.0` feature actually improved the performance of the random forest classifier. If we want to remove this featue from training and classifying we can do this using the `--drop-features` option. This option is expecting a text file with one feature per line that will be removed during training and classification. Below is an example of the file required by the `--drop-features` option that will remove two features: 117 | 118 | ``` 119 | polarizability.1.25 120 | polarizability.1.0 121 | ``` 122 | 123 | If the above example file was named `features.remove.txt` here is the command that will train the random forest classifier removing these features: 124 | 125 | ```bash 126 | ampep train -p training_data/M_model_train_AMP_sequence.numbered.fasta \ 127 | -n training_data/M_model_train_nonAMP_sequence.numbered.proplen.subsample.fasta \ 128 | --drop-features features.remove.txt --seed 2012 129 | ``` 130 | 131 | Once the model is trained with these features removed and we want to use it to classify sequences we need to provide the `predict` command with the `-drop-features` option. Below is an example command for predicting AMP sequences with the above features removed. 132 | ```bash 133 | ampep predict -m amPEP.model -i training_data/M_model_train_AMP_sequence.numbered.fasta --drop-features features.remove.txt -o results.tsv --seed 2012 134 | ``` 135 | 136 | ## Citing 137 | ## Appendix 138 | ### Global options 139 | `-v --version` 140 | Show program's version number and exit 141 | 142 | `-h --help` 143 | Print help message and exit 144 | 145 | `--seed` 146 | Seed for random processes. This allows reproducibility of random forest training. Default is a random seed number. 147 | 148 | `-t --num-processes` 149 | Number of processor cores to use for training and classification. Default is the number of system cores reported by the OS 150 | 151 | `-d --drop-features` 152 | Text file containing list of features to drop during random forest training and classification. File must have one feature per line 153 | 154 | ### Train 155 | `-p --positive` 156 | Fasta file containing amino acid sequences of known antimicrobial peptides. 157 | 158 | `-n --negative` 159 | Fasta file containing amino acid sequences of non-antimicrobial peptides. 160 | 161 | `--test-trees` 162 | Test accuracy of random forest classifier using different numbers of classifiers evaluted using out of bag error. `--min-trees` and `--max-trees` control the range of classifiers tested. Results are printed to stdout. 163 | 164 | `--min-trees` 165 | Minimum number of classifiers within random forest classifier when evaluating out of bag error. Default is 23. 166 | 167 | `--max-trees` 168 | Minimum number of classifiers within random forest classifier when evaluating out of bag error. Default is 175. 169 | 170 | `--num-trees` 171 | Number of classifers used to train random forest classifier. Default is 160 which was shown to produce the lowest out of bag error on training data. 172 | 173 | `--feature-importance` 174 | Test feature importance using the drop feature method. Results are written to a csv file in the current directory. 175 | 176 | ### Predict 177 | `-i --input-sequences` 178 | Fasta file containing amino acid sequences to be classified. 179 | 180 | `-o --output-file` 181 | Output file to save classification results. Default is stdout. 182 | 183 | `-m --model` 184 | Random forest model to use for classification. Random forest model is trained and outputted using the train module. Default is amPEP.model in the current directory. 185 | -------------------------------------------------------------------------------- /amPEPpy/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tlawrence3/amPEPpy/85aab3428b328d9fe4744052258746d8f4ba7bf6/amPEPpy/__init__.py -------------------------------------------------------------------------------- /amPEPpy/_version.py: -------------------------------------------------------------------------------- 1 | __version__ = "1.1.0" 2 | -------------------------------------------------------------------------------- /amPEPpy/amPEP.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | import argparse 4 | import random 5 | import pickle 6 | import numpy as np 7 | import pandas as pd 8 | from datetime import date 9 | import sklearn.utils 10 | from sklearn.base import clone 11 | from sklearn.ensemble import RandomForestClassifier 12 | #from sklearn.metrics import plot_roc_curve 13 | from sklearn import metrics 14 | from Bio import SeqIO 15 | from amPEPpy._version import __version__ 16 | 17 | def main(): 18 | parser = argparse.ArgumentParser(prog="ampep", 19 | description="amPEPpy is a python implementation of the amPEP random forest approach for identifying antimicrobial peptides. In addition to the original implementation, we have included a commandline interface, improved performance to scale to genomic data, and improved feature selection procedures", 20 | epilog="Please cite Lawrence et al. (2020) AmPEPpy: Antimicrobial Peptide prediction in python and Bhadra et al. (2018) AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest.", 21 | add_help=False) 22 | parser.add_argument('-v', '--version', action='version', version="v{}".format(__version__)) 23 | parser.add_argument("--seed", type=int, default=random.SystemRandom().randint(0, 1000000), dest="seed", 24 | help="Seed for random processes. This allows reproducibility of random forest training. Default is a random seed number.") 25 | parser.add_argument("-t", "--num-processes", type=int, default=os.cpu_count(), dest="num_processes", 26 | help="Number of processor cores to use for training and classification. Default is the number of system cores reported by the OS") 27 | parser.add_argument("-d", "--drop-features", dest="drop_feature", help="Text file containing list of features to drop during random forest training and classification. File must have one feature per line") 28 | subparsers = parser.add_subparsers(help="sub-command help") 29 | 30 | parser_train = subparsers.add_parser("train", parents=[parser], help="Module to train random forest classifier to identify antimicrobial peptides from amino acid sequence data.") 31 | requiredNamed_train = parser_train.add_argument_group('required arguments') 32 | requiredNamed_train.add_argument("-p", "--positive", help="Fasta file containing amino acid sequences of known antimicrobial peptides.", required=True) 33 | requiredNamed_train.add_argument("-n", "--negative", help="Fasta file containing amino acid sequences of non-antimicrobial peptides.", required=True) 34 | parser_train.add_argument("--test-trees", action="store_true", dest="tree_test", help="Test accuracy of random forest classifier using different numbers of classifiers evaluted using out of bag error. --min-trees and --max-trees control the range of classifiers tested. Results are printed to stdout.") 35 | parser_train.add_argument("--min-trees", type=int, default=23, dest="min_tree", help="Minimum number of classifiers within random forest classifier when evaluating out of bag error. Default is 23.") 36 | parser_train.add_argument("--max-trees", type=int, default=175, dest="max_tree", help="Minimum number of classifiers within random forest classifier when evaluating out of bag error. Default is 175") 37 | parser_train.add_argument("--num-trees", type=int, default=160, dest="num_trees", help="Number of classifers used to train random forest classifier. Default is 160 which was shown to produce the lowest out of bag error on training data.") 38 | parser_train.add_argument("--feature-importance", action="store_true", dest="feature_importance", help="Test feature importance using the drop feature method. Results are written to a csv file in the current directory.") 39 | parser_train.set_defaults(func=train) 40 | 41 | parser_predict = subparsers.add_parser("predict", parents=[parser], help="Module to identify antimicrobial peptides from amino acid sequence data using the random forest classifier trained using the train module.") 42 | requiredNamed = parser_predict.add_argument_group('required arguments') 43 | parser_predict.add_argument("-m", "--model", default="amPEP.model", dest="model", help="Random forest model to use for classification. Random forest model is trained and outputted using the train module. Default is amPEP.model in the current directory.") 44 | requiredNamed.add_argument("-i", "--input-sequences", required=True, dest="seq_file", help="Fasta file containing amino acid sequences to be classified.") 45 | parser_predict.add_argument("-o", "--output-file", dest="out_file", help="Ooutput file to save classification results. Default is stdout.") 46 | parser_predict.set_defaults(func=predict) 47 | 48 | if len(sys.argv) == 1 or sys.argv[1] == "-h" or sys.argv[1] == "--help": 49 | parser.print_help(sys.stderr) 50 | sys.exit(1) 51 | else: 52 | args = parser.parse_args() 53 | args.func(args) 54 | 55 | def train(args): 56 | if args.max_tree <= args.min_tree: 57 | print(f"--max-trees ({args.max_tree}) less than or equal to --min-trees ({args.min_tree}). --max-trees must be larger than --min-trees", 58 | file=sys.stderr) 59 | sys.exit(1) 60 | 61 | try: 62 | with open(args.positive, "r") as training_positive: 63 | positive_df = score(training_positive) 64 | positive_df['classi'] = 1 65 | except FileNotFoundError: 66 | print(f"AMP positive fasta sequence file: {args.positive} not found!", file=sys.stderr) 67 | sys.exit(1) 68 | 69 | try: 70 | with open(args.negative, "r") as training_negative: 71 | negative_df = score(training_negative) 72 | negative_df['classi'] = 0 73 | except FileNotFoundError: 74 | print(f"AMP negative fasta sequence file: {args.negative} not found!", file=sys.stderr) 75 | sys.exit(1) 76 | 77 | feature_drop_list = [] 78 | if args.drop_feature: 79 | try: 80 | with open(args.drop_feature, "r") as drop_data: 81 | for feature in drop_data: 82 | feature = feature.strip() 83 | feature_drop_list.append(feature) 84 | except FileNotFoundError: 85 | print(f"Feature drop file: {args.drop_feature} not found!", file=sys.stderr) 86 | sys.exit(1) 87 | 88 | feature_drop_list.append("classi") 89 | training_df = pd.concat([positive_df, negative_df]) 90 | training_df = sklearn.utils.shuffle(training_df, random_state=args.seed) 91 | X = training_df.drop(columns=feature_drop_list) 92 | #X = training_df.drop(columns=['classi', 'polarizability.1.0']) 93 | y = training_df.classi 94 | clf = RandomForestClassifier(n_estimators=args.num_trees, oob_score=True, 95 | random_state=args.seed, 96 | n_jobs=args.num_processes) 97 | clf.fit(X, y) 98 | print(f"Out-of-bag accuracy: {clf.oob_score_}", file=sys.stderr) 99 | pred_train = np.argmax(clf.oob_decision_function_, axis=1).tolist() 100 | print(f"Out-of-bag balanced accuracy: {metrics.balanced_accuracy_score(y, pred_train)}", 101 | file=sys.stderr) 102 | #train_name = training_df.index.tolist() 103 | #rates_df = pd.DataFrame(list(zip(pred_train, train_name)), columns=["prediction", "name"]) 104 | #rates_df.to_csv("oob_classify_random.csv", index=False) 105 | if args.tree_test: 106 | min_estimators = args.min_tree 107 | max_estimators = args.max_tree 108 | print("n_estimators\toob_error\toob_balanced_error") 109 | for i in range(min_estimators, max_estimators + 1): 110 | clf_ = RandomForestClassifier(n_estimators=i, oob_score=True, 111 | random_state=args.seed, 112 | n_jobs=args.num_processes) 113 | clf_.fit(X, y) 114 | oob_error = 1 - clf_.oob_score_ 115 | pred_train = np.argmax(clf_.oob_decision_function_, axis=1).tolist() 116 | oob_balanced_accuracy = metrics.balanced_accuracy_score(y, pred_train) 117 | print(f"{i}\t{oob_error}\t{1-oob_balanced_accuracy}") 118 | 119 | if args.feature_importance: 120 | oob_dropcol_importances(clf, X, y, args.seed, args.num_processes) 121 | 122 | try: 123 | with open("amPEP.model", "wb") as model_pickle: 124 | pickle.dump(clf, model_pickle) 125 | except IOError: 126 | print("Error in writing model to file!", file=sys.stderr) 127 | 128 | def predict(args): 129 | try: 130 | with open(args.model, "rb") as model_handle: 131 | clf = pickle.load(model_handle) 132 | except FileNotFoundError: 133 | print(f"Model file: {args.model} not found!", file=sys.stderr) 134 | sys.exit(1) 135 | 136 | try: 137 | with open(args.seq_file, "r") as classify_input: 138 | classify_df = score(classify_input) 139 | except FileNotFoundError: 140 | print(f"Sequence file: {args.seq_file} not found!", file=sys.stderr) 141 | sys.exit(1) 142 | 143 | if args.out_file: 144 | classify_output = open(args.out_file, "w") 145 | 146 | feature_drop_list = [] 147 | if args.drop_feature: 148 | try: 149 | with open(args.drop_feature, "r") as drop_data: 150 | for feature in drop_data: 151 | feature = feature.strip() 152 | feature_drop_list.append(feature) 153 | except FileNotFoundError: 154 | print(f"Feature drop file: {args.drop_feature} not found!", file=sys.stderr) 155 | sys.exit(1) 156 | 157 | if feature_drop_list: 158 | classify_df = classify_df.drop(columns=feature_drop_list) 159 | id_info = classify_df.index.tolist() 160 | 161 | if args.out_file: 162 | print("probability_nonAMP\tprobability_AMP\tpredicted\tseq_id", file=classify_output) 163 | else: 164 | print("probability_nonAMP\tprobability_AMP\tpredicted\tseq_id") 165 | preds = clf.predict_proba(classify_df) 166 | for i, pred in enumerate(preds): 167 | pred_list = pred.tolist() 168 | if clf.predict(classify_df.loc[[id_info[i]]])[0] == 1: 169 | predicted = "AMP" 170 | else: 171 | predicted = "nonAMP" 172 | output_line = "{}\t{}\t{}".format("\t".join([str(y) for y in pred_list]), 173 | predicted, 174 | id_info[i]) 175 | if args.out_file: 176 | print(output_line, file=classify_output) 177 | else: 178 | print(output_line) 179 | if args.out_file: 180 | classify_output.close() 181 | 182 | def score(fasta_handle): 183 | CTD = {'hydrophobicity': {1: ['R', 'K', 'E', 'D', 'Q', 'N'], 2: ['G', 'A', 'S', 'T', 'P', 'H', 'Y'], 3: ['C', 'L', 'V', 'I', 'M', 'F', 'W']}, 184 | 'normalized.van.der.waals': {1: ['G', 'A', 'S', 'T', 'P', 'D', 'C'], 2: ['N', 'V', 'E', 'Q', 'I', 'L'], 3: ['M', 'H', 'K', 'F', 'R', 'Y', 'W']}, 185 | 'polarity': {1: ['L', 'I', 'F', 'W', 'C', 'M', 'V', 'Y'], 2: ['P', 'A', 'T', 'G', 'S'], 3: ['H', 'Q', 'R', 'K', 'N', 'E', 'D']}, 186 | 'polarizability': {1: ['G', 'A', 'S', 'D', 'T'], 2: ['C', 'P', 'N', 'V', 'E', 'Q', 'I', 'L'], 3: ['K', 'M', 'H', 'F', 'R', 'Y', 'W']}, 187 | 'charge': {1: ['K', 'R'], 2: ['A', 'N', 'C', 'Q', 'G', 'H', 'I', 'L', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V'], 3: ['D', 'E']}, 188 | 'secondary': {1: ['E', 'A', 'L', 'M', 'Q', 'K', 'R', 'H'], 2: ['V', 'I', 'Y', 'C', 'W', 'F', 'T'], 3: ['G', 'N', 'P', 'S', 'D']}, 189 | 'solvent': {1: ['A', 'L', 'F', 'C', 'G', 'I', 'V', 'W'], 2: ['R', 'K', 'Q', 'E', 'N', 'D'], 3: ['M', 'S', 'P', 'T', 'H', 'Y']}} 190 | header = [] 191 | groups = [1, 2, 3] 192 | values = [0, 25, 50, 75, 100] 193 | for AAproperty in CTD: 194 | for types in groups: 195 | for numbers in values: 196 | label = "" 197 | label = label.join("{}.{}.{}".format(AAproperty, types, numbers)) 198 | header.append(label) 199 | All_groups = [] 200 | Sequence_names = [] 201 | for sequences in SeqIO.parse(fasta_handle, "fasta"): 202 | sequence_name = sequences.id 203 | Sequence_names.append(sequence_name) 204 | sequence = str(sequences.seq) 205 | sequence=sequence.replace("*","") 206 | sequencelength=len(sequence) 207 | Sequence_group = [] 208 | for AAproperty in CTD: 209 | propvalues = "" 210 | for letter in sequence: 211 | if letter in CTD[AAproperty][1]: 212 | propvalues += "1" 213 | elif letter in CTD[AAproperty][2]: 214 | propvalues += "2" 215 | elif letter in CTD[AAproperty][3]: 216 | propvalues += "3" 217 | abpos_1 = [i for i in range(len(propvalues)) if propvalues.startswith("1", i)] 218 | abpos_1 = [x+1 for x in abpos_1] 219 | abpos_1.insert(0, "-") 220 | abpos_2 = [i for i in range(len(propvalues)) if propvalues.startswith("2", i)] 221 | abpos_2 = [x+1 for x in abpos_2] 222 | abpos_2.insert(0, "-") 223 | abpos_3 = [i for i in range(len(propvalues)) if propvalues.startswith("3", i)] 224 | abpos_3 = [x+1 for x in abpos_3] 225 | abpos_3.insert(0, "-") 226 | property_group1_length = propvalues.count("1") 227 | if property_group1_length == 0: 228 | Sequence_group.extend([0, 0, 0, 0, 0]) 229 | elif property_group1_length == 1: 230 | Sequence_group.append((abpos_1[1]/sequencelength)*100) 231 | Sequence_group.append((abpos_1[1]/sequencelength)*100) 232 | Sequence_group.append((abpos_1[1]/sequencelength)*100) 233 | Sequence_group.append((abpos_1[1]/sequencelength)*100) 234 | Sequence_group.append((abpos_1[1]/sequencelength)*100) 235 | elif property_group1_length == 2: 236 | Sequence_group.append((abpos_1[1]/sequencelength)*100) 237 | Sequence_group.append((abpos_1[1]/sequencelength)*100) 238 | Sequence_group.append((abpos_1[round((0.5*property_group1_length)-0.1)]/sequencelength)*100) 239 | Sequence_group.append((abpos_1[round((0.75*property_group1_length)-0.1)]/sequencelength)*100) 240 | Sequence_group.append((abpos_1[property_group1_length]/sequencelength)*100) 241 | else: 242 | Sequence_group.append((abpos_1[1]/sequencelength)*100) 243 | Sequence_group.append((abpos_1[round((0.25*property_group1_length)-0.1)]/sequencelength)*100) 244 | Sequence_group.append((abpos_1[round((0.5*property_group1_length)-0.1)]/sequencelength)*100) 245 | Sequence_group.append((abpos_1[round((0.75*property_group1_length)-0.1)]/sequencelength)*100) 246 | Sequence_group.append((abpos_1[property_group1_length]/sequencelength)*100) 247 | 248 | property_group2_length = propvalues.count("2") 249 | if property_group2_length == 0: 250 | Sequence_group.extend([0, 0, 0, 0, 0]) 251 | elif property_group2_length == 1: 252 | Sequence_group.append((abpos_2[1]/sequencelength)*100) 253 | Sequence_group.append((abpos_2[1]/sequencelength)*100) 254 | Sequence_group.append((abpos_2[1]/sequencelength)*100) 255 | Sequence_group.append((abpos_2[1]/sequencelength)*100) 256 | Sequence_group.append((abpos_2[1]/sequencelength)*100) 257 | elif property_group2_length == 2: 258 | Sequence_group.append((abpos_2[1]/sequencelength)*100) 259 | Sequence_group.append((abpos_2[1]/sequencelength)*100) 260 | Sequence_group.append((abpos_2[round((0.5*property_group2_length)-0.1)]/sequencelength)*100) 261 | Sequence_group.append((abpos_2[round((0.75*property_group2_length)-0.1)]/sequencelength)*100) 262 | Sequence_group.append((abpos_2[property_group2_length]/sequencelength)*100) 263 | else: 264 | Sequence_group.append((abpos_2[1]/sequencelength)*100) 265 | Sequence_group.append((abpos_2[round((0.25*property_group2_length)-0.1)]/sequencelength)*100) 266 | Sequence_group.append((abpos_2[round((0.5*property_group2_length)-0.1)]/sequencelength)*100) 267 | Sequence_group.append((abpos_2[round((0.75*property_group2_length)-0.1)]/sequencelength)*100) 268 | Sequence_group.append((abpos_2[property_group2_length]/sequencelength)*100) 269 | 270 | property_group3_length = propvalues.count("3") 271 | if property_group3_length == 0: 272 | Sequence_group.extend([0, 0, 0, 0, 0]) 273 | elif property_group3_length == 1: 274 | Sequence_group.append((abpos_3[1]/sequencelength)*100) 275 | Sequence_group.append((abpos_3[1]/sequencelength)*100) 276 | Sequence_group.append((abpos_3[1]/sequencelength)*100) 277 | Sequence_group.append((abpos_3[1]/sequencelength)*100) 278 | Sequence_group.append((abpos_3[1]/sequencelength)*100) 279 | elif property_group3_length == 2: 280 | Sequence_group.append((abpos_3[1]/sequencelength)*100) 281 | Sequence_group.append((abpos_3[1]/sequencelength)*100) 282 | Sequence_group.append((abpos_3[round((0.5*property_group3_length)-0.1)]/sequencelength)*100) 283 | Sequence_group.append((abpos_3[round((0.75*property_group3_length)-0.1)]/sequencelength)*100) 284 | Sequence_group.append((abpos_3[property_group3_length]/sequencelength)*100) 285 | else: 286 | Sequence_group.append((abpos_3[1]/sequencelength)*100) 287 | Sequence_group.append((abpos_3[round((0.25*property_group3_length)-0.1)]/sequencelength)*100) 288 | Sequence_group.append((abpos_3[round((0.5*property_group3_length)-0.1)]/sequencelength)*100) 289 | Sequence_group.append((abpos_3[round((0.75*property_group3_length)-0.1)]/sequencelength)*100) 290 | Sequence_group.append((abpos_3[property_group3_length]/sequencelength)*100) 291 | All_groups.append(Sequence_group) 292 | 293 | Property_dataframe = pd.DataFrame.from_dict(All_groups) 294 | Property_dataframe.columns = header 295 | Property_dataframe.index = Sequence_names 296 | Property_dataframe.to_csv(f'{date.today().strftime("%Y%m%d")}_scored_features.csv', index=True, index_label="sequence_name") 297 | return Property_dataframe 298 | 299 | def oob_dropcol_importances(rf, X_train, y_train, seed, n_jobs): 300 | """ 301 | Compute drop-column feature importances for scikit-learn. 302 | Given a RandomForestClassifier or RandomForestRegressor in rf 303 | and training X and y data, return a data frame with columns 304 | Feature and Importance sorted in reverse order by importance. 305 | A clone of rf is trained once to get the baseline score and then 306 | again, once per feature to compute the drop in out of bag (OOB) 307 | score. 308 | return: A data frame with Feature, Importance columns 309 | SAMPLE CODE 310 | rf = RandomForestRegressor(n_estimators=100, n_jobs=-1, oob_score=True) 311 | X_train, y_train = ..., ... 312 | rf.fit(X_train, y_train) 313 | imp = oob_dropcol_importances(rf, X_train, y_train) 314 | """ 315 | rf_ = clone(rf) 316 | rf_.random_state = seed 317 | rf_.oob_score = True 318 | rf_.n_jobs = n_jobs 319 | rf_.fit(X_train, y_train) 320 | baseline = rf_.oob_score_ 321 | imp = [] 322 | for col in X_train.columns: 323 | rf_ = clone(rf) 324 | rf_.random_state = seed 325 | rf_.oob_score = True 326 | rf_.n_jobs = n_jobs 327 | rf_.fit(X_train.drop(col, axis=1), y_train) 328 | drop_in_score = baseline - rf_.oob_score_ 329 | imp.append(drop_in_score) 330 | imp = np.array(imp) 331 | I = pd.DataFrame(data={'Feature':X_train.columns, 'Importance':imp}) 332 | I = I.set_index('Feature') 333 | I = I.sort_values('Importance', ascending=False) 334 | I.to_csv("feature.importances.dropcol.oob.csv", index=True) 335 | 336 | if __name__ == "__main__": 337 | main() 338 | -------------------------------------------------------------------------------- /images/Figure_S2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tlawrence3/amPEPpy/85aab3428b328d9fe4744052258746d8f4ba7bf6/images/Figure_S2.png -------------------------------------------------------------------------------- /paper_results/feature.importances.dropcol.oob.lengthbalanced.csv: -------------------------------------------------------------------------------- 1 | Feature,Importance 2 | normalized.van.der.waals.2.25,0.008720930232558155 3 | polarity.2.50,0.008108935128518957 4 | hydrophobicity.1.100,0.00795593635250913 5 | secondary.1.0,0.007037943696450388 6 | normalized.van.der.waals.1.50,0.006884944920440561 7 | solvent.1.50,0.00642594859241119 8 | charge.3.100,0.00596695226438182 9 | normalized.van.der.waals.3.25,0.005813953488372103 10 | secondary.2.100,0.005660954712362276 11 | polarizability.2.25,0.005507955936352449 12 | polarity.1.100,0.005354957160342733 13 | hydrophobicity.1.50,0.005354957160342733 14 | polarity.1.0,0.005201958384332905 15 | normalized.van.der.waals.3.75,0.005201958384332905 16 | secondary.1.75,0.005201958384332905 17 | normalized.van.der.waals.1.75,0.005201958384332905 18 | polarity.2.75,0.005048959608323078 19 | solvent.1.0,0.005048959608323078 20 | secondary.3.100,0.005048959608323078 21 | hydrophobicity.1.25,0.005048959608323078 22 | solvent.1.25,0.005048959608323078 23 | solvent.3.75,0.005048959608323078 24 | hydrophobicity.3.75,0.004895960832313362 25 | polarity.2.0,0.004895960832313362 26 | polarizability.3.0,0.004895960832313362 27 | charge.2.100,0.004895960832313362 28 | charge.3.25,0.004742962056303535 29 | charge.1.100,0.004742962056303535 30 | polarity.3.50,0.004589963280293707 31 | polarizability.3.50,0.004589963280293707 32 | charge.2.75,0.004589963280293707 33 | hydrophobicity.2.0,0.004589963280293707 34 | charge.1.75,0.004436964504283991 35 | solvent.1.100,0.004436964504283991 36 | polarity.1.75,0.004436964504283991 37 | charge.3.0,0.004283965728274164 38 | polarizability.1.75,0.004283965728274164 39 | secondary.1.25,0.004283965728274164 40 | normalized.van.der.waals.1.0,0.004130966952264337 41 | charge.2.25,0.004130966952264337 42 | charge.1.25,0.003977968176254509 43 | secondary.3.75,0.003977968176254509 44 | solvent.2.75,0.003977968176254509 45 | hydrophobicity.3.50,0.003977968176254509 46 | polarity.2.100,0.003977968176254509 47 | secondary.2.0,0.003824969400244793 48 | polarity.3.0,0.003824969400244793 49 | polarizability.3.25,0.003824969400244793 50 | polarizability.1.100,0.003824969400244793 51 | secondary.3.25,0.003824969400244793 52 | normalized.van.der.waals.2.50,0.003824969400244793 53 | polarizability.2.75,0.003671970624234966 54 | charge.2.0,0.003671970624234966 55 | polarizability.2.50,0.003671970624234966 56 | secondary.3.0,0.003671970624234966 57 | solvent.3.50,0.003671970624234966 58 | polarizability.2.100,0.0035189718482251386 59 | secondary.2.25,0.0035189718482251386 60 | normalized.van.der.waals.2.100,0.0035189718482251386 61 | solvent.1.75,0.0035189718482251386 62 | hydrophobicity.2.100,0.0035189718482251386 63 | solvent.3.25,0.0035189718482251386 64 | solvent.2.25,0.0033659730722154224 65 | secondary.2.50,0.0033659730722154224 66 | solvent.3.100,0.0033659730722154224 67 | normalized.van.der.waals.3.100,0.0033659730722154224 68 | hydrophobicity.2.75,0.0033659730722154224 69 | solvent.2.100,0.003212974296205595 70 | polarity.1.25,0.003212974296205595 71 | secondary.3.50,0.003212974296205595 72 | normalized.van.der.waals.3.50,0.003212974296205595 73 | hydrophobicity.3.25,0.003212974296205595 74 | solvent.2.50,0.003059975520195768 75 | polarity.2.25,0.003059975520195768 76 | secondary.2.75,0.0029069767441860517 77 | normalized.van.der.waals.3.0,0.0027539779681762244 78 | polarity.3.25,0.0027539779681762244 79 | polarizability.2.0,0.002600979192166397 80 | normalized.van.der.waals.1.100,0.002600979192166397 81 | secondary.1.50,0.002600979192166397 82 | hydrophobicity.2.25,0.002600979192166397 83 | charge.1.0,0.002600979192166397 84 | charge.3.75,0.002447980416156681 85 | solvent.3.0,0.002447980416156681 86 | normalized.van.der.waals.2.0,0.002447980416156681 87 | hydrophobicity.1.75,0.002447980416156681 88 | hydrophobicity.1.0,0.0022949816401468537 89 | polarity.3.75,0.0022949816401468537 90 | charge.1.50,0.0021419828641370264 91 | hydrophobicity.3.100,0.0021419828641370264 92 | charge.3.50,0.00198898408812731 93 | polarizability.3.100,0.00198898408812731 94 | hydrophobicity.2.50,0.00198898408812731 95 | polarizability.3.75,0.00198898408812731 96 | solvent.2.0,0.001835985312117483 97 | polarizability.1.50,0.0016829865361076557 98 | charge.2.50,0.0015299877600979395 99 | hydrophobicity.3.0,0.0015299877600979395 100 | normalized.van.der.waals.2.75,0.0015299877600979395 101 | normalized.van.der.waals.1.25,0.0010709914320685687 102 | polarity.3.100,0.0010709914320685687 103 | polarity.1.50,0.0007649938800489142 104 | secondary.1.100,0.0003059975520195435 105 | polarizability.1.25,0.0 106 | polarizability.1.0,-0.0009179926560587415 107 | -------------------------------------------------------------------------------- /paper_results/feature_drop.lengthbalanced.txt: -------------------------------------------------------------------------------- 1 | polarizability.1.0 2 | -------------------------------------------------------------------------------- /paper_results/oob_count.balanced.seed5.txt: -------------------------------------------------------------------------------- 1 | 0.9622093023255814 2 | n_estimators oob_error 3 | 23 0.04268665850673192 4 | 24 0.04146266829865364 5 | 25 0.04192166462668301 6 | 26 0.041615667074663354 7 | 27 0.04130966952264381 8 | 28 0.04085067319461444 9 | 29 0.03977968176254587 10 | 30 0.0399326805385557 11 | 31 0.0399326805385557 12 | 32 0.0399326805385557 13 | 33 0.039626682986536155 14 | 34 0.039626682986536155 15 | 35 0.039626682986536155 16 | 36 0.03977968176254587 17 | 37 0.04023867809057524 18 | 38 0.0399326805385557 19 | 39 0.03977968176254587 20 | 40 0.0399326805385557 21 | 41 0.0399326805385557 22 | 42 0.04023867809057524 23 | 43 0.03947368421052633 24 | 44 0.039167686658506784 25 | 45 0.03901468788249696 26 | 46 0.03840269277845776 27 | 47 0.038555691554467586 28 | 48 0.038096695226438215 29 | 49 0.038096695226438215 30 | 50 0.038096695226438215 31 | 51 0.03886168910648713 32 | 52 0.03824969400244793 33 | 53 0.03794369645042839 34 | 54 0.038555691554467586 35 | 55 0.03824969400244793 36 | 56 0.038555691554467586 37 | 57 0.03901468788249696 38 | 58 0.038555691554467586 39 | 59 0.038555691554467586 40 | 60 0.0387086903304773 41 | 61 0.03901468788249696 42 | 62 0.03886168910648713 43 | 63 0.03886168910648713 44 | 64 0.039626682986536155 45 | 65 0.039626682986536155 46 | 66 0.0393206854345165 47 | 67 0.0393206854345165 48 | 68 0.0387086903304773 49 | 69 0.03886168910648713 50 | 70 0.03886168910648713 51 | 71 0.039167686658506784 52 | 72 0.03886168910648713 53 | 73 0.03901468788249696 54 | 74 0.0393206854345165 55 | 75 0.03840269277845776 56 | 76 0.0393206854345165 57 | 77 0.039167686658506784 58 | 78 0.03947368421052633 59 | 79 0.0393206854345165 60 | 80 0.039167686658506784 61 | 81 0.03886168910648713 62 | 82 0.038555691554467586 63 | 83 0.03824969400244793 64 | 84 0.038096695226438215 65 | 85 0.0387086903304773 66 | 86 0.0387086903304773 67 | 87 0.03886168910648713 68 | 88 0.03886168910648713 69 | 89 0.03886168910648713 70 | 90 0.03886168910648713 71 | 91 0.03824969400244793 72 | 92 0.038555691554467586 73 | 93 0.038555691554467586 74 | 94 0.038096695226438215 75 | 95 0.038096695226438215 76 | 96 0.03840269277845776 77 | 97 0.03824969400244793 78 | 98 0.03840269277845776 79 | 99 0.038555691554467586 80 | 100 0.038555691554467586 81 | 101 0.03824969400244793 82 | 102 0.03779069767441856 83 | 103 0.03794369645042839 84 | 104 0.037637698898408845 85 | 105 0.03733170134638919 86 | 106 0.03733170134638919 87 | 107 0.03779069767441856 88 | 108 0.037637698898408845 89 | 109 0.03748470012239902 90 | 110 0.037637698898408845 91 | 111 0.03733170134638919 92 | 112 0.03748470012239902 93 | 113 0.03748470012239902 94 | 114 0.03748470012239902 95 | 115 0.03748470012239902 96 | 116 0.037178702570379474 97 | 117 0.03702570379436965 98 | 118 0.03702570379436965 99 | 119 0.03702570379436965 100 | 120 0.03702570379436965 101 | 121 0.0367197062423501 102 | 122 0.036566707466340276 103 | 123 0.0367197062423501 104 | 124 0.036566707466340276 105 | 125 0.0367197062423501 106 | 126 0.0367197062423501 107 | 127 0.03641370869033045 108 | 128 0.036107711138310905 109 | 129 0.036566707466340276 110 | 130 0.036566707466340276 111 | 131 0.03687270501835982 112 | 132 0.037178702570379474 113 | 133 0.03687270501835982 114 | 134 0.03641370869033045 115 | 135 0.036107711138310905 116 | 136 0.036566707466340276 117 | 137 0.0367197062423501 118 | 138 0.0367197062423501 119 | 139 0.0367197062423501 120 | 140 0.036566707466340276 121 | 141 0.0367197062423501 122 | 142 0.036566707466340276 123 | 143 0.03687270501835982 124 | 144 0.03687270501835982 125 | 145 0.0367197062423501 126 | 146 0.03702570379436965 127 | 147 0.037178702570379474 128 | 148 0.037178702570379474 129 | 149 0.03733170134638919 130 | 150 0.03748470012239902 131 | 151 0.03733170134638919 132 | 152 0.03779069767441856 133 | 153 0.03779069767441856 134 | 154 0.03779069767441856 135 | 155 0.038096695226438215 136 | 156 0.037637698898408845 137 | 157 0.037637698898408845 138 | 158 0.037178702570379474 139 | 159 0.03779069767441856 140 | 160 0.03779069767441856 141 | 161 0.037637698898408845 142 | 162 0.03779069767441856 143 | 163 0.03794369645042839 144 | 164 0.037637698898408845 145 | 165 0.03779069767441856 146 | 166 0.038096695226438215 147 | 167 0.03779069767441856 148 | 168 0.038096695226438215 149 | 169 0.03779069767441856 150 | 170 0.03779069767441856 151 | 171 0.037637698898408845 152 | 172 0.037637698898408845 153 | 173 0.03779069767441856 154 | 174 0.037637698898408845 155 | 175 0.03779069767441856 156 | -------------------------------------------------------------------------------- /paper_results/oob_prop.balanced.seed2012.txt: -------------------------------------------------------------------------------- 1 | 0.8369033047735618 2 | n_estimators oob_error 3 | 23 0.19033047735618114 4 | 24 0.1900244798041616 5 | 25 0.19033047735618114 6 | 26 0.18925948592411257 7 | 27 0.18665850673194617 8 | 28 0.1868115055079559 9 | 29 0.18589351285189715 10 | 30 0.18574051407588743 11 | 31 0.18589351285189715 12 | 32 0.18405752753977966 13 | 33 0.18222154222766218 14 | 34 0.18130354957160344 15 | 35 0.1803855569155447 16 | 36 0.17992656058751533 17 | 37 0.17962056303549567 18 | 38 0.17885556915544676 19 | 39 0.17900856793145659 20 | 40 0.17870257037943693 21 | 41 0.17885556915544676 22 | 42 0.17793757649938802 23 | 43 0.17671358629130962 24 | 44 0.17610159118727053 25 | 45 0.1759485924112607 26 | 46 0.17487760097919214 27 | 47 0.17640758873929008 28 | 48 0.17503059975520197 29 | 49 0.17625458996328025 30 | 50 0.17579559363525088 31 | 51 0.17487760097919214 32 | 52 0.17579559363525088 33 | 53 0.1751835985312118 34 | 54 0.1739596083231334 35 | 55 0.17350061199510403 36 | 56 0.17288861689106483 37 | 57 0.17472460220318242 38 | 58 0.17472460220318242 39 | 59 0.1733476132190942 40 | 60 0.17181762545899637 41 | 61 0.17151162790697672 42 | 62 0.17089963280293763 43 | 63 0.16998164014687878 44 | 64 0.1687576499388005 45 | 65 0.16998164014687878 46 | 66 0.1693696450428397 47 | 67 0.1693696450428397 48 | 68 0.16799265605875158 49 | 69 0.1687576499388005 50 | 70 0.16799265605875158 51 | 71 0.16845165238678095 52 | 72 0.1681456548347613 53 | 73 0.16998164014687878 54 | 74 0.16967564259485923 55 | 75 0.1693696450428397 56 | 76 0.16998164014687878 57 | 77 0.16891064871481032 58 | 78 0.16860465116279066 59 | 79 0.16906364749082003 60 | 80 0.16845165238678095 61 | 81 0.16860465116279066 62 | 82 0.16845165238678095 63 | 83 0.16860465116279066 64 | 84 0.16921664626682986 65 | 85 0.16967564259485923 66 | 86 0.16891064871481032 67 | 87 0.16998164014687878 68 | 88 0.1695226438188494 69 | 89 0.16891064871481032 70 | 90 0.16783965728274175 71 | 91 0.1681456548347613 72 | 92 0.1681456548347613 73 | 93 0.16783965728274175 74 | 94 0.16860465116279066 75 | 95 0.1687576499388005 76 | 96 0.1687576499388005 77 | 97 0.16860465116279066 78 | 98 0.1687576499388005 79 | 99 0.16738066095471238 80 | 100 0.16860465116279066 81 | 101 0.16860465116279066 82 | 102 0.16829865361077112 83 | 103 0.16799265605875158 84 | 104 0.1687576499388005 85 | 105 0.1681456548347613 86 | 106 0.16891064871481032 87 | 107 0.16921664626682986 88 | 108 0.16967564259485923 89 | 109 0.1695226438188494 90 | 110 0.16891064871481032 91 | 111 0.16829865361077112 92 | 112 0.16860465116279066 93 | 113 0.16799265605875158 94 | 114 0.16829865361077112 95 | 115 0.16845165238678095 96 | 116 0.1681456548347613 97 | 117 0.16845165238678095 98 | 118 0.16783965728274175 99 | 119 0.1675336597307222 100 | 120 0.1675336597307222 101 | 121 0.1675336597307222 102 | 122 0.16722766217870255 103 | 123 0.16783965728274175 104 | 124 0.16783965728274175 105 | 125 0.16799265605875158 106 | 126 0.16799265605875158 107 | 127 0.1675336597307222 108 | 128 0.16722766217870255 109 | 129 0.16738066095471238 110 | 130 0.16661566707466335 111 | 131 0.16646266829865364 112 | 132 0.1655446756425949 113 | 133 0.16615667074663398 114 | 134 0.16477968176254587 115 | 135 0.16508567931456553 116 | 136 0.16462668298653615 117 | 137 0.16447368421052633 118 | 138 0.16508567931456553 119 | 139 0.16523867809057524 120 | 140 0.16539167686658507 121 | 141 0.1655446756425949 122 | 142 0.16523867809057524 123 | 143 0.16539167686658507 124 | 144 0.16539167686658507 125 | 145 0.1655446756425949 126 | 146 0.16477968176254587 127 | 147 0.16508567931456553 128 | 148 0.16508567931456553 129 | 149 0.16523867809057524 130 | 150 0.16508567931456553 131 | 151 0.16462668298653615 132 | 152 0.16477968176254587 133 | 153 0.1637086903304773 134 | 154 0.16416768665850678 135 | 155 0.16355569155446759 136 | 156 0.1637086903304773 137 | 157 0.1637086903304773 138 | 158 0.1637086903304773 139 | 159 0.1643206854345165 140 | 160 0.16309669522643822 141 | 161 0.16324969400244793 142 | 162 0.16477968176254587 143 | 163 0.16508567931456553 144 | 164 0.1637086903304773 145 | 165 0.16416768665850678 146 | 166 0.16386168910648713 147 | 167 0.16447368421052633 148 | 168 0.16447368421052633 149 | 169 0.16523867809057524 150 | 170 0.16508567931456553 151 | 171 0.16508567931456553 152 | 172 0.16508567931456553 153 | 173 0.1649326805385557 154 | 174 0.1643206854345165 155 | 175 0.16477968176254587 156 | -------------------------------------------------------------------------------- /pretrained_models/amPEP.model: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tlawrence3/amPEPpy/85aab3428b328d9fe4744052258746d8f4ba7bf6/pretrained_models/amPEP.model -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy >= 1.19.0 2 | scikit-learn >= 1.0.0 3 | pandas >= 1.0.5 4 | biopython >= 1.77 5 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import re 2 | import os 3 | from setuptools import setup, Extension 4 | from codecs import open 5 | from os import path 6 | 7 | version_file = open("amPEPpy/_version.py", "r").read() 8 | version_match = re.match(r"^__version__ = ['\"]([^'\"]*)['\"]", version_file) 9 | if (version_match): 10 | version = version_match.group(1) 11 | else: 12 | raise RuntimeError("Unable to find version string in _version.py") 13 | 14 | here = path.abspath(path.dirname(__file__)) 15 | # Get the long description from the README file 16 | with open(path.join(here, 'README.md'), encoding='utf-8') as f: 17 | long_description = f.read() 18 | 19 | setup(name = "amPEPpy", 20 | setup_requires=['pytest-runner'], 21 | tests_require=['pytest'], 22 | install_requires=['biopython', 'numpy', 'scikit-learn', 'pandas'], 23 | packages = ["amPEPpy"], 24 | python_requires='~=3.5', 25 | entry_points = { 26 | "console_scripts": ['ampep = amPEPpy.amPEP:main']}, 27 | version = version, 28 | author="Dana L. Carper, Travis J. Lawrence, Alyssa A. Carrell, David J. Weston", 29 | author_email="", 30 | description = "amPEP ...", 31 | long_description=long_description, 32 | license='GPLv3', 33 | url = "", 34 | classifiers=['Development Status :: 4 - Beta', 35 | 'Environment :: Console', 36 | 'Intended Audience :: Science/Research', 37 | 'License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)', 38 | 'Natural Language :: English', 39 | 'Operating System :: MacOS :: MacOS X', 40 | 'Operating System :: POSIX :: Linux', 41 | 'Programming Language :: Python :: 3.6', 42 | 'Programming Language :: Python :: 3.7', 43 | 'Programming Language :: Python :: 3.8', 44 | 'Programming Language :: Python :: 3.9', 45 | 'Programming Language :: Python :: 3.10', 46 | 'Programming Language :: Python :: Implementation :: CPython', 47 | 'Topic :: Scientific/Engineering :: Bio-Informatics'],) 48 | -------------------------------------------------------------------------------- /training_data/Subsampled_community_taxa.txt: -------------------------------------------------------------------------------- 1 | ID length range 2 | nonAMP122021 228 >200 3 | nonAMP86767 245 >200 4 | nonAMP117409 253 >200 5 | nonAMP115056 227 >200 6 | nonAMP135561 231 >200 7 | nonAMP119174 215 >200 8 | nonAMP46828 204 >200 9 | nonAMP39516 211 >200 10 | nonAMP115367 224 >200 11 | nonAMP71371 208 >200 12 | nonAMP161586 217 >200 13 | nonAMP75649 235 >200 14 | nonAMP43619 249 >200 15 | nonAMP117774 208 >200 16 | nonAMP35938 237 >200 17 | nonAMP43011 248 >200 18 | nonAMP141568 240 >200 19 | nonAMP76267 243 >200 20 | nonAMP36919 220 >200 21 | nonAMP145355 254 >200 22 | nonAMP34254 219 >200 23 | nonAMP12121 229 >200 24 | nonAMP73841 225 >200 25 | nonAMP127074 213 >200 26 | nonAMP104936 223 >200 27 | nonAMP125441 209 >200 28 | nonAMP117685 227 >200 29 | nonAMP105498 231 >200 30 | nonAMP151426 227 >200 31 | nonAMP58340 201 >200 32 | nonAMP43649 209 >200 33 | nonAMP13194 225 >200 34 | nonAMP19869 250 >200 35 | nonAMP12643 231 >200 36 | nonAMP147748 205 >200 37 | nonAMP86719 223 >200 38 | nonAMP146759 229 >200 39 | nonAMP60048 207 >200 40 | nonAMP97792 201 >200 41 | nonAMP51117 208 >200 42 | nonAMP41848 205 >200 43 | nonAMP59184 239 >200 44 | nonAMP39570 212 >200 45 | nonAMP14774 245 >200 46 | nonAMP28457 238 >200 47 | nonAMP117568 254 >200 48 | nonAMP154265 244 >200 49 | nonAMP128624 221 >200 50 | nonAMP79578 242 >200 51 | nonAMP18319 202 >200 52 | nonAMP63861 230 >200 53 | nonAMP99959 219 >200 54 | nonAMP48758 242 >200 55 | nonAMP42104 229 >200 56 | nonAMP85711 207 >200 57 | nonAMP36634 247 >200 58 | nonAMP74718 241 >200 59 | nonAMP1203 252 >200 60 | nonAMP94140 208 >200 61 | nonAMP119642 217 >200 62 | nonAMP121737 231 >200 63 | nonAMP106046 220 >200 64 | nonAMP145727 203 >200 65 | nonAMP94955 213 >200 66 | nonAMP41157 223 >200 67 | nonAMP112990 239 >200 68 | nonAMP36618 207 >200 69 | nonAMP161611 231 >200 70 | nonAMP131118 225 >200 71 | nonAMP166551 247 >200 72 | nonAMP61192 230 >200 73 | nonAMP78636 222 >200 74 | nonAMP79211 244 >200 75 | nonAMP125701 226 >200 76 | nonAMP15425 241 >200 77 | nonAMP144430 214 >200 78 | nonAMP115318 236 >200 79 | nonAMP117169 230 >200 80 | nonAMP31460 230 >200 81 | nonAMP146344 207 >200 82 | nonAMP106415 203 >200 83 | nonAMP44538 210 >200 84 | nonAMP16961 211 >200 85 | nonAMP43014 245 >200 86 | nonAMP38178 227 >200 87 | nonAMP49486 254 >200 88 | nonAMP135811 204 >200 89 | nonAMP58281 213 >200 90 | nonAMP153998 216 >200 91 | nonAMP22701 213 >200 92 | nonAMP16904 226 >200 93 | nonAMP76524 218 >200 94 | nonAMP27335 201 >200 95 | nonAMP38255 227 >200 96 | nonAMP153876 216 >200 97 | nonAMP151674 217 >200 98 | nonAMP103792 230 >200 99 | nonAMP92216 253 >200 100 | nonAMP146409 202 >200 101 | nonAMP21166 201 >200 102 | nonAMP71510 224 >200 103 | nonAMP61493 30 0-50 104 | nonAMP12580 38 0-50 105 | nonAMP149518 26 0-50 106 | nonAMP43389 12 0-50 107 | nonAMP145034 43 0-50 108 | nonAMP68701 38 0-50 109 | nonAMP50819 26 0-50 110 | nonAMP86042 37 0-50 111 | nonAMP3199 45 0-50 112 | nonAMP59084 49 0-50 113 | nonAMP110538 24 0-50 114 | nonAMP135459 44 0-50 115 | nonAMP115750 46 0-50 116 | nonAMP54141 11 0-50 117 | nonAMP86607 6 0-50 118 | nonAMP46506 23 0-50 119 | nonAMP118665 48 0-50 120 | nonAMP138999 37 0-50 121 | nonAMP145266 49 0-50 122 | nonAMP117988 37 0-50 123 | nonAMP126806 49 0-50 124 | nonAMP24573 44 0-50 125 | nonAMP2758 45 0-50 126 | nonAMP14461 15 0-50 127 | nonAMP160876 32 0-50 128 | nonAMP132301 24 0-50 129 | nonAMP22974 47 0-50 130 | nonAMP56140 26 0-50 131 | nonAMP79539 39 0-50 132 | nonAMP140777 50 0-50 133 | nonAMP43138 21 0-50 134 | nonAMP81749 35 0-50 135 | nonAMP35389 38 0-50 136 | nonAMP92805 47 0-50 137 | nonAMP64236 19 0-50 138 | nonAMP58255 9 0-50 139 | nonAMP1156 32 0-50 140 | nonAMP18105 23 0-50 141 | nonAMP65023 28 0-50 142 | nonAMP59903 9 0-50 143 | nonAMP115850 8 0-50 144 | nonAMP25528 34 0-50 145 | nonAMP53252 24 0-50 146 | nonAMP33985 34 0-50 147 | nonAMP16273 30 0-50 148 | nonAMP153752 15 0-50 149 | nonAMP78074 17 0-50 150 | nonAMP149876 18 0-50 151 | nonAMP80443 11 0-50 152 | nonAMP75930 37 0-50 153 | nonAMP33580 20 0-50 154 | nonAMP143618 33 0-50 155 | nonAMP84995 14 0-50 156 | nonAMP159912 35 0-50 157 | nonAMP112087 37 0-50 158 | nonAMP66126 31 0-50 159 | nonAMP157930 31 0-50 160 | nonAMP161274 35 0-50 161 | nonAMP45645 44 0-50 162 | nonAMP20899 44 0-50 163 | nonAMP84443 36 0-50 164 | nonAMP15938 21 0-50 165 | nonAMP136101 44 0-50 166 | nonAMP93204 19 0-50 167 | nonAMP125598 47 0-50 168 | nonAMP24701 11 0-50 169 | nonAMP56089 48 0-50 170 | nonAMP157865 17 0-50 171 | nonAMP17448 30 0-50 172 | nonAMP151145 10 0-50 173 | nonAMP107467 44 0-50 174 | nonAMP53039 37 0-50 175 | nonAMP81008 47 0-50 176 | nonAMP49332 8 0-50 177 | nonAMP103056 18 0-50 178 | nonAMP109864 36 0-50 179 | nonAMP125014 50 0-50 180 | nonAMP162009 36 0-50 181 | nonAMP158468 47 0-50 182 | nonAMP141471 11 0-50 183 | nonAMP166736 43 0-50 184 | nonAMP158670 41 0-50 185 | nonAMP18942 38 0-50 186 | nonAMP95084 19 0-50 187 | nonAMP83122 27 0-50 188 | nonAMP125200 49 0-50 189 | nonAMP142277 30 0-50 190 | nonAMP57662 40 0-50 191 | nonAMP58193 27 0-50 192 | nonAMP39638 37 0-50 193 | nonAMP106061 24 0-50 194 | nonAMP128289 11 0-50 195 | nonAMP138849 11 0-50 196 | nonAMP45802 49 0-50 197 | nonAMP79762 25 0-50 198 | nonAMP30087 37 0-50 199 | nonAMP39692 9 0-50 200 | nonAMP60203 15 0-50 201 | nonAMP96651 24 0-50 202 | nonAMP120393 47 0-50 203 | nonAMP153620 43 0-50 204 | nonAMP41852 40 0-50 205 | nonAMP69318 28 0-50 206 | nonAMP662 11 0-50 207 | nonAMP108800 36 0-50 208 | nonAMP79790 9 0-50 209 | nonAMP163061 10 0-50 210 | nonAMP63745 21 0-50 211 | nonAMP103770 24 0-50 212 | nonAMP98415 36 0-50 213 | nonAMP10870 11 0-50 214 | nonAMP36390 35 0-50 215 | nonAMP78103 9 0-50 216 | nonAMP68692 37 0-50 217 | nonAMP35485 33 0-50 218 | nonAMP40685 44 0-50 219 | nonAMP19577 47 0-50 220 | nonAMP51876 26 0-50 221 | nonAMP166117 15 0-50 222 | nonAMP144778 25 0-50 223 | nonAMP45801 24 0-50 224 | nonAMP120341 40 0-50 225 | nonAMP158703 49 0-50 226 | nonAMP7703 37 0-50 227 | nonAMP41847 35 0-50 228 | nonAMP64591 44 0-50 229 | nonAMP161556 49 0-50 230 | nonAMP43140 19 0-50 231 | nonAMP40578 26 0-50 232 | nonAMP160509 16 0-50 233 | nonAMP32763 10 0-50 234 | nonAMP38730 12 0-50 235 | nonAMP55028 37 0-50 236 | nonAMP81471 50 0-50 237 | nonAMP72840 40 0-50 238 | nonAMP18514 38 0-50 239 | nonAMP156645 22 0-50 240 | nonAMP75411 37 0-50 241 | nonAMP88520 20 0-50 242 | nonAMP80574 27 0-50 243 | nonAMP81846 44 0-50 244 | nonAMP105958 44 0-50 245 | nonAMP155000 13 0-50 246 | nonAMP70192 47 0-50 247 | nonAMP159613 37 0-50 248 | nonAMP36269 31 0-50 249 | nonAMP72347 11 0-50 250 | nonAMP100312 31 0-50 251 | nonAMP68482 12 0-50 252 | nonAMP65446 30 0-50 253 | nonAMP158397 39 0-50 254 | nonAMP152262 44 0-50 255 | nonAMP51214 37 0-50 256 | nonAMP22094 38 0-50 257 | nonAMP135636 23 0-50 258 | nonAMP5073 37 0-50 259 | nonAMP64201 28 0-50 260 | nonAMP88131 34 0-50 261 | nonAMP12511 18 0-50 262 | nonAMP90486 48 0-50 263 | nonAMP29501 43 0-50 264 | nonAMP158629 49 0-50 265 | nonAMP95969 46 0-50 266 | nonAMP42386 28 0-50 267 | nonAMP74668 24 0-50 268 | nonAMP127218 31 0-50 269 | nonAMP10079 20 0-50 270 | nonAMP68656 36 0-50 271 | nonAMP120346 8 0-50 272 | nonAMP77161 17 0-50 273 | nonAMP60956 38 0-50 274 | nonAMP74784 15 0-50 275 | nonAMP48980 47 0-50 276 | nonAMP105346 44 0-50 277 | nonAMP90651 40 0-50 278 | nonAMP149432 14 0-50 279 | nonAMP54555 39 0-50 280 | nonAMP61269 35 0-50 281 | nonAMP92937 39 0-50 282 | nonAMP143401 8 0-50 283 | nonAMP70825 31 0-50 284 | nonAMP99441 31 0-50 285 | nonAMP75771 38 0-50 286 | nonAMP68329 29 0-50 287 | nonAMP93142 15 0-50 288 | nonAMP84251 8 0-50 289 | nonAMP30076 20 0-50 290 | nonAMP1351 46 0-50 291 | nonAMP139566 37 0-50 292 | nonAMP148150 41 0-50 293 | nonAMP142225 25 0-50 294 | nonAMP109396 30 0-50 295 | nonAMP12751 28 0-50 296 | nonAMP29909 8 0-50 297 | nonAMP129011 40 0-50 298 | nonAMP43137 43 0-50 299 | nonAMP22359 49 0-50 300 | nonAMP126095 31 0-50 301 | nonAMP99167 22 0-50 302 | nonAMP46987 24 0-50 303 | nonAMP102051 20 0-50 304 | nonAMP166509 22 0-50 305 | nonAMP88976 49 0-50 306 | nonAMP49794 37 0-50 307 | nonAMP81210 38 0-50 308 | nonAMP125388 17 0-50 309 | nonAMP40236 32 0-50 310 | nonAMP121133 37 0-50 311 | nonAMP136625 28 0-50 312 | nonAMP146831 14 0-50 313 | nonAMP113315 31 0-50 314 | nonAMP92812 6 0-50 315 | nonAMP36379 10 0-50 316 | nonAMP26011 37 0-50 317 | nonAMP27770 42 0-50 318 | nonAMP126659 43 0-50 319 | nonAMP78822 49 0-50 320 | nonAMP8817 30 0-50 321 | nonAMP114258 9 0-50 322 | nonAMP132516 38 0-50 323 | nonAMP124863 15 0-50 324 | nonAMP103604 50 0-50 325 | nonAMP84459 24 0-50 326 | nonAMP51266 39 0-50 327 | nonAMP14494 22 0-50 328 | nonAMP80728 29 0-50 329 | nonAMP145586 30 0-50 330 | nonAMP106130 26 0-50 331 | nonAMP142376 12 0-50 332 | nonAMP47560 36 0-50 333 | nonAMP102709 39 0-50 334 | nonAMP72501 17 0-50 335 | nonAMP31174 17 0-50 336 | nonAMP17865 34 0-50 337 | nonAMP35531 14 0-50 338 | nonAMP87022 18 0-50 339 | nonAMP147537 49 0-50 340 | nonAMP43325 7 0-50 341 | nonAMP115632 33 0-50 342 | nonAMP66967 10 0-50 343 | nonAMP51856 35 0-50 344 | nonAMP81814 7 0-50 345 | nonAMP7491 16 0-50 346 | nonAMP23994 44 0-50 347 | nonAMP14650 43 0-50 348 | nonAMP109454 31 0-50 349 | nonAMP93995 39 0-50 350 | nonAMP78152 45 0-50 351 | nonAMP137008 44 0-50 352 | nonAMP117363 10 0-50 353 | nonAMP84975 36 0-50 354 | nonAMP89255 46 0-50 355 | nonAMP134326 35 0-50 356 | nonAMP30999 9 0-50 357 | nonAMP64850 12 0-50 358 | nonAMP111961 49 0-50 359 | nonAMP25525 40 0-50 360 | nonAMP49543 6 0-50 361 | nonAMP75444 12 0-50 362 | nonAMP120142 27 0-50 363 | nonAMP11234 13 0-50 364 | nonAMP5753 37 0-50 365 | nonAMP125586 9 0-50 366 | nonAMP10069 7 0-50 367 | nonAMP30040 34 0-50 368 | nonAMP158427 22 0-50 369 | nonAMP97772 39 0-50 370 | nonAMP112489 49 0-50 371 | nonAMP87143 34 0-50 372 | nonAMP127103 20 0-50 373 | nonAMP79698 49 0-50 374 | nonAMP84691 35 0-50 375 | nonAMP127092 46 0-50 376 | nonAMP74505 19 0-50 377 | nonAMP37479 31 0-50 378 | nonAMP164070 29 0-50 379 | nonAMP96153 41 0-50 380 | nonAMP153494 37 0-50 381 | nonAMP137747 12 0-50 382 | nonAMP69127 15 0-50 383 | nonAMP79034 38 0-50 384 | nonAMP97887 17 0-50 385 | nonAMP56372 36 0-50 386 | nonAMP90460 9 0-50 387 | nonAMP60060 9 0-50 388 | nonAMP59211 49 0-50 389 | nonAMP31829 19 0-50 390 | nonAMP8426 27 0-50 391 | nonAMP96204 13 0-50 392 | nonAMP63134 29 0-50 393 | nonAMP61520 10 0-50 394 | nonAMP47517 39 0-50 395 | nonAMP18299 43 0-50 396 | nonAMP8569 20 0-50 397 | nonAMP113219 35 0-50 398 | nonAMP148929 19 0-50 399 | nonAMP61951 37 0-50 400 | nonAMP92409 31 0-50 401 | nonAMP107776 7 0-50 402 | nonAMP24350 29 0-50 403 | nonAMP54681 37 0-50 404 | nonAMP3252 39 0-50 405 | nonAMP90642 37 0-50 406 | nonAMP144687 41 0-50 407 | nonAMP133254 16 0-50 408 | nonAMP113471 41 0-50 409 | nonAMP611 17 0-50 410 | nonAMP30632 38 0-50 411 | nonAMP161247 21 0-50 412 | nonAMP11646 13 0-50 413 | nonAMP157012 7 0-50 414 | nonAMP114750 10 0-50 415 | nonAMP98182 35 0-50 416 | nonAMP148635 34 0-50 417 | nonAMP750 9 0-50 418 | nonAMP7704 36 0-50 419 | nonAMP122629 37 0-50 420 | nonAMP89588 19 0-50 421 | nonAMP137785 38 0-50 422 | nonAMP118709 20 0-50 423 | nonAMP81457 32 0-50 424 | nonAMP21051 11 0-50 425 | nonAMP99714 14 0-50 426 | nonAMP49273 5 0-50 427 | nonAMP113399 29 0-50 428 | nonAMP117079 50 0-50 429 | nonAMP92879 37 0-50 430 | nonAMP31720 29 0-50 431 | nonAMP100732 32 0-50 432 | nonAMP107398 44 0-50 433 | nonAMP51953 24 0-50 434 | nonAMP62587 13 0-50 435 | nonAMP131654 35 0-50 436 | nonAMP102812 38 0-50 437 | nonAMP53087 39 0-50 438 | nonAMP35267 8 0-50 439 | nonAMP122225 23 0-50 440 | nonAMP65911 9 0-50 441 | nonAMP135157 37 0-50 442 | nonAMP163386 22 0-50 443 | nonAMP150153 21 0-50 444 | nonAMP79774 18 0-50 445 | nonAMP14808 34 0-50 446 | nonAMP158319 20 0-50 447 | nonAMP74411 37 0-50 448 | nonAMP1638 49 0-50 449 | nonAMP30515 43 0-50 450 | nonAMP10867 37 0-50 451 | nonAMP103766 30 0-50 452 | nonAMP87058 25 0-50 453 | nonAMP86335 48 0-50 454 | nonAMP88677 43 0-50 455 | nonAMP46533 25 0-50 456 | nonAMP12733 38 0-50 457 | nonAMP111387 13 0-50 458 | nonAMP36698 11 0-50 459 | nonAMP97606 37 0-50 460 | nonAMP31090 15 0-50 461 | nonAMP8729 38 0-50 462 | nonAMP9658 10 0-50 463 | nonAMP19825 41 0-50 464 | nonAMP79881 33 0-50 465 | nonAMP161118 14 0-50 466 | nonAMP73622 23 0-50 467 | nonAMP109794 45 0-50 468 | nonAMP91647 43 0-50 469 | nonAMP36171 40 0-50 470 | nonAMP107103 40 0-50 471 | nonAMP39715 39 0-50 472 | nonAMP96842 44 0-50 473 | nonAMP15179 45 0-50 474 | nonAMP29950 16 0-50 475 | nonAMP50950 36 0-50 476 | nonAMP60294 14 0-50 477 | nonAMP24420 13 0-50 478 | nonAMP110672 10 0-50 479 | nonAMP90058 38 0-50 480 | nonAMP97197 29 0-50 481 | nonAMP140646 33 0-50 482 | nonAMP40291 8 0-50 483 | nonAMP37346 15 0-50 484 | nonAMP131162 45 0-50 485 | nonAMP110659 49 0-50 486 | nonAMP93880 39 0-50 487 | nonAMP153023 9 0-50 488 | nonAMP92753 38 0-50 489 | nonAMP14256 46 0-50 490 | nonAMP102870 36 0-50 491 | nonAMP121831 24 0-50 492 | 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568 | nonAMP120829 20 0-50 569 | nonAMP94828 14 0-50 570 | nonAMP91314 24 0-50 571 | nonAMP126177 20 0-50 572 | nonAMP126742 12 0-50 573 | nonAMP152706 13 0-50 574 | nonAMP64113 15 0-50 575 | nonAMP135996 36 0-50 576 | nonAMP40344 32 0-50 577 | nonAMP99947 25 0-50 578 | nonAMP79563 10 0-50 579 | nonAMP1396 19 0-50 580 | nonAMP67007 18 0-50 581 | nonAMP26201 48 0-50 582 | nonAMP143740 10 0-50 583 | nonAMP133219 35 0-50 584 | nonAMP102925 37 0-50 585 | nonAMP92011 43 0-50 586 | nonAMP94427 49 0-50 587 | nonAMP64580 34 0-50 588 | nonAMP7432 44 0-50 589 | nonAMP32041 36 0-50 590 | nonAMP58912 46 0-50 591 | nonAMP97667 44 0-50 592 | nonAMP94748 7 0-50 593 | nonAMP10298 50 0-50 594 | nonAMP66233 31 0-50 595 | nonAMP155073 49 0-50 596 | nonAMP10620 35 0-50 597 | nonAMP77152 10 0-50 598 | nonAMP88804 14 0-50 599 | nonAMP86715 10 0-50 600 | nonAMP76244 46 0-50 601 | nonAMP56183 45 0-50 602 | nonAMP11504 38 0-50 603 | nonAMP31710 45 0-50 604 | nonAMP128600 16 0-50 605 | nonAMP122863 14 0-50 606 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| nonAMP128843 20 0-50 759 | nonAMP5645 39 0-50 760 | nonAMP91120 17 0-50 761 | nonAMP132504 12 0-50 762 | nonAMP8111 44 0-50 763 | nonAMP141268 29 0-50 764 | nonAMP146218 30 0-50 765 | nonAMP38629 32 0-50 766 | nonAMP69747 10 0-50 767 | nonAMP120518 47 0-50 768 | nonAMP77131 19 0-50 769 | nonAMP48159 37 0-50 770 | nonAMP141126 35 0-50 771 | nonAMP101575 38 0-50 772 | nonAMP131794 17 0-50 773 | nonAMP84688 46 0-50 774 | nonAMP34811 21 0-50 775 | nonAMP159950 49 0-50 776 | nonAMP103831 17 0-50 777 | nonAMP73047 32 0-50 778 | nonAMP7648 41 0-50 779 | nonAMP811 31 0-50 780 | nonAMP15462 11 0-50 781 | nonAMP92150 42 0-50 782 | nonAMP4381 25 0-50 783 | nonAMP58342 41 0-50 784 | nonAMP25487 48 0-50 785 | nonAMP1128 49 0-50 786 | nonAMP29066 41 0-50 787 | nonAMP97397 44 0-50 788 | nonAMP163533 37 0-50 789 | nonAMP90573 20 0-50 790 | nonAMP97737 39 0-50 791 | nonAMP21606 36 0-50 792 | nonAMP68933 31 0-50 793 | nonAMP119350 44 0-50 794 | nonAMP5335 37 0-50 795 | nonAMP1506 37 0-50 796 | 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| nonAMP3559 24 0-50 987 | nonAMP89573 14 0-50 988 | nonAMP12524 31 0-50 989 | nonAMP128586 15 0-50 990 | nonAMP38128 24 0-50 991 | nonAMP37956 31 0-50 992 | nonAMP121058 9 0-50 993 | nonAMP49276 19 0-50 994 | nonAMP91956 35 0-50 995 | nonAMP17158 45 0-50 996 | nonAMP68680 33 0-50 997 | nonAMP49086 48 0-50 998 | nonAMP8461 37 0-50 999 | nonAMP104052 30 0-50 1000 | nonAMP21587 50 0-50 1001 | nonAMP39952 15 0-50 1002 | nonAMP1356 41 0-50 1003 | nonAMP2504 10 0-50 1004 | nonAMP10986 50 0-50 1005 | nonAMP23571 44 0-50 1006 | nonAMP55896 46 0-50 1007 | nonAMP80080 15 0-50 1008 | nonAMP60044 47 0-50 1009 | nonAMP28967 34 0-50 1010 | nonAMP100893 12 0-50 1011 | nonAMP59276 30 0-50 1012 | nonAMP90445 19 0-50 1013 | nonAMP90210 20 0-50 1014 | nonAMP38924 50 0-50 1015 | nonAMP154933 12 0-50 1016 | nonAMP133438 37 0-50 1017 | nonAMP122944 11 0-50 1018 | nonAMP34180 49 0-50 1019 | nonAMP127630 34 0-50 1020 | nonAMP139882 39 0-50 1021 | nonAMP118151 32 0-50 1022 | nonAMP88574 39 0-50 1023 | 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0-50 1829 | nonAMP45116 16 0-50 1830 | nonAMP140105 42 0-50 1831 | nonAMP4391 15 0-50 1832 | nonAMP26760 43 0-50 1833 | nonAMP150930 27 0-50 1834 | nonAMP29202 38 0-50 1835 | nonAMP70620 5 0-50 1836 | nonAMP55456 25 0-50 1837 | nonAMP61474 36 0-50 1838 | nonAMP74716 11 0-50 1839 | nonAMP8427 49 0-50 1840 | nonAMP87756 44 0-50 1841 | nonAMP152398 31 0-50 1842 | nonAMP26929 5 0-50 1843 | nonAMP88871 31 0-50 1844 | nonAMP103575 31 0-50 1845 | nonAMP61448 40 0-50 1846 | nonAMP18730 12 0-50 1847 | nonAMP28939 14 0-50 1848 | nonAMP114893 41 0-50 1849 | nonAMP75595 37 0-50 1850 | nonAMP125367 15 0-50 1851 | nonAMP41461 25 0-50 1852 | nonAMP135403 31 0-50 1853 | nonAMP61383 16 0-50 1854 | nonAMP151126 12 0-50 1855 | nonAMP155121 25 0-50 1856 | nonAMP104113 21 0-50 1857 | nonAMP156519 35 0-50 1858 | nonAMP126195 7 0-50 1859 | nonAMP89419 37 0-50 1860 | nonAMP121808 41 0-50 1861 | nonAMP120758 32 0-50 1862 | nonAMP45483 37 0-50 1863 | nonAMP92118 38 0-50 1864 | nonAMP115031 19 0-50 1865 | 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1902 | nonAMP65267 37 0-50 1903 | nonAMP110757 22 0-50 1904 | nonAMP39628 20 0-50 1905 | nonAMP109538 45 0-50 1906 | nonAMP138644 39 0-50 1907 | nonAMP49883 9 0-50 1908 | nonAMP45708 33 0-50 1909 | nonAMP3826 44 0-50 1910 | nonAMP81218 19 0-50 1911 | nonAMP22049 24 0-50 1912 | nonAMP47627 32 0-50 1913 | nonAMP145249 7 0-50 1914 | nonAMP109736 28 0-50 1915 | nonAMP152686 38 0-50 1916 | nonAMP39634 33 0-50 1917 | nonAMP65178 20 0-50 1918 | nonAMP126591 39 0-50 1919 | nonAMP3356 26 0-50 1920 | nonAMP33617 15 0-50 1921 | nonAMP131528 22 0-50 1922 | nonAMP42281 42 0-50 1923 | nonAMP55683 30 0-50 1924 | nonAMP31755 37 0-50 1925 | nonAMP65570 18 0-50 1926 | nonAMP137058 39 0-50 1927 | nonAMP49880 5 0-50 1928 | nonAMP118420 37 0-50 1929 | nonAMP57415 15 0-50 1930 | nonAMP98589 9 0-50 1931 | nonAMP10336 33 0-50 1932 | nonAMP155280 13 0-50 1933 | nonAMP81095 31 0-50 1934 | nonAMP153619 44 0-50 1935 | nonAMP137599 9 0-50 1936 | nonAMP116166 50 0-50 1937 | nonAMP47368 11 0-50 1938 | nonAMP158309 15 0-50 1939 | nonAMP31818 33 0-50 1940 | nonAMP98615 41 0-50 1941 | nonAMP80131 47 0-50 1942 | nonAMP51647 7 0-50 1943 | nonAMP81553 15 0-50 1944 | nonAMP129082 13 0-50 1945 | nonAMP131442 31 0-50 1946 | nonAMP147043 13 0-50 1947 | nonAMP12199 11 0-50 1948 | nonAMP7451 8 0-50 1949 | nonAMP63123 13 0-50 1950 | nonAMP153139 27 0-50 1951 | nonAMP29156 11 0-50 1952 | nonAMP18676 39 0-50 1953 | nonAMP84035 25 0-50 1954 | nonAMP3590 38 0-50 1955 | nonAMP92784 34 0-50 1956 | nonAMP54074 45 0-50 1957 | nonAMP72150 38 0-50 1958 | nonAMP94379 29 0-50 1959 | nonAMP73507 44 0-50 1960 | nonAMP118137 25 0-50 1961 | nonAMP125233 26 0-50 1962 | nonAMP138073 20 0-50 1963 | nonAMP48830 49 0-50 1964 | nonAMP51566 12 0-50 1965 | nonAMP28833 9 0-50 1966 | nonAMP19122 46 0-50 1967 | nonAMP66166 25 0-50 1968 | nonAMP123468 50 0-50 1969 | nonAMP142966 5 0-50 1970 | nonAMP69055 26 0-50 1971 | nonAMP150095 31 0-50 1972 | nonAMP105227 11 0-50 1973 | nonAMP87445 36 0-50 1974 | nonAMP103645 39 0-50 1975 | 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0-50 2012 | nonAMP148204 28 0-50 2013 | nonAMP5635 44 0-50 2014 | nonAMP128403 20 0-50 2015 | nonAMP81842 33 0-50 2016 | nonAMP12845 31 0-50 2017 | nonAMP15795 13 0-50 2018 | nonAMP70248 29 0-50 2019 | nonAMP53225 16 0-50 2020 | nonAMP138955 12 0-50 2021 | nonAMP103593 39 0-50 2022 | nonAMP74247 23 0-50 2023 | nonAMP39187 15 0-50 2024 | nonAMP25450 46 0-50 2025 | nonAMP76617 43 0-50 2026 | nonAMP57914 23 0-50 2027 | nonAMP127470 15 0-50 2028 | nonAMP112830 25 0-50 2029 | nonAMP99338 29 0-50 2030 | nonAMP102594 12 0-50 2031 | nonAMP110355 44 0-50 2032 | nonAMP159364 38 0-50 2033 | nonAMP55294 35 0-50 2034 | nonAMP93899 19 0-50 2035 | nonAMP113938 50 0-50 2036 | nonAMP19663 12 0-50 2037 | nonAMP164897 40 0-50 2038 | nonAMP139933 49 0-50 2039 | nonAMP165270 46 0-50 2040 | nonAMP56572 23 0-50 2041 | nonAMP12236 48 0-50 2042 | nonAMP116088 38 0-50 2043 | nonAMP78140 33 0-50 2044 | nonAMP72287 46 0-50 2045 | nonAMP30259 11 0-50 2046 | nonAMP79190 5 0-50 2047 | nonAMP59657 21 0-50 2048 | 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nonAMP83324 42 0-50 2415 | nonAMP73283 25 0-50 2416 | nonAMP132119 50 0-50 2417 | nonAMP69807 36 0-50 2418 | nonAMP32547 41 0-50 2419 | nonAMP135935 35 0-50 2420 | nonAMP20232 44 0-50 2421 | nonAMP37473 42 0-50 2422 | nonAMP116854 22 0-50 2423 | nonAMP100030 38 0-50 2424 | nonAMP132820 9 0-50 2425 | nonAMP141309 34 0-50 2426 | nonAMP54876 28 0-50 2427 | nonAMP125635 9 0-50 2428 | nonAMP86727 28 0-50 2429 | nonAMP8970 31 0-50 2430 | nonAMP51993 24 0-50 2431 | nonAMP78493 13 0-50 2432 | nonAMP31131 41 0-50 2433 | nonAMP166184 31 0-50 2434 | nonAMP7883 13 0-50 2435 | nonAMP2143 22 0-50 2436 | nonAMP163080 50 0-50 2437 | nonAMP99448 45 0-50 2438 | nonAMP33970 21 0-50 2439 | nonAMP120410 50 0-50 2440 | nonAMP121551 10 0-50 2441 | nonAMP57462 21 0-50 2442 | nonAMP85401 8 0-50 2443 | nonAMP118530 11 0-50 2444 | nonAMP151848 49 0-50 2445 | nonAMP154375 45 0-50 2446 | nonAMP25341 33 0-50 2447 | nonAMP162886 30 0-50 2448 | nonAMP2056 33 0-50 2449 | nonAMP142583 8 0-50 2450 | nonAMP751 24 0-50 2451 | nonAMP69613 27 0-50 2452 | nonAMP141776 33 0-50 2453 | nonAMP43820 39 0-50 2454 | nonAMP117749 39 0-50 2455 | nonAMP48808 21 0-50 2456 | nonAMP147307 15 0-50 2457 | nonAMP9779 11 0-50 2458 | nonAMP20906 26 0-50 2459 | nonAMP4082 36 0-50 2460 | nonAMP26599 19 0-50 2461 | nonAMP53918 46 0-50 2462 | nonAMP27481 49 0-50 2463 | nonAMP140192 44 0-50 2464 | nonAMP34699 45 0-50 2465 | nonAMP70444 30 0-50 2466 | nonAMP55416 38 0-50 2467 | nonAMP74546 40 0-50 2468 | nonAMP164291 12 0-50 2469 | nonAMP2410 19 0-50 2470 | nonAMP28539 24 0-50 2471 | nonAMP93977 5 0-50 2472 | nonAMP146866 33 0-50 2473 | nonAMP9881 44 0-50 2474 | nonAMP17860 10 0-50 2475 | nonAMP103092 45 0-50 2476 | nonAMP73250 39 0-50 2477 | nonAMP61394 49 0-50 2478 | nonAMP111806 41 0-50 2479 | nonAMP39819 29 0-50 2480 | nonAMP82617 15 0-50 2481 | nonAMP13063 38 0-50 2482 | nonAMP105595 13 0-50 2483 | nonAMP74964 45 0-50 2484 | nonAMP16569 50 0-50 2485 | nonAMP67956 37 0-50 2486 | nonAMP122580 36 0-50 2487 | nonAMP49162 47 0-50 2488 | nonAMP155077 49 0-50 2489 | nonAMP78323 12 0-50 2490 | nonAMP125839 36 0-50 2491 | nonAMP124360 37 0-50 2492 | nonAMP29639 45 0-50 2493 | nonAMP48566 12 0-50 2494 | nonAMP21814 47 0-50 2495 | nonAMP97293 48 0-50 2496 | nonAMP52433 23 0-50 2497 | nonAMP12700 5 0-50 2498 | nonAMP37387 22 0-50 2499 | nonAMP153176 44 0-50 2500 | nonAMP102464 39 0-50 2501 | nonAMP14698 42 0-50 2502 | nonAMP130424 25 0-50 2503 | nonAMP162923 41 0-50 2504 | nonAMP93032 21 0-50 2505 | nonAMP105036 23 0-50 2506 | nonAMP50893 47 0-50 2507 | nonAMP85679 40 0-50 2508 | nonAMP41986 34 0-50 2509 | nonAMP165081 38 0-50 2510 | nonAMP111999 39 0-50 2511 | nonAMP144849 35 0-50 2512 | nonAMP106248 37 0-50 2513 | nonAMP77434 37 0-50 2514 | nonAMP81275 37 0-50 2515 | nonAMP114419 34 0-50 2516 | nonAMP80923 15 0-50 2517 | nonAMP160436 10 0-50 2518 | nonAMP124730 15 0-50 2519 | nonAMP158529 50 0-50 2520 | nonAMP48987 29 0-50 2521 | nonAMP130897 30 0-50 2522 | nonAMP143418 25 0-50 2523 | nonAMP69149 37 0-50 2524 | nonAMP150278 42 0-50 2525 | nonAMP113093 50 0-50 2526 | nonAMP165632 48 0-50 2527 | nonAMP45103 48 0-50 2528 | nonAMP12374 25 0-50 2529 | nonAMP65948 13 0-50 2530 | nonAMP137933 46 0-50 2531 | nonAMP21199 47 0-50 2532 | nonAMP11642 33 0-50 2533 | nonAMP45034 29 0-50 2534 | nonAMP25135 37 0-50 2535 | nonAMP74344 30 0-50 2536 | nonAMP58388 35 0-50 2537 | nonAMP90514 44 0-50 2538 | nonAMP3289 40 0-50 2539 | nonAMP31828 23 0-50 2540 | nonAMP140788 17 0-50 2541 | nonAMP132896 37 0-50 2542 | nonAMP87666 38 0-50 2543 | nonAMP114360 35 0-50 2544 | nonAMP131464 43 0-50 2545 | nonAMP39961 33 0-50 2546 | nonAMP90094 44 0-50 2547 | nonAMP68200 11 0-50 2548 | nonAMP48222 37 0-50 2549 | nonAMP92088 14 0-50 2550 | nonAMP122819 11 0-50 2551 | nonAMP118415 32 0-50 2552 | nonAMP115374 16 0-50 2553 | nonAMP58851 35 0-50 2554 | nonAMP136354 48 0-50 2555 | nonAMP155761 10 0-50 2556 | nonAMP136728 42 0-50 2557 | nonAMP119732 31 0-50 2558 | nonAMP64873 47 0-50 2559 | nonAMP151115 12 0-50 2560 | nonAMP123965 37 0-50 2561 | nonAMP122434 8 0-50 2562 | nonAMP166596 12 0-50 2563 | nonAMP153818 15 0-50 2564 | nonAMP24197 25 0-50 2565 | nonAMP112328 15 0-50 2566 | nonAMP94774 37 0-50 2567 | nonAMP131712 42 0-50 2568 | nonAMP131363 31 0-50 2569 | nonAMP162464 31 0-50 2570 | nonAMP72436 13 0-50 2571 | nonAMP150298 25 0-50 2572 | nonAMP39216 48 0-50 2573 | nonAMP66647 12 0-50 2574 | nonAMP160174 34 0-50 2575 | nonAMP64187 37 0-50 2576 | nonAMP32393 29 0-50 2577 | nonAMP97120 16 0-50 2578 | nonAMP102877 11 0-50 2579 | nonAMP49163 38 0-50 2580 | nonAMP107826 16 0-50 2581 | nonAMP152959 43 0-50 2582 | nonAMP43051 20 0-50 2583 | nonAMP146827 41 0-50 2584 | nonAMP76072 24 0-50 2585 | nonAMP44584 16 0-50 2586 | nonAMP86458 13 0-50 2587 | nonAMP66630 46 0-50 2588 | nonAMP104692 12 0-50 2589 | nonAMP58108 49 0-50 2590 | nonAMP30261 22 0-50 2591 | nonAMP106591 39 0-50 2592 | nonAMP100906 43 0-50 2593 | nonAMP33773 34 0-50 2594 | nonAMP54244 24 0-50 2595 | nonAMP40394 20 0-50 2596 | nonAMP86025 21 0-50 2597 | nonAMP121997 11 0-50 2598 | nonAMP18856 38 0-50 2599 | nonAMP135506 9 0-50 2600 | nonAMP138919 16 0-50 2601 | nonAMP49048 28 0-50 2602 | nonAMP81483 20 0-50 2603 | nonAMP80243 12 0-50 2604 | nonAMP36505 33 0-50 2605 | nonAMP119425 48 0-50 2606 | nonAMP1369 49 0-50 2607 | nonAMP21103 38 0-50 2608 | nonAMP101333 30 0-50 2609 | nonAMP143434 28 0-50 2610 | nonAMP165585 37 0-50 2611 | nonAMP34399 7 0-50 2612 | nonAMP23833 14 0-50 2613 | nonAMP83251 9 0-50 2614 | nonAMP20680 16 0-50 2615 | nonAMP30287 32 0-50 2616 | nonAMP62349 8 0-50 2617 | nonAMP75854 11 0-50 2618 | nonAMP103750 44 0-50 2619 | nonAMP37213 9 0-50 2620 | nonAMP27546 17 0-50 2621 | nonAMP48137 25 0-50 2622 | nonAMP64346 44 0-50 2623 | nonAMP125621 19 0-50 2624 | nonAMP164087 19 0-50 2625 | nonAMP80456 46 0-50 2626 | nonAMP41875 19 0-50 2627 | nonAMP8254 24 0-50 2628 | nonAMP9759 47 0-50 2629 | nonAMP132475 37 0-50 2630 | nonAMP43492 37 0-50 2631 | nonAMP8667 7 0-50 2632 | nonAMP105138 7 0-50 2633 | nonAMP104514 16 0-50 2634 | nonAMP27964 23 0-50 2635 | nonAMP63787 42 0-50 2636 | nonAMP158209 14 0-50 2637 | nonAMP136322 26 0-50 2638 | nonAMP57949 27 0-50 2639 | nonAMP2780 29 0-50 2640 | nonAMP46965 29 0-50 2641 | nonAMP44159 46 0-50 2642 | nonAMP92307 17 0-50 2643 | nonAMP93688 47 0-50 2644 | nonAMP20235 14 0-50 2645 | nonAMP119880 30 0-50 2646 | nonAMP151882 20 0-50 2647 | nonAMP2537 37 0-50 2648 | nonAMP88639 49 0-50 2649 | nonAMP31968 29 0-50 2650 | nonAMP81207 48 0-50 2651 | nonAMP97725 9 0-50 2652 | nonAMP14619 17 0-50 2653 | nonAMP63508 32 0-50 2654 | nonAMP114889 45 0-50 2655 | nonAMP25506 31 0-50 2656 | nonAMP11433 41 0-50 2657 | nonAMP151787 28 0-50 2658 | nonAMP122576 35 0-50 2659 | nonAMP70331 37 0-50 2660 | nonAMP88836 8 0-50 2661 | nonAMP53834 14 0-50 2662 | nonAMP77840 5 0-50 2663 | nonAMP77978 118 101-150 2664 | nonAMP84780 101 101-150 2665 | nonAMP166133 134 101-150 2666 | nonAMP84559 147 101-150 2667 | nonAMP113591 122 101-150 2668 | nonAMP150566 141 101-150 2669 | nonAMP60548 140 101-150 2670 | nonAMP72136 119 101-150 2671 | nonAMP53901 149 101-150 2672 | nonAMP61437 138 101-150 2673 | nonAMP13722 128 101-150 2674 | nonAMP125107 126 101-150 2675 | nonAMP3410 119 101-150 2676 | nonAMP130065 130 101-150 2677 | nonAMP37660 133 101-150 2678 | nonAMP28361 130 101-150 2679 | nonAMP155819 147 101-150 2680 | nonAMP49438 149 101-150 2681 | nonAMP21360 109 101-150 2682 | nonAMP33862 141 101-150 2683 | nonAMP29614 128 101-150 2684 | nonAMP100538 144 101-150 2685 | nonAMP21649 142 101-150 2686 | nonAMP73508 132 101-150 2687 | nonAMP38415 141 101-150 2688 | nonAMP135516 110 101-150 2689 | nonAMP134115 143 101-150 2690 | nonAMP105406 121 101-150 2691 | nonAMP134452 127 101-150 2692 | nonAMP84090 121 101-150 2693 | nonAMP105246 143 101-150 2694 | nonAMP50121 130 101-150 2695 | nonAMP142408 130 101-150 2696 | nonAMP57907 143 101-150 2697 | nonAMP89129 146 101-150 2698 | nonAMP59750 110 101-150 2699 | nonAMP96949 148 101-150 2700 | nonAMP4659 112 101-150 2701 | nonAMP30741 124 101-150 2702 | nonAMP160527 149 101-150 2703 | nonAMP67886 143 101-150 2704 | nonAMP16903 119 101-150 2705 | nonAMP147338 132 101-150 2706 | nonAMP63283 132 101-150 2707 | nonAMP158155 145 101-150 2708 | nonAMP4065 141 101-150 2709 | nonAMP25926 143 101-150 2710 | nonAMP34058 103 101-150 2711 | nonAMP71683 146 101-150 2712 | nonAMP132738 106 101-150 2713 | nonAMP40508 101 101-150 2714 | nonAMP165185 115 101-150 2715 | nonAMP25039 148 101-150 2716 | nonAMP39287 129 101-150 2717 | nonAMP45794 150 101-150 2718 | nonAMP64075 136 101-150 2719 | nonAMP75809 109 101-150 2720 | nonAMP115500 129 101-150 2721 | nonAMP164325 146 101-150 2722 | nonAMP69526 101 101-150 2723 | nonAMP39233 109 101-150 2724 | nonAMP72421 107 101-150 2725 | nonAMP109690 120 101-150 2726 | nonAMP16530 118 101-150 2727 | nonAMP92581 150 101-150 2728 | nonAMP135164 103 101-150 2729 | nonAMP121338 137 101-150 2730 | nonAMP100212 124 101-150 2731 | nonAMP49317 113 101-150 2732 | nonAMP117282 134 101-150 2733 | nonAMP164724 148 101-150 2734 | nonAMP70568 117 101-150 2735 | nonAMP165443 130 101-150 2736 | nonAMP45509 104 101-150 2737 | nonAMP149228 123 101-150 2738 | nonAMP91117 150 101-150 2739 | nonAMP48268 142 101-150 2740 | nonAMP9000 134 101-150 2741 | nonAMP95291 119 101-150 2742 | nonAMP87420 149 101-150 2743 | nonAMP158948 147 101-150 2744 | nonAMP54860 127 101-150 2745 | nonAMP114453 103 101-150 2746 | nonAMP16720 114 101-150 2747 | nonAMP26280 141 101-150 2748 | nonAMP73498 130 101-150 2749 | nonAMP126533 116 101-150 2750 | nonAMP5593 124 101-150 2751 | nonAMP93950 147 101-150 2752 | nonAMP127069 148 101-150 2753 | nonAMP66489 101 101-150 2754 | nonAMP55926 117 101-150 2755 | nonAMP52790 102 101-150 2756 | nonAMP115654 125 101-150 2757 | nonAMP125990 108 101-150 2758 | nonAMP26368 127 101-150 2759 | nonAMP166422 125 101-150 2760 | nonAMP17353 124 101-150 2761 | nonAMP71931 116 101-150 2762 | nonAMP126870 118 101-150 2763 | nonAMP157597 115 101-150 2764 | nonAMP32011 105 101-150 2765 | nonAMP100571 131 101-150 2766 | nonAMP78744 101 101-150 2767 | nonAMP83020 119 101-150 2768 | nonAMP45238 132 101-150 2769 | nonAMP53712 116 101-150 2770 | nonAMP155957 126 101-150 2771 | nonAMP34471 136 101-150 2772 | nonAMP78856 133 101-150 2773 | nonAMP9294 127 101-150 2774 | nonAMP10455 133 101-150 2775 | nonAMP55354 144 101-150 2776 | nonAMP113564 133 101-150 2777 | nonAMP70411 142 101-150 2778 | nonAMP57874 116 101-150 2779 | nonAMP47797 106 101-150 2780 | nonAMP145832 103 101-150 2781 | nonAMP150540 111 101-150 2782 | nonAMP149900 150 101-150 2783 | nonAMP2211 118 101-150 2784 | nonAMP138850 150 101-150 2785 | nonAMP17926 110 101-150 2786 | nonAMP52175 101 101-150 2787 | nonAMP126569 116 101-150 2788 | nonAMP54212 122 101-150 2789 | nonAMP28484 113 101-150 2790 | nonAMP48384 149 101-150 2791 | nonAMP26809 115 101-150 2792 | nonAMP46436 120 101-150 2793 | nonAMP87092 127 101-150 2794 | nonAMP60293 135 101-150 2795 | nonAMP110605 138 101-150 2796 | nonAMP152954 144 101-150 2797 | nonAMP96575 127 101-150 2798 | nonAMP109574 108 101-150 2799 | nonAMP23950 131 101-150 2800 | nonAMP138574 115 101-150 2801 | nonAMP158920 129 101-150 2802 | nonAMP156217 144 101-150 2803 | nonAMP46480 124 101-150 2804 | nonAMP21172 144 101-150 2805 | nonAMP99277 101 101-150 2806 | nonAMP166096 142 101-150 2807 | nonAMP34233 120 101-150 2808 | nonAMP139829 150 101-150 2809 | nonAMP41916 139 101-150 2810 | nonAMP128110 120 101-150 2811 | nonAMP25072 116 101-150 2812 | nonAMP71682 105 101-150 2813 | nonAMP112601 121 101-150 2814 | nonAMP146411 146 101-150 2815 | nonAMP44960 130 101-150 2816 | nonAMP16313 103 101-150 2817 | nonAMP39813 111 101-150 2818 | nonAMP21465 123 101-150 2819 | nonAMP131235 122 101-150 2820 | nonAMP51779 116 101-150 2821 | nonAMP52014 122 101-150 2822 | nonAMP74695 140 101-150 2823 | nonAMP151347 149 101-150 2824 | nonAMP74818 105 101-150 2825 | nonAMP49702 139 101-150 2826 | nonAMP140099 129 101-150 2827 | nonAMP107328 147 101-150 2828 | nonAMP91460 123 101-150 2829 | nonAMP157126 122 101-150 2830 | nonAMP103661 120 101-150 2831 | nonAMP106033 109 101-150 2832 | nonAMP41390 122 101-150 2833 | nonAMP39866 117 101-150 2834 | nonAMP116576 121 101-150 2835 | nonAMP59117 140 101-150 2836 | nonAMP41981 112 101-150 2837 | nonAMP4894 127 101-150 2838 | nonAMP162593 148 101-150 2839 | nonAMP91163 155 151-200 2840 | nonAMP87114 161 151-200 2841 | nonAMP67343 184 151-200 2842 | nonAMP73482 163 151-200 2843 | nonAMP120327 158 151-200 2844 | nonAMP7318 179 151-200 2845 | nonAMP71564 199 151-200 2846 | nonAMP41065 192 151-200 2847 | nonAMP145376 175 151-200 2848 | nonAMP74147 156 151-200 2849 | nonAMP155355 176 151-200 2850 | nonAMP85601 188 151-200 2851 | nonAMP12331 183 151-200 2852 | nonAMP118184 157 151-200 2853 | nonAMP82585 163 151-200 2854 | nonAMP83204 192 151-200 2855 | nonAMP27267 160 151-200 2856 | nonAMP161850 198 151-200 2857 | nonAMP82738 177 151-200 2858 | nonAMP78638 169 151-200 2859 | nonAMP153175 159 151-200 2860 | nonAMP155696 170 151-200 2861 | nonAMP6647 158 151-200 2862 | nonAMP107144 185 151-200 2863 | nonAMP124145 191 151-200 2864 | nonAMP111429 162 151-200 2865 | nonAMP116895 78 51-100 2866 | nonAMP21760 89 51-100 2867 | nonAMP9181 88 51-100 2868 | nonAMP27068 96 51-100 2869 | nonAMP11595 85 51-100 2870 | nonAMP4321 80 51-100 2871 | nonAMP137477 88 51-100 2872 | nonAMP60219 92 51-100 2873 | nonAMP128086 99 51-100 2874 | nonAMP55967 93 51-100 2875 | nonAMP85140 66 51-100 2876 | nonAMP136452 98 51-100 2877 | nonAMP6129 91 51-100 2878 | nonAMP111932 66 51-100 2879 | nonAMP53107 71 51-100 2880 | nonAMP87562 92 51-100 2881 | nonAMP31773 69 51-100 2882 | nonAMP142069 65 51-100 2883 | nonAMP72712 73 51-100 2884 | nonAMP90531 97 51-100 2885 | nonAMP123776 86 51-100 2886 | nonAMP150155 89 51-100 2887 | nonAMP46718 77 51-100 2888 | nonAMP95138 78 51-100 2889 | nonAMP135310 62 51-100 2890 | nonAMP109090 60 51-100 2891 | nonAMP10462 70 51-100 2892 | nonAMP92181 81 51-100 2893 | nonAMP114493 62 51-100 2894 | nonAMP107028 77 51-100 2895 | nonAMP133428 59 51-100 2896 | nonAMP124522 91 51-100 2897 | nonAMP52958 92 51-100 2898 | nonAMP94920 100 51-100 2899 | nonAMP82013 80 51-100 2900 | nonAMP121386 86 51-100 2901 | nonAMP131854 63 51-100 2902 | nonAMP70868 96 51-100 2903 | nonAMP77549 87 51-100 2904 | nonAMP153741 85 51-100 2905 | nonAMP116213 86 51-100 2906 | nonAMP91710 85 51-100 2907 | nonAMP66613 89 51-100 2908 | nonAMP117002 53 51-100 2909 | nonAMP22173 90 51-100 2910 | nonAMP57920 66 51-100 2911 | nonAMP94034 63 51-100 2912 | nonAMP129782 57 51-100 2913 | nonAMP52889 91 51-100 2914 | nonAMP77380 95 51-100 2915 | nonAMP89310 57 51-100 2916 | nonAMP37671 94 51-100 2917 | nonAMP105312 100 51-100 2918 | nonAMP63794 96 51-100 2919 | nonAMP130955 85 51-100 2920 | nonAMP3318 72 51-100 2921 | nonAMP9069 66 51-100 2922 | nonAMP63817 91 51-100 2923 | nonAMP41121 58 51-100 2924 | nonAMP7302 66 51-100 2925 | nonAMP147067 83 51-100 2926 | nonAMP142397 82 51-100 2927 | nonAMP164525 95 51-100 2928 | nonAMP128645 82 51-100 2929 | nonAMP134187 64 51-100 2930 | nonAMP69295 80 51-100 2931 | nonAMP55120 86 51-100 2932 | nonAMP141495 69 51-100 2933 | nonAMP139876 97 51-100 2934 | nonAMP49408 92 51-100 2935 | nonAMP73679 100 51-100 2936 | nonAMP93337 90 51-100 2937 | nonAMP26111 83 51-100 2938 | nonAMP31517 61 51-100 2939 | nonAMP118676 58 51-100 2940 | nonAMP75546 59 51-100 2941 | nonAMP118787 61 51-100 2942 | nonAMP48357 87 51-100 2943 | nonAMP84566 62 51-100 2944 | nonAMP114679 85 51-100 2945 | nonAMP23325 74 51-100 2946 | nonAMP153315 73 51-100 2947 | nonAMP101609 93 51-100 2948 | nonAMP117935 69 51-100 2949 | nonAMP127405 91 51-100 2950 | nonAMP81872 60 51-100 2951 | nonAMP63204 65 51-100 2952 | nonAMP62858 79 51-100 2953 | nonAMP71364 88 51-100 2954 | nonAMP137954 93 51-100 2955 | nonAMP141523 59 51-100 2956 | nonAMP90319 61 51-100 2957 | nonAMP31304 88 51-100 2958 | nonAMP83551 96 51-100 2959 | nonAMP144264 80 51-100 2960 | nonAMP121649 93 51-100 2961 | nonAMP35101 88 51-100 2962 | nonAMP138997 73 51-100 2963 | nonAMP160861 60 51-100 2964 | nonAMP163578 100 51-100 2965 | nonAMP101202 54 51-100 2966 | nonAMP99437 76 51-100 2967 | nonAMP70259 60 51-100 2968 | nonAMP59020 89 51-100 2969 | nonAMP132095 96 51-100 2970 | nonAMP37969 55 51-100 2971 | nonAMP146825 87 51-100 2972 | nonAMP94329 95 51-100 2973 | nonAMP64502 55 51-100 2974 | nonAMP45244 60 51-100 2975 | nonAMP52169 89 51-100 2976 | nonAMP7615 92 51-100 2977 | nonAMP58625 71 51-100 2978 | nonAMP117073 78 51-100 2979 | nonAMP94701 92 51-100 2980 | nonAMP155697 84 51-100 2981 | nonAMP132165 83 51-100 2982 | nonAMP125882 91 51-100 2983 | nonAMP73954 66 51-100 2984 | nonAMP126460 65 51-100 2985 | nonAMP106560 75 51-100 2986 | nonAMP24234 98 51-100 2987 | nonAMP139372 79 51-100 2988 | nonAMP49059 77 51-100 2989 | nonAMP68196 90 51-100 2990 | nonAMP79251 96 51-100 2991 | nonAMP126486 59 51-100 2992 | nonAMP77617 100 51-100 2993 | nonAMP105400 94 51-100 2994 | nonAMP67875 86 51-100 2995 | nonAMP37501 78 51-100 2996 | nonAMP70779 97 51-100 2997 | nonAMP128988 68 51-100 2998 | nonAMP32569 66 51-100 2999 | nonAMP3362 99 51-100 3000 | nonAMP90102 87 51-100 3001 | nonAMP79538 55 51-100 3002 | nonAMP43227 72 51-100 3003 | nonAMP86511 93 51-100 3004 | nonAMP124318 92 51-100 3005 | nonAMP126283 81 51-100 3006 | nonAMP134402 63 51-100 3007 | nonAMP58010 73 51-100 3008 | nonAMP17253 96 51-100 3009 | nonAMP66064 52 51-100 3010 | nonAMP151834 67 51-100 3011 | nonAMP70897 100 51-100 3012 | nonAMP77386 87 51-100 3013 | nonAMP142430 94 51-100 3014 | nonAMP158444 82 51-100 3015 | nonAMP29312 94 51-100 3016 | nonAMP10857 89 51-100 3017 | nonAMP145523 84 51-100 3018 | nonAMP79551 84 51-100 3019 | nonAMP38403 74 51-100 3020 | nonAMP150798 95 51-100 3021 | nonAMP50525 89 51-100 3022 | nonAMP25567 72 51-100 3023 | nonAMP12805 100 51-100 3024 | nonAMP90795 75 51-100 3025 | nonAMP57432 85 51-100 3026 | nonAMP157178 68 51-100 3027 | nonAMP11517 64 51-100 3028 | nonAMP38044 71 51-100 3029 | nonAMP157369 97 51-100 3030 | nonAMP100046 67 51-100 3031 | nonAMP140089 92 51-100 3032 | nonAMP544 64 51-100 3033 | nonAMP68441 91 51-100 3034 | nonAMP138411 76 51-100 3035 | nonAMP120817 70 51-100 3036 | nonAMP116159 89 51-100 3037 | nonAMP44900 92 51-100 3038 | nonAMP111565 75 51-100 3039 | nonAMP74916 63 51-100 3040 | nonAMP64511 71 51-100 3041 | nonAMP141678 89 51-100 3042 | nonAMP103095 96 51-100 3043 | nonAMP68375 84 51-100 3044 | nonAMP69031 77 51-100 3045 | nonAMP65115 66 51-100 3046 | nonAMP106076 56 51-100 3047 | nonAMP114469 79 51-100 3048 | nonAMP155787 94 51-100 3049 | nonAMP45354 100 51-100 3050 | nonAMP2333 82 51-100 3051 | nonAMP154638 89 51-100 3052 | nonAMP71652 80 51-100 3053 | nonAMP19034 94 51-100 3054 | nonAMP109314 78 51-100 3055 | nonAMP38891 66 51-100 3056 | nonAMP165823 84 51-100 3057 | nonAMP68954 94 51-100 3058 | nonAMP21556 98 51-100 3059 | nonAMP77946 97 51-100 3060 | nonAMP28844 97 51-100 3061 | nonAMP134222 62 51-100 3062 | nonAMP54103 100 51-100 3063 | nonAMP58367 98 51-100 3064 | nonAMP19004 82 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nonAMP18639 98 51-100 3099 | nonAMP160489 54 51-100 3100 | nonAMP93400 87 51-100 3101 | nonAMP52065 97 51-100 3102 | nonAMP4957 90 51-100 3103 | nonAMP58235 71 51-100 3104 | nonAMP153299 95 51-100 3105 | nonAMP96993 92 51-100 3106 | nonAMP49685 55 51-100 3107 | nonAMP31 98 51-100 3108 | nonAMP152651 57 51-100 3109 | nonAMP73147 98 51-100 3110 | nonAMP55649 61 51-100 3111 | nonAMP105794 100 51-100 3112 | nonAMP93293 55 51-100 3113 | nonAMP99357 73 51-100 3114 | nonAMP25038 83 51-100 3115 | nonAMP145869 72 51-100 3116 | nonAMP164699 71 51-100 3117 | nonAMP15242 75 51-100 3118 | nonAMP56244 95 51-100 3119 | nonAMP143623 68 51-100 3120 | nonAMP113185 64 51-100 3121 | nonAMP101107 93 51-100 3122 | nonAMP146911 69 51-100 3123 | nonAMP65554 89 51-100 3124 | nonAMP141422 68 51-100 3125 | nonAMP12252 89 51-100 3126 | nonAMP122924 70 51-100 3127 | nonAMP26652 83 51-100 3128 | nonAMP7767 87 51-100 3129 | nonAMP21419 77 51-100 3130 | nonAMP64914 92 51-100 3131 | nonAMP93727 72 51-100 3132 | nonAMP161968 68 51-100 3133 | nonAMP51640 95 51-100 3134 | nonAMP48338 66 51-100 3135 | nonAMP40617 80 51-100 3136 | nonAMP97223 51 51-100 3137 | nonAMP148611 86 51-100 3138 | nonAMP76912 100 51-100 3139 | nonAMP43768 96 51-100 3140 | nonAMP23404 71 51-100 3141 | nonAMP127793 61 51-100 3142 | nonAMP87341 95 51-100 3143 | nonAMP146486 87 51-100 3144 | nonAMP140303 91 51-100 3145 | nonAMP28518 87 51-100 3146 | nonAMP126932 62 51-100 3147 | nonAMP5135 92 51-100 3148 | nonAMP95566 80 51-100 3149 | nonAMP159911 67 51-100 3150 | nonAMP825 96 51-100 3151 | nonAMP160478 94 51-100 3152 | nonAMP72272 64 51-100 3153 | nonAMP129973 91 51-100 3154 | nonAMP46628 92 51-100 3155 | nonAMP48735 82 51-100 3156 | nonAMP2235 90 51-100 3157 | nonAMP44157 100 51-100 3158 | nonAMP51501 96 51-100 3159 | nonAMP148760 77 51-100 3160 | nonAMP9395 55 51-100 3161 | nonAMP8935 81 51-100 3162 | nonAMP3346 72 51-100 3163 | nonAMP117012 57 51-100 3164 | nonAMP164802 68 51-100 3165 | nonAMP139879 82 51-100 3166 | 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nonAMP141099 51 51-100 3269 | nonAMP89260 71 51-100 3270 | -------------------------------------------------------------------------------- /training_data/nonAMPprop.txt: -------------------------------------------------------------------------------- 1 | >200 0.031 2 | 0-50 0.783 3 | 101-150 0.054 4 | 151-200 0.008 5 | 51-100 0.124 6 | --------------------------------------------------------------------------------