├── seeker ├── tests │ ├── __init__.py │ └── test_seeker.py ├── __init__.py ├── models │ ├── model.h5 │ ├── MatModel0.h5 │ └── MatModelPRO.h5 ├── command_line.py └── seeker.py ├── seeker.png ├── example_input ├── BACE2.output.txt └── PGE.output.txt ├── MANIFEST.in ├── train_model ├── README.md ├── train_model.py ├── sample_phage_training.txt └── sample_bacteria_training.txt ├── setup.py ├── .gitignore ├── README.md └── LICENSE /seeker/tests/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /seeker/__init__.py: -------------------------------------------------------------------------------- 1 | from .seeker import * 2 | -------------------------------------------------------------------------------- /seeker.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gussow/seeker/HEAD/seeker.png -------------------------------------------------------------------------------- /seeker/models/model.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gussow/seeker/HEAD/seeker/models/model.h5 -------------------------------------------------------------------------------- /example_input/BACE2.output.txt: -------------------------------------------------------------------------------- 1 | NZ_CP026026.1 Bacteria 0.26 2 | NZ_CP026027.1 Bacteria 0.26 3 | 4 | -------------------------------------------------------------------------------- /seeker/models/MatModel0.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gussow/seeker/HEAD/seeker/models/MatModel0.h5 -------------------------------------------------------------------------------- /seeker/models/MatModelPRO.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gussow/seeker/HEAD/seeker/models/MatModelPRO.h5 -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include seeker/models/model.h5 2 | include seeker/models/MatModel0.h5 3 | include seeker/models/MatModelPRO.h5 4 | -------------------------------------------------------------------------------- /example_input/PGE.output.txt: -------------------------------------------------------------------------------- 1 | MH356729.1 Phage 0.85 2 | LC333428.1 Phage 0.79 3 | MK903728.1 Phage 0.94 4 | MN016939.1 Bacteria 0.34 5 | MN095770.1 Phage 0.82 6 | MN095772.1 Phage 0.84 7 | MN176219.1 Phage 0.52 8 | MN310548.1 Phage 0.62 9 | MN379739.1 Phage 0.82 10 | MN419153.1 Phage 0.89 11 | 12 | -------------------------------------------------------------------------------- /train_model/README.md: -------------------------------------------------------------------------------- 1 | This directory contains the script to train an LSTM model, along with sample bacterial and phage sequences. To run the script and train a model, use the following command: 2 | ``` 3 | python train_model.py --bacteria sample_bacteria_training.txt --phage sample_phage_training.txt --out sample_model.h5 4 | ``` 5 | -------------------------------------------------------------------------------- /seeker/tests/test_seeker.py: -------------------------------------------------------------------------------- 1 | from unittest import TestCase 2 | 3 | import seeker 4 | 5 | TEST_INPUT = { 6 | "bacterial": ("example_input/BACE2.txt", "example_input/BACE2.output.txt"), 7 | "phage": ("example_input/PGE.txt", "example_input/PGE.output.txt") 8 | } 9 | 10 | 11 | class TestSeeker(TestCase): 12 | longMessage = True 13 | 14 | def test_fragment(self): 15 | for name, sample_data in TEST_INPUT.items(): 16 | sample_input_path, sample_output_path = sample_data 17 | sample_output = open(sample_output_path).read().strip() 18 | 19 | seeker_fasta = seeker.SeekerFasta(sample_input_path) 20 | output = "\n".join(seeker_fasta.phage_or_bacteria()) 21 | self.assertEqual(output, sample_output, msg="Unequal output for {}".format(name)) -------------------------------------------------------------------------------- /seeker/command_line.py: -------------------------------------------------------------------------------- 1 | import sys 2 | from os.path import basename, isfile 3 | from .seeker import SeekerFasta 4 | 5 | # Constants ------------------------------------------------------------------------------------------------------------ 6 | MGM_USAGE = "Usage:\npredict-metagenome " 7 | 8 | 9 | # Terminal functions --------------------------------------------------------------------------------------------------- 10 | def predict_metagenome(): 11 | if len(sys.argv) != 2: 12 | print(MGM_USAGE) 13 | sys.exit(1) 14 | 15 | path = sys.argv[1] 16 | if not isfile(path): 17 | print("{} is not a valid file path.\n{}".format(path, MGM_USAGE)) 18 | sys.exit(1) 19 | 20 | print("Reading Fasta at {}...".format(path), file=sys.stderr) 21 | seeker_fasta = SeekerFasta(path, load_seqs=False) 22 | print("name\tprediction\tscore") 23 | print("\n".join(seeker_fasta.phage_or_bacteria())) -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import setuptools 2 | 3 | INSTALL_DEPS = [ 4 | "numpy>=1.17.3", 5 | "tensorflow>=2.0.0" 6 | ] 7 | 8 | with open("README.md", "r") as fh: 9 | long_description = "".join(fh.readlines()[1:]) 10 | 11 | setuptools.setup( 12 | name="seeker", 13 | version="1.0.3", 14 | author="Ayal B. Gussow, Noam Auslander", 15 | author_email="ayal.gussow@gmail.com, noamaus@gmail.com", 16 | description="Predict bacterial or phage sequence", 17 | long_description=long_description, 18 | long_description_content_type="text/markdown", 19 | url="http://seeker.pythonanywhere.com", 20 | include_package_data=True, 21 | packages=setuptools.find_packages(), 22 | classifiers=[ 23 | "Programming Language :: Python :: 3", 24 | "License :: OSI Approved :: GNU General Public License (GPL)", 25 | "Operating System :: OS Independent", 26 | ], 27 | python_requires='>=3.6', 28 | install_requires=INSTALL_DEPS, 29 | test_suite='nose.collector', 30 | tests_require=['nose'], 31 | entry_points = { 32 | 'console_scripts': [ 33 | 'predict-metagenome=seeker.command_line:predict_metagenome', 34 | ], 35 | } 36 | ) 37 | -------------------------------------------------------------------------------- /.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 | -------------------------------------------------------------------------------- /train_model/train_model.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | ############################################################################### 3 | """ 4 | Summary: Train an LSTM model for sequence classification. 5 | Input: Bacterial Fasta, Phage Fasta, in segments of 1K. 6 | Output: LSTM model. 7 | """ 8 | ############################################################################### 9 | 10 | 11 | # Imports --------------------------------------------------------------------- 12 | import warnings 13 | warnings.simplefilter(action='ignore', category=FutureWarning) 14 | 15 | import argparse 16 | import re 17 | 18 | import numpy as np 19 | 20 | from Bio import SeqIO 21 | 22 | from keras.models import Sequential 23 | from keras.layers import Dense, LSTM 24 | 25 | 26 | # Configuration --------------------------------------------------------------- 27 | NUC_ORDER = {y: x for x, y in enumerate(["A", "T", "C", "G"])} 28 | NUC_COUNT = len(NUC_ORDER) 29 | 30 | # Functions ------------------------------------------------------------------- 31 | def seq2matrix(sequence, nuc_order, fragment_length): 32 | """Convert sequence to binary matrix.""" 33 | # Set sequence length to a maximum of our set fragment length 34 | assert len(sequence) <= fragment_length 35 | sequence_len = min(len(sequence), fragment_length) 36 | sequence = sequence[:sequence_len] 37 | 38 | # Create a dataframe to represent the sequence 39 | ret = np.zeros((4, fragment_length)) 40 | 41 | # Set each base in the matrix 42 | for idx, base in enumerate(sequence): 43 | ret[nuc_order[base], idx] = 1 44 | 45 | return ret 46 | 47 | 48 | def read_fasta(fasta_path, nuc_order, fragment_length): 49 | """Read a Fasta into array of arrays, for LSTM input.""" 50 | # Read in file 51 | fasta = SeqIO.parse(fasta_path, "fasta") 52 | 53 | # Iterate fasta and convert to matrices 54 | matrices = [] 55 | for idx, entry in enumerate(fasta, start=1): 56 | entry.seq = entry.seq.upper() 57 | 58 | if re.match('^[ACGT]+$', str(entry.seq)) is None: 59 | continue 60 | 61 | matrix = seq2matrix(str(entry.seq), nuc_order, fragment_length) 62 | 63 | matrices.append(matrix) 64 | 65 | ret = np.array(matrices) 66 | 67 | return ret 68 | 69 | def build_model(): 70 | """Build the LSTM model for sequence classification.""" 71 | # Initialize a sequential model 72 | model = Sequential() 73 | 74 | # Add LSTM layer 75 | model.add( 76 | LSTM(5, input_shape=(NUC_COUNT, 1000)) 77 | ) 78 | 79 | # Add Dense NN layer 80 | model.add( 81 | Dense(1, activation='tanh') 82 | ) 83 | 84 | model.compile( 85 | loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'] 86 | ) 87 | 88 | return model 89 | 90 | 91 | # Main ------------------------------------------------------------------------ 92 | PARSER = argparse.ArgumentParser(description=__doc__) 93 | PARSER.add_argument("--bacteria", 94 | type=argparse.FileType('r'), 95 | required=True) 96 | PARSER.add_argument("--phage", 97 | type=argparse.FileType('r'), 98 | required=True) 99 | PARSER.add_argument("--out", 100 | type=str, 101 | required=True) 102 | 103 | args = PARSER.parse_args() 104 | 105 | # Read phage sequences 106 | X_phage = read_fasta( 107 | args.phage, NUC_ORDER, 1000 108 | ) 109 | 110 | # Read bacteria sequences 111 | X_bac = read_fasta( 112 | args.bacteria, NUC_ORDER, 1000 113 | ) 114 | 115 | # Build, train model 116 | model = build_model() 117 | X_train = np.concatenate((X_phage, X_bac)) 118 | y_train = np.concatenate((np.ones(len(X_phage)), np.zeros(len(X_bac)))) 119 | 120 | model.fit( 121 | X_train, y_train, epochs=100, batch_size=27 122 | ) 123 | 124 | # Save model 125 | model.save(args.out) 126 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![Seeker](seeker.png) 2 | Seeker is a python library for discriminating between bacterial and phage genomes. 3 | Seeker is based on an LSTM deep-learning models and does not rely on a reference genome, 4 | genomic alignment or any direct genome comparison. 5 | 6 | ## Overview 7 | This file describes a python package that implements Seeker, an alignment-free discrimination between Bacterial vs. phages DNA sequences, based on a deep learning framework [1]. 8 | This package can call classifiers that were trained with (a) either Python Keras LSTM with embedding layer, or (b) Matlab trained LSTM with a sequence imput layer, which was converted to a Keras model. A web portal is also available (http://seeker.pythonanywhere.com/). 9 | 10 | If you have any trouble installing or using Seeker, please let us know by opening an issue on GitHub or emailing us 11 | (either ayal.gussow@gmail.com or noamaus@gmail.com). 12 | 13 | Note: Seeker relies on tensorflow, which is not yet supported in python 3.8. Therefore, to use 14 | Seeker you need to use Python 3.6 or 3.7. Creating different Python environments is easy using conda 15 | (https://docs.conda.io/en/latest/). 16 | 17 | 18 | ## Citation 19 | [1]Noam Auslander*, Ayal B. Gussow1*#, Sean Benler, Yuri I. Wolf, Eugene V. Koonin# [Seeker: Alignment-free identification of bacteriophage genomes by deep learning](https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkaa856/5921300) Nucleic Acid Research, October 2020. 20 | (*) These authors contributed equally, (#) Corresponding authors 21 | 22 | 23 | ## Installation 24 | Seeker requires python3, and has been tested with python3.6 and python3.7. 25 | Seeker can be installed using pip. From a terminal, run: 26 | 27 | `pip install seeker` 28 | 29 | This will install Seeker and all of its dependencies. 30 | 31 | ## Installation using Conda 32 | Conda provides an easy method to install Seeker. First, install conda or miniconda 33 | (https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html). 34 | 35 | Then run the following commands to install seeker: 36 | ``` 37 | conda create --name seeker python=3.7 pip 38 | conda activate seeker 39 | pip install seeker 40 | ``` 41 | 42 | Note: If you rely on conda, any time you want to use Seeker's libraries or commands you have to first run: 43 | ``` 44 | conda activate seeker 45 | ``` 46 | 47 | ## Usage 48 | The Seeker library consists of binaries that can be run from the command line and a python library that 49 | can be incorporated into Python scripts. 50 | 51 | ### Binaries 52 | Seeker includes a binary that predicts whether an entire sequence is bacterial or phage. 53 | 54 | To predict whether sequences are bacterial or phage, run the following from the terminal: 55 | 56 | `predict-metagenome input.fa` 57 | 58 | This will output a prediction for each sequence in `input.fa` along with Seeker's score. Scores are between 0 and 1. 59 | Higher scores correspond to phage predictions while lower scores correspond to bacterial predictions. Sequences with 60 | scores above 0.5 are predicted phages, while sequences with scores below 0.5 61 | are predicted bacteria. 62 | 63 | ### Python Library 64 | The primary class in the Python library is SeekerFasta. SeekerFasta can load a Fasta file and score its entries using 65 | Seeker. SeekerFasta has the following parameters: 66 | 67 | 1. path_or_str. Either a path to a Fasta or a Fasta string. 68 | 2. LSTM_type. Which LSTM implementation to use. Options are "python", "matlab", "prophage" (not recommended). Default is Matlab. 69 | 3. seeker_model. If you've already loaded a model into a SeekerModel object and prefer to use that model, you can 70 | provide it as a parameter here. Default is None, in which case the model will be loaded from file. 71 | 1. load_seqs. Whether to preload all Fasta sequences to memory. Default is True. 72 | 5. is_fasta_str. Set to True if you provided a Fasta string instead of a path to a Fasta file. Default is False. 73 | 74 | Once a Fasta is loaded, there are several functions that can be used to generate Seeker predictions. 75 | For example, to predict whether the entries of a Fasta are phage or bacteria: 76 | ``` 77 | from seeker import SeekerFasta 78 | seeker_fasta = SeekerFasta("input.fa") 79 | predictions = seeker_fasta.phage_or_bacteria() # This returns a list of phage/bacteria predictions for the Fasta 80 | print("\n".join(predictions)) # print predictions 81 | 82 | # To filter the Fasta file for predicted phage sequences, the following will 83 | # create a new fasta and save it to "seeker_phage_contigs.fa" with all sequences with 84 | # a Seeker score of 0.5 and above (threshold can be adjusted per user goals) 85 | seeker_fasta.meta2fasta(out_fasta_path="seeker_phage_contigs.fa", threshold=0.5) 86 | ``` 87 | 88 | Alternatively, to predict prophages: 89 | ``` 90 | seeker_fasta = SeekerFasta("input.fa", LSTM_type="prophage") 91 | seeker_fasta.save2bed("output.bed") # Save prophage coordinates to BED file 92 | seeker_fasta.save2fasta("output.fa") # Save prophage sequences to Fasta file 93 | ``` 94 | NOTE: Seeker was not trained to predict prophages. The prophage model is the output of the first training step, that has been described in [1]. This model has not been tested thoroughly for prophage prediction, and its performance is affected by the prophage prediction parameters which depend on the organism and the user's goals. Due to this, the use of this model for prophage detection is not recommended, unless it is done as an initial filtering step. 95 | 96 | ## LSTM Models 97 | The LSTM models can be found in the `models` directory. 98 | 1. model.h5. Metagenome LSTM model, trained in python using Keras. 99 | 1. MatModel0.h5. Metagenome LSTM model, trained in matlab. 100 | 1. MatModePRO.h5. Prophage LSTM model, trained in matlab. 101 | 102 | ## Datasets 103 | Training, validation and test datasets are available from: 104 | ftp://ftp.ncbi.nih.gov/pub/wolf/_suppl/Seeker/ 105 | 106 | ## Contact 107 | If you run into any issues or have any questions, feel free to open an issue on Github or email us 108 | at either ayal.gussow@gmail.com or noamaus@gmail.com. 109 | -------------------------------------------------------------------------------- /train_model/sample_phage_training.txt: -------------------------------------------------------------------------------- 1 | >NC_021307.1_1 2 | TGCGCCTGCCCGATCATGTACGGGTTTTGGACCTCCGGTTCTGGGACCCCCGCGCCAGAATCCACCCGCACGCGGGCTGTGTGGCCCGCTGACGGACGAACAGGGAGGCCCGAGGGTGGTAGTACCCGGCGTGGAGACGCCGGGAGACGGCCACTGGCGAGACACCCTCTGACCTGCGGTTAGTTACCCTTTTGCGAAGTAACCCCCACTATAGGGTGAAGGGGTTCAGAGTAGAGACCCTCTCCTATGGGAGGGGGGATAAAGGGGGGTGGGCAGGTTAAGCGGCGCTCAACGCCGCGCCGCCCTGGTTTACCCAGGGGGCCTGGAGGCCCCCAAGCCGGAAGGCCCCCTGAAAGACCCGCCGTTTTCGGCGGCGGGTCATCTGGTGGGAGGTCTCTCGAAGACCTCCCTGTCGGTCGGTCTTCTCGTCGGCAGCCGCCGAAGCGGCTGCCTCTACTAAGGGGGTCCTCAGTGTCTTGGAGCACTTCGGACCGCAGCAGCAGACTCCCAGCGGACTGGGAGGAGAACTACCGGCAGCCCGTTCTGAGGGCAGCCGGATACCGGTGTCAGATCCGTCAGCGCGGTTGCCTCGGGAAGGCAACCGACGTGGACCACATCGAGCGCGGCGACGACCACAGTCGCCGCAATCTCCAGGCAGCGTGCAACCGCTGCCACGGGAAGAAGTCATCCGCAGAGGGCCATGCCCGCAAGCGGCAGTTACGAGCCAGGAGGAAGCGACCCGCAGAACGCCACCCTGGCCGCCAGTAGCGGGCCAGGAGCCCGCTTTCACCCAGGAGGTGTCAGGTGGGCGAACGCGGCCCCGTTCGGAAGCGCAGCGATCAGCGCGTCCGACGCAACAAGGACGAGGTCCCGACCGAGAAGGTCGTGGCCATCGGGACGGTACCCGTCCCGGAACTCGGGTTCGATGATCCGCACCCGATAGTGAGAGACCTCTACGAGTCTCTGGCTTCCTCAGCGCAGTCCCGGTACTACGAACCGA 3 | >NC_021307.1_2 4 | GCGACTGGCAGTACGCGAGGCTGGCCCTGCACTTCGCAGACCAGCTCCTCAAGTCCTCCAAGCCCAACGGTCAGTTGCTGACGACCGTGAATCAGATGCTCACGGGTCTCCTCGTCTCAGAGGGTGACCGCCGGCGTGTCCAGCTCGAAGTCGAGCGGACCCAGGCCGAAGGAGTCGTTGTCGACGTGGCAGCGATGTTCCGTGAGCAGCTCGGAGCGGCGCAGCGCACCGGCTGACCCAGAGACCTACCGGAGGGGGTCTGAGCGCGTTTCCTCTCGGCGCAGCTCCCCCCTCCGGGATGGATTTCCCGATTGAAAGGAACCGATGTCCGTACAGATCATCACCAATGACCCCGCAGCCCCCTACGCGGTCATGTCCATCGACATCACCGATGTCGACGTGTCCACCGAGGCGGCCCAGAACGCGCTGCGTTCGGCGGTCGCGGAGATCAACCCCGCAGCCGCCTACATCCAGGTGGGCGTCGAGACCGCCGAGACCGTCACGGTCGGCAGCCAGGCGTTCAACGTGAAGCGCCAGCGGTCGTTCTACTCGCTGGACGACTACTTCGCCTACGCCACGAGCGCGTAGGCCCCAGAAGTTCCCCCCTCCGGTCGGGCAATGCAGTGATCGAGCGGGGGTTTGTTGTTGCGGCGGCCCCCGTAGGGCGGGGGCCCCGCCCATAGGCATGTAGCTCAATTGGTAGAGCAGCGGTCTCCAAAGCCGCCGGTTGCAGGTTCGAGTCCTGCCGTGTCTGCTCCCTTCCTCGTTCGTCTAATCGGTAGGACACCAGGCTCTGGACCTGGGGGTTGAGGTTCGAGTCCTTGGCGAGGAGCACCCACCCAGTTCTCTCGCAGGGCTGGGTCTCTTTGACGTGTAGCTCAACCGGCAGAGCAGGCGGCTGTTAACCGCCCGGTTGGAGGTTCGAGTCCTCCCTCGTCAGCAACTTGACTTCGTACACACAGAAAGGAGCACGTCATGGGCTTCCTCCAGACCATCGGCA 5 | >NC_021307.1_3 6 | AGGCCGTCGGAGAGATCGTCGTCCCCGTCATCCAGCGCGAGCTGAAGGAGCACGCGGAGACGCTGATCCCGCAGGTGCTCGATTCCGTCCGCAGCGAGTTCGACAAGCAGCTCCCCGCACTGACCAAGGCGCTCGTGACCGCAGTGACCGAGGCCGCCGCCAAGGGCGCGGCCGAGGGCGCAGACCGGATCACCGACGCGATCCCCGGCCAGCTCGATGACCAGATCATCGACCCCATCGTCAACCGGGCACTGGAGATCTTCCGAGGGGCTCTGGGTCGCTGATGGCGCTCGGAGGCCGCATGGAGAACGGCTGGCCCGAGTGCGATCTGTCGGACTGTGACTACGCCACGGTCCCCGGCACCCCGCTTCGGCTGCCGTTCCGCAAGGGACACCCGTTCATCATCCTGCAGGCGTTCCTGCGGGACCTGAACGAGTTCATCGAGCCGCTCATGAACGCCCGAGGGGCGACCGACGAGGGCTCCTGGACCGACAACAACAGCGTCTACACGTCGAACCACAAGGGCGCGACTGCATTCGACTACAACTGGTCGGACCACCCGATGGGCAACGCCAACGCCGGATGGAACGGCTCGGTGCTCATCGCAGGCGACCAGGTGCCGGCTGTCCGTGAGCTGCTGCGGTTCTACACGTACCGAGGCATCCAGCTCGTCTGGTGGGGCAACGACTGGAACAGCCCCAAGGACTCGATGCACTTCCAGATGGGCTACAACACGGTCAACAACCCCCAGATCGTGCAGGAGTTCATCGACAAGTTCATCCGTCCGGACGGCTTCTCGACGTTCCGTCGCGGCGGTGCAGCCCCCTCGGCTCCCGTTGTCGACGCAGCCACGATCCTCGCTCGTGCGACCGGCATCGCCTACGCCAAGGCGCAGGAGATCCTGCCGACGCTCCGCGACGGCCTGATCCTGGCCGACTGCAAGTCGGTCCCCCGGATCGCCATGTTCGTCGCGCAGACGTGCTGGGAATCGGACCACTAC 7 | >NC_021307.1_4 8 | AACGCCACCGAGGAGTACGCCAACGGCCCGCTCAACGAAGAGCGGTGGATCTACAAGGGGCGCACCTGGATTCAGCTCACCTGGAAGTCCGCGTATGCGGGCTTCGGGCAGTGGTGCTTCGAGCGTGGACTGGTCAACGACCCGAACATCTTCGTGAACCACCCGAGGATGCTGGCCGAAACCCGTTGGGCCGGTTTGGGAGCCGCGTACTACTGGCTGACCACCCGCCGACCCACCCGGAAGTACCCGACGCTGAATCAGGCTTCGGACGCCCGCGACGTGCTGGTGGCAACGCAGATCATCAACGGTGGGACCACCCACCTGGCAGAGCGGCAGGCGCTCTACAACCGCGCCATCGCGCTCGGTGACGACCTGCTGCTGATACTCGGAGGAGACGAGGACGAAATGGCTGGTTGGGACCAGACCAAGGTGGACCGCGCAATGGTCCTCCTGGAGAACATCGCCGGCGTGCGCCGGAAGTCACGCAGCCCGCTGCGCTGGCCGTATGAGAAAGAGATCGACACCGCAATCGGCTTCGCCTGGACGGCTGACGCCAACGTGCATGTCCAGCTCGTGGAGAAGCTGGCCGTCATCTACGGCGACCCGACCTCCATCGCGCTGCTGACCGCTGTGGCCAACACCGATGAGCCCGGTCGTGAAGGCGACCGCGAGCTTGCTCGCCGCATCCTCGCTCGCTGTAGCGAAGACGCCGTGCAGGAAGCCCACAAGCGCATCAACGAGTGGCTCGAAGCGGAGAAGGCCCACAAGGTAGGCGTCTGATGCCGCTGAGGTTAGGCGACCGCAACGACACCGTGCGCGAGTGGCGCTACATCATGTCGAAGCGGTTCTCGGGCTATGCCAGGGCGCTGGGCCCACTTCCGACTGACACGAACGAGTTTGGCCCACGGGCCAAATCGTGGCAGGAGGAGTACGAGCGGCGCACTGGGCAGCCAGTCGACGGCGAGGTCTCCGACCTCGATCTGTTTGCGCTGGGTGTCTC 9 | >NC_021307.1_5 10 | TGTGTCGCCGCACGCTCCGGGCCCCACGCGCTGGCTGTTCACGGTGCATGGCACTGGCATGGCAGACCCGCTTGGACCGGGGCTACCGGCCGACACTGCTCGCGCTTGCCTGGACAAGTACACCTGGCAGCCCATCGGGAACTACCCCGCCTCGGTCTGGCCGATGTGGCCCTCGATCCAGAAGGGCCGCACTGAGCTTCGCGCTCAGATCGCAGCCAAGCAGGGCGAGATCAACATGGCCGGCTACTCGCAGGGCGCTGTCGTGGTGGGTCAGGTGCTCAAGCACGACATCATCGACCCCGCTGGTCCGCTGCATCACCGTCTCGGAGATGTCCGCAAGGTCGTCTTCTGGGGCAACCCGATGCGCCAGCAAGGGATTCAGCATGATGACCAGTGGATTCACGAGATCGCACCGGCCGACACGCACGGCATCCTGACGTTCGACCTGCTGGAGGGTCTGGAGCGGTTCCCGCACATCCAGGTCCGTGACTACGCCCACAAGGGCGACATGTACGCCTGCAACACCGACGACCAGGCCGCTGAGTACAAGCGGGCTATCGCACGGATCGTCATGCGTGCCACGGACTTCTATGGCGGCGAGGACTCCATCGTGGCCCAGCTTCGAGAGCTGGCCGCCCGTCCGCTGCCCGAGATGATCGCGGCGGCACAGGCTGCCATCCAGGCGCTGCAGTTCGCAACGAGCACCGCACACGGCTACAACATCGGCCCCGCAATCGAGTTCCTGCGGAGCTGACTTGACATCGAACGGAAGGAGGAGGGGTGAGCCTCGCCAATCACCACCCGGTGCCGCTCCTCCCTTCTCCTCCGCACAAGATCGGCCCGACGTGGCAGATCCGCGAGGATGGGAGTTGGCACCTACCTGAGAGGACCCTCGGCTGGGGCGTCCTGAACTGGTGGGCCAAGTACGTGCGCTCGCCAGGCGGCACGACCGCAGGCAACCCGTTCCTGCCCACTCTGGAGCAGGCCCGGTTCGTCTTGT 11 | >NC_021307.1_6 12 | GGTGGTACGCCGTAGACGAGAACGGACGGTACGCATACCGCGACGGCGTGCTCCGTCGCATGAAGGGCTGGGGCAAGGACCCGCTGGCCGCCGCGCTCGCTTTGGTCGAGCTGTGCGGCCCGGTGGAGTTCTCGCACTGGGACCTCGACGGCAACCCGGTCGGCAAGACCCGCCACGCGGCCTGGGTCCAGATCGCCGCTGTGTCACAGGATCAGACGAAGAACACGTTCTCGCTGTTCCCGATCATGATCAGCAAGGATCTGAAGTCCGACTACGGCCTGGACGTGAACAAGTTCCTGATCTACTCCGAGGCCGGCGGCCGGATCGAGGCAGTGACCTCGTCCCCCGCGTCCATGGAGGGCAACCGCCCTACGTTCGTGATCTGCAACGAGACCCAGTGGTGGATCGAGACCAACGACGGCCACGACATGTACGGGGTCATCGAGGGCAACGTCACGAAGATCCCAGGCTCCCGCAAGCTGTCGATCTGCAACGCCCACATCCCCGGACAGGATTCGGTCGCGGAGCGCGAGTACGACGCATGGCAGGACGTGCTGTCTGGCAAGGCAGTCGACGCGGGCTCGCTGTACGACGCTCTCGAAGCGCCAGCGGACACCCCGGTCTCCGAGATCCCCTCGCAGAAGGAAGATCCCGAGGGCTACGCGGAAGGCATCGCCAAGCTGCTCGACGGCCTGGCAGTGGCCCGAGGCGACTCGTACTGGCTCCCGCTGGAGGAGATCCTCAAGTCCGTCCTGGACGTGAAGAACCCGGTCAGCGAGTCACGGCGCAAGTTCCTCAATCAGGTGAACGCCCACGAGGATTCGTGGATCTCACCGTCTGAGTGGAACGCCTGCTACCCCGAGGGAGGCCCCAAGCCGCTGGCGCGGAACGACCGCATCACGCTCGGGTTCGACGGATCGAAGTCCAACGACTGGACCGCCTTGGTGGCGTGTCGGGTCGAGGACGGTTGCCTGTTCCTGCTCAAGGCGTGGAACCCGGT 13 | >NC_021307.1_7 14 | GGATCACCCGTTCGAGGAGGTCCCCCGCGAGGACGTGGACGCCGTCGTCAGATCGGCGTTCCAACGGTACGACGTGGTCGGCTTCCGGGCCGACGTGAAGGAGTTCGAGGCATACGTCGACCAATGGGGCAGGGACTTCCGACGCAAGATCAAGGTCAACGCGACTCCGGGCAACCCCATCGCCTTCGACATGCGTGGGCAGACGAAGCGGTTCGCACTGGACTGCGAGAGGTTTCTGGACGCCGTTCTCGAACGGGAGTTGCAGCATGACGGGAATCCCGTTCTGCGCCAACACATCCTGAACGCCCGCCGACACCCGACGACATACGACGCAATCAGCATCCGCAAGGAGAGCAAGGACAGCAGCAAGAAGATCGACGCCGCTGTCTGCGCCGTCCTCGCGTTTGGTAGCAGACAGGACTTCCTGATGAGTAAGAAGAGCCGTAGCGGCCGAGCGGTGGTGATCCGATGACCGCCCCGCTGCAGACCGAGAACGTGAACCCCGACGAACGTCGGGAGGCGCTGCTGAGCGCCTTCGAGGGCAGTGTCGCTGGCTACTCCGTGAACACCGACTACTACGAGTCGGTACGCCGGCCCGATGCGGTCGGCATCGCGGTGCCGCCCGAGATGCGGAAGTTGCTGGCCCACGTCGGTTACCCCCGGCTTTACGTCAACGCGCTGGCTGATCGCCTGCGAGTGGAGGGCTTCCGACTGGCTGACGCCGACGAGGCCGACAAGCAGCTCTGGGACTGGTGGCAGGCCAACGACCTCGACGTGGAATCCGTTCTGGGCCACGTCGATGCGCTGGTGCATGGCCGGTCCTACGTGACCATCTCGGCACCGGACCCGAGCATCGAGGACCCGATGGTGGACCCCTCGGTCCCGATCATCCGTGTCGAGCCCCCGACCAACCTGTTCGCCAACATCGACCCGCGCACGCGCCGGGTGACCGAGGCGCTCCGGGCGATCTACGACCAGGAGGGCAGCGAGGTCATCTCGG 15 | >NC_021307.1_8 16 | CCACGCTGTACCTACCCGACCAGACCGTCCTGTACGACAAGGTCAACGGCCAGTGGACGGCGACGAACACGATCAACCACGGCATGGGCCTGGTCCCGGTCGTGCCGATCTCGAACCGCAACAGGCTCTCGGATCTGTATGGCACGTCGGAGATTACGCCCGAGCTGCGCTCGGTAACCGACGCCGCCGCCAGGACGCTGATGCTGATGCAGTCCACGGCAGAGCTGATGGGTGTGCCACTTCGCCTGCTGTTCGGCGTGACCCGCCGAGAGCTGGGCCTGGAGGACGCTGACGACCCCGATGCCCCGGTGACCCCGAGGCAGGCGTTCGAGGCGTACTACGCACGCATCCTCGGCTTCGAGGCTGCAGAGGGTAAGGCGTACCAGTTCGACGCAGCCGAGCTGCGGAACTTCGTGGATGCCCTTGACGCACTGGACAAGAAGGCAGCGGCCTACACCGGCCTGCCGCCTCAGTACCTGTCGTTCAGCTCGGACAACCCGGCCTCGGCTGAGGCCATCCGGTCGTCTGAGTCGCGACTGGTGATGAACGCAGAGCGCAAGGCACGCATCTTCGGTGGCGCATGGGAACAGGTCATGCGGGTGGCATGGAAGGTCATGAACCCCGGTGGCACGATCCCGCCGAACATGTTCCGGATGGAGTCCATCTGGGCTGACCCGGCGACCCCGACCTACGCGGCCAAGGCCGACGCGGCGACCAAGCTCTACAACCAGGGCATGGGCCTGATTCCGAAGGAACAGGGCCGGATCGACATGGGCTACAGCGAAGCAGCTCGACGCCAGATGCGGAAGTGGGACGAGGAGGAGAACCCGGTACAGGGTGCTCTTGCTCGCGTGGTGAACCCCGGACGGACGACCCCGCCGGCTGAGACCACGGATGCCCCTCCGAGTGAGGAGACGACTGACGAGTGACCCCCGAGGAGTACACCGCCCAACAGGCGGCGCTCTCGGCGGCAGTCGCACAGTTCGTCCTCCGCTTAGGA 17 | >NC_021307.1_9 18 | CGACTGTTCCTGGGGCCCAGGCTGTCTGTGACGGACTGGATCGGGTTCCTGGAGGTCTTGTACCCCGAGATCTACCAGAGGCGTCTGGAGGCCGCTGAGATCGCTCGGCAGTTCTACGACACCGAGCGCCAGCGTCACGGACGCATCCCGCACCCCCGCTACCTGACGGAGTACGACTTCTCCGAGTTCGTGGCCGACATGGAGCCGACGCGAGACAAGATGTCCCGCAACGACGCTCCTGAGGCTGCCCTCGGGGAGGTCGCTCTCCGAGCCGTCCGCAATGTCGAGATGGCAGGACGGAAGCAGATCCTTCGGGCGGTCGAGAACGACCCCGAGCCCCAGAAGGTCACGGGGTGGGCCCGAGTGGCGACTGGTCGAGAGACCTGTGCCTGGTGCCTGATGCTGATCTCGCGAGGCCCCGTATACCTCGATGCGAGAACAGCCGGCTTCGACGTTGACAACGAGCAGGCGCTCGAAATCCTCGCTGCTGGTGAGGATGTCAGTGAGTACATGCGGGAGTGGCATACGGGCTGCGACTGCAAAGTGGTGCCGGTCTACGACAAGAAGAACTGGCCAGGGTACGCCGAGTACAAGCGTGCCGAGAAGTTGTGGATCGACGCCGGTAGAGAGGCAAGCCGCCTCATCGAGTCGGGCGAAGCTCGTACTCGAAACATGAACAAGGAGACGCAGAACGCGCTCCGTCGTCGGCTCGAAAGAGGCGACATCACCATGACCGAGTTCGCGGTAGCCGCGTAACTCATCACCGAGCAGCCCCCAGGTGGGGCTCAAACATGCCCAGGAGGCGAATCAGTATGGCTGACAACGACACCCAGACCCCCGACACCACCGGCACGCCGGAAGGCACCCCGGAGGGCGAGACGCAGGCCCCGGAGGCCCCAGTCGAGACCTTCAGCCGGGAGTACGTCGAGGAACTCCGTCGTGAGAACGCCAAGGCACGCACCGCCAAGAAGACCGCCGTCGATGAGGCCAAGGCCGAAGT 19 | >NC_021307.1_10 20 | GACGCGGATCTACGAGGCGAAGCTGGCCGAGAAGGACACGGCCTACACCGAACTGGAGAACCAGCTCGGACAGGCGTGGATCGAGCTGGAGAAGGTCTACACGGCCATCGACGCAAAGGTCCCGTCCGACAAGGTCCGCGCTTTCGCGGCGATCCTGCAGGGGTCCGATCCTGACTCGATCAAGGAGTCAGCGAAGTCGGCCACGGAACTGTTCGGGGGCTTCAACACGAAGCAGCCCCCGGTCGACCCCACCCAGGGTTCCGGTGGTGGCAAGCACACGCCGCTCAACGGAGACCCGATCCTCGAAGCCCTCAAGCGGGCTGTCGGGGCCTAATCCCCAACTAGGCAAGGAAGATACAAATGGCAGCAGGAACTGCTTTCCCGGTCAACCACACGCAGATCGCGCAGACCGGTGACTCGATGTTCCAGGGTTACCTGGAGCCCGAGCAGGCACAGGACTACTTCGCGGAAGCGGAGAAGACCTCCATCGTTCAGCGCGTGGCGCGGAAGATCCCCATGGGTTCGACCGGCGTGAAGATCCCGCACTGGACCGGCGACGTGAGCGCCGCGTGGATCGGTGAAGGCGACATGAAGCCGATCACCAAGGGCGACATGAGCGTCCAGCAGGTCGAGCCGCACAAGATCGCGACGATCTTCGTGGCCTCGGCCGAAACCGTCCGTGCGAACCCCGGCAACTACCTGGGCACCATGCGGACCAAGGTCGCCACGGCCATCGCGCTGGCGTTCGACGAGGCGGCGCTGCACGGCACCGACTCGCCGTTCGACAAGAACCTGGATGAGACGACCAAGTCGGTCGACCTCACCCCGGCCACCGGCACGACCTACGACGCCATCGGCGTGAACGCCCTGAGCTTGCTGGTCAACGCCGGCAAGAAGTGGGGTGCCACTCTGCTGGACGACGTGGCCGAGCCCATCCTCAACGGTGCCAAGGACGCGAACGGCCGCCCGCTGTTCGTGGAGAGCACCTACGAGGCGGTCA -------------------------------------------------------------------------------- /train_model/sample_bacteria_training.txt: -------------------------------------------------------------------------------- 1 | >NC_012587.1_1 2 | CGTTGCGACAGGGCGTCTATCGGGCGCGGCATGGCTTTTGAGGGCAGCCGTGTCCGATGGACATAAGGATGAGGCCGGCAGCGATGACGGCATCGGATAATGATATTGGAAGGCGGCAACATGCAGATGAATTTGGCGACGGCGTCTGAAGGAATTCAGGCCGGAAGCAAGCAGTCTCAGGCGGCGGGGGAAAAGCACGACATGCGGCATGACGCACTTTTCGAGCGGGTAAGTGCGCGTCTAAAAGCCCAGGTCGGTCCGGATGTCTTCGCCAGCTGGTTCGGACGTCTCAAGCTCCATTCGGTATCGAAAAGCGTCGTACGCCTGTCGGTGCCGACGACGTTCTTGAAGTCATGGATCAACAATCGCTATCTCGAGCTGATCACCAGTCTTTTCCAGCAGGAAGACGGTGAAATCCTCAAGGTGGAGATTCTCGTGCGCACCGCGACCCGCGGCCAGCGCCCGGCGGTTCACGAAGAGGCAGTTGCTGCGGCAGCCGAACCGGCGGCGGCTGCCCCGGTCCGCCGCGCAGCCTCGCCCCAGCCGGTCGCCGCTGCCGCCGCCACGGTCGCAGCTTCGGCAAAGCCGGTGCAGGCGCCGCTGTTCGGCTCGCCGCTCGACCAGCGCTACAATTTCGAGAGCTTCGTCGAAGGCTCGTCCAACCGCGTGGCGCTTGCCGCCGCACGTACGATCGCCGAGGCCGGCGCCGGCGCCGTGCGCTTCAATCCGCTCTTCATTCATTCGAGCGTCGGGCTCGGCAAGACGCATCTGCTGCAGGCGATCGCGCTTGCTGCCCTGCAAAGCGCCCGGGCGCCGCGCGTCGTCTACCTGACGGCCGAATATTTCATGTGGCGTTTCGCTACGGCGATCCGTGACAACGACGCGCTGTCGCTGAAGGAATCGCTGCGCAACATCGATCTTCTCGTCATCGACGACATGCAGTTCCTGCAGGGAAAGTCGATCCAGCATGAGTTCTGCCATCTCCTGAACATGCTGCTCG 3 | >NC_012587.1_2 4 | ACTCGGCCAAGCAGGTGGTCGTGGCGGCGGACCGGGCGCCCTGGGAACTGGAATCGCTCGACAGTCGCGTTCGTTCCCGCCTGCAGGGCGGCGTGGCGATCGAGATGGAGGGCCCCGATTACGACATGCGCCTCGAAATGCTGAAGCGCCGCCTGGATACCGCCCGTCAGGACGACACCTCGCTCGACATTCCGCAGGAGATTCTCTCCCATGTGGCGCGAAGCGTCACGGCGAGCGGCCGCGAGCTGGAAGGGGCCTTCAACCAGCTCCTGTTTCGCCGGTCCTTCGAGCCGCAACTGTCGATAGAGCGCGTCGACGAACTGCTCGGACACCTGGTCAATGCCGGCGAACCGCGCCGCGTCCGCATCGAGGATATCCAGCGCATTGTCGCCAAGCACTATAATGTGTCGCGCCAGGAATTGGTTTCGAACCGCCGAACCCGCGTCATCGTCAAGCCGCGCCAGATCGCCATGTACCTGTCGAAGACGCTGACGCCGCGCTCCTTCCCGGAAATCGGTCGCCGCTTCGGCGGCCGCGATCACACGACGGTCTTGCACGCGGTGCGCAAGATCGAGGAGCTGATCTCCGGCGACACGAAACTCAGCCACGAGATCGAGCTGCTCAAGCGGCTGATCAATGAATAGAAATTGGTAAGCTCGTAGTTACGAAAGCCCGGCTCCGCCGGGCTTTTTTGTCAACCGCCACCGCGGTTGGTTTCGTCGATCATTGCGGCAGTGTTTTTTTCGTCCCGCTTGTCGGCTATCTCTTTCGTCATTCGTTTTTTAATCGAAGCGGGAGAGGTCTCGATGCTTTACGCGGTGCTTTGCTATAATACGGAAGACGTCACCAGCGCCTGGACCAAGGAGCAGGATGACAAGGTCATGCGTGATCTGACCGCCGTCCAGCGCAAATATGTCGAGGCCGGCAAGCTCGGCCCCGTGGCACGGCTCCTGCCGACGACCGCCGCGACGACATTGCGCCACAGGCCCGGCGAGACCGT 5 | >NC_012587.1_3 6 | CGTCATCGATGGCCCTTTTGCCGAAACCAAGGAGCAACTGCTCGGCTTCTATCTGATCGACTGCGCATCGCTCGACGAGGCGCTCGAATTTGCACGTGAGCTGAGTGCCGCCAACCCGGCTGCCGGCTCCTACGAGATCCGCCCCCTTTCCCTCTATAAGCCAGGTGAGCTTGCCCCATGACCGACACCGCCTGGATCGACCTTGCCCTGGTTTCGGCGCGGCCGCAGGCAATGGGCGCTCTGCTGCGCTATTTCCGCAATCTCGATCTGGCGGAGGAAGCGTTCCAGGAGGCTTGCATCCGGGCCTTGAAGACCTGGCCGACAAACGGGCCGCCGCGCGACCCGGCTGCCTGGCTGATCTTCGTCGGCCGCAACAGCGGTATCGACCGGGTGCGGCGGCAATCGCGCGAGACGGCGCTGCCGCCGGAAGAGCTGCTCTCGGATATCGAGGACCGCGAAAGCGAGCTTGCGGACCGCCTCGACGGGGCGCACTACCGCGACGATATCCTGCGGCTGCTCTTCGTCTGCAGCAATCCGGTCCTGCCGGCGACCCAGCAGATCGCCCTGGCGCTCCGCATCGTGTCCGGGCTTTCCGTCAAGCAGATCGCCCGCGCCTTCCTTGTCGGCGAGGCGGCGATGGAGCAGCGCATCACCCGTGCCAAGGCGCGGGTCACGGCGGCCGGCATCCCGTTCGAAACGCCGGATGCCTCCGAGCGCGCCGAGCGGCTCGCGGCGGTCGCCATGATGATCTATCTCGTCTTCAACGAGGGCTATTCGGCCATGAACGGTCCGGAAGGGGTCTCCGCCGATCTCTGCGACGAGGCGATCCGTTTGGCGCGGCTGCTGCTGCGGCTGTTTCCGGCGGAGCCGGAAATTATGGGGTTGACGGCGCTCCTGCTTCTCCAGCATTCCCGTGCGCGCGCCCGCTTCGATGCCCATGGCGCCATCGTCCTGCTCGAGGATCAGGACCGGCAGCTGTGGAGCCTGCCGATGATCACCG 7 | >NC_012587.1_4 8 | AGGCACTGGCGATGATAGACAAGGCGATGCGCCACCGCCGCCCCGGCCCCTACCAGGTCCAGGCGGCGATTGCAGCGCTGCATGCACGCGCCGAACGCTCGGAACAGACGGATTGGGAGGAAATCGAGCTGCTCTACCGGGCGCTCGAGCGCCTGCAGCCGTCACCCGTCGTCACGCTCAACCGGGCCGTGGCCGTCTCGAAGCGCGACGGGCCGGAGGCGGCACTGGCGCTGGTCGAGCCGCTTGCCGACCGGCTTTCCGGCTATTTCTATTTCCACGGCCTGCGCGGCGGCCTGCTCAAGCAAATGGGCCGGGCTCGTGAGGCGCGCGTCGCCTTCGACCGGGCGATCGCGCTCGCCACCAATGCGGCGGAGGCCGCCTATATCCGCAAGCAGCTTGACCACCTTGCGGGAGAGGCAGGCGCCTCCCACCTTCACGAAGACCAGCAATAAAAAATCCTCGGTCGCTGTCGGAGTCCGCTCTTTCCGGTCGTCCTAGGTTCGAACCCACCAAGGAGGAATTGCAAATGACCGCAGCAACCGACACCAGGCCCGCCGACGAACGCGACCTGGTCCTGATCCGCCTGATCGACGCACCGCGCGAAAAGGTCTATCGGGCCTTCACGGATCCTGAGCTCCTGAAGCAGTGGTTCGCACCCCTGCCGTGGACGATCACCGAAGCGGAGCTCGATGTCCGACCGGGCGGCACCAACCGCTTCGTGATGCGCTCGCCGGAGGGTGAGCTTTATCCCAATCAGGGTGTCTATCTGGAGGTCGTGCCGAACGAGAAACTGGTGATGAGCGATGCCTACACGGAAGCCTGGACGCCCTCTGAAAAGCCCTTCATGACAGCGATCCTGACCTTCGAGGAGGAGGGCGGCAAGACCCGCTACACCGCCCGCGCCCGCCACTGGAGCACCGCGGACCGTGAGGCGCACGAGAAGATGGGCTTTCACGAGGGCTGGGGCCAGTGCGCCGACCAGCTCGCCGCACTCGTCGCA 9 | >NC_012587.1_5 10 | AAACTCTGAGAGGAGGATCGATCATGTCCACCAACGCGAGGACAGCAAGCGTCGCCGATATTCGGGCGGCAATCGAAGATTGGGCGGGGGCCCTGCGGCGGAAGGATGCTGGCCGTGTCCTTTCGCATGGCACGCAGGATTGCGTCATCTACTCGCTGGCACCGCCGCTGAAGGCCTCGGACGCCGACCCCGGCGGCCTGGAACAGTGGTTTTCCACCTGGAAGGGGCCGCTCGGCTACCAGCTTCAGGACCTCGAGATTTCATCCGGCGGGGACATCGCCTTCGCCACGGCGCTGGCGCATCTCTCCGGCGAGAAGCAGGATGGTGCGAAGGTATCGCTCTGGTACCGGCTGACGCTGGGCCTCGAACGCACCGACAAGGGATGGAAGATCGCCCACCAGCACGAGTCGGTCCCCTTCTATATGGACGGAAGCTTCAAGGCGGCGATCGATCTCGACCCGACGTCGCAGGTCGACTGGAATGCGCCCTCGCATCCGCCGATGGCCGGCGTCATCGCCTATCTCAACGTCGAAAATGCGGATGCGACCGCCAAGTTCTACGGCCGCGCCTTTGGCGCCAGGGAGGAGGACCGCAAATATGCTGAGGACGGCAAGCGCCTGATCCATTGCCACCTGACGATCAACGGCGGCGCGCTAATGCTGAACGACCCCTTCCCGGAATTCGGCTTTCCCTGGAAGCCGCCGGAGGGCTTCGTCCTGCATCTCATCGTCGACGACGCCCATGCCTGGTGGGAACGGGCGGTCGCGGCCGGTGCGGAGGCGACGATGCCGCTGGAGATCGCCTTCTGGGGCGACTATTACGGCCAGCTCCGCGATCCGTTCGGCATCACCTGGGGCATTGTCGCGCCTGTGAAGAAGAGCAACTGAGCCAACCGGCGGGCATGCGTTGCCGCCGCGCGTGAAACAAACTTGCGCGGCGGGCGGCGAAACAGAGCGCAATTCTTGCGGCCGATTAACTATTCTTCGGGAACGTCAACAGC 11 | >NC_012587.1_6 12 | CAACGCACCCGAGGCATAAGTGATCCTCTACTACCAGACCCATTCCCCCTTCGCCCGCAAGGCGCTCGTGTTCGCCCATGAGATCGGTCTTGCCGATCGCCTGGAAGTCGTCCACCACGAGACGAGCCCGACACGGCGCAATGACGACGTCTATGCCGAGAATCCGCTCGGCAAGGTTCCGGTCCTGCTGCGCAGTGACGCCGAGCCGATCTTCGATTCCGATGTCATCTGTGCCTATTTCGACACCTTGCACGAGCGACGCAAGCTCATTCCCGAAAGCGGGGAGCCGCGGTGGCGGGCGTTGCGGCTGCAATCGGTGGCACAGGTCCTTGCCCAGACCGGGATCGGGCTTCGCTGGGAGTGGGAACGCCGCCCGGAACAGTTGCGCTGGCCGGCGCTTGCCAAGGGCTATGAAGAGAAGATCGAGGCGACCTACGACTGGCTCGACACGACGTTTCTTCCTGAGGAAGACATCGATGTCGGCCAGATCGCACTGGCCACGACACTGTCGTGGATGGCTTTTCGCGCTCTTCCGTCATTTCGCGACCGGTCGCGCCTGACGCGCTGGTTCGACGCCTTCGAGCAGCGTGCCTCGATGGTCGCTACACCGCTGTCGGGCGAAACGCAGGATTGATCGCAACCTGCAGCCGGCGGGCATGATCTGCCGGTTCTAGAGCGGGATGAGGAAAAGTGTGTGCGGTTTTCCGCCCGCATCCCGCTCTAACCTATTAGAATCGATCACGTTCGTGATTTTAGGGCGATCCGACCTAAAATCACGAACGTGATCTAGAGCGGGATGCGCTCGCCGCCGCTCTAAGCCTTTGTTTTTGCCGCGTTTGTGCGACGTCGGGTGATTCCACCTGACTGCAAATGCTCTAAGGGCAGGCGAGGTCGGCGACGACGGCATCGAGGATCAACATGCCGGCCGGCGTGCAGCGCAGCCGCGAATTGCCGAGCCGTTCGACAAAGCCGTGCTCGATCAGGAACTGTTCCCGTTCGG 13 | >NC_012587.1_7 14 | GATCCGGATCGCGACCGGAGAGGCCCTGCCAGCGGGCGAGATCGACGCCTTCCCGGAGCCGCAAACCCATCAGCAGCAGTTCGTCGGCCTGGGCTTCGCGATCGAGCGGTTCCTGCTCGGTGATGCCGTCACCGCGCTCTTCGACGAGCCGCAGCCACGCTTCCGGGTTCCGTTCCGTTGCGGTCGCGATCTTGCCGGAGCCGGCGGTCAGCCGCCCATGGGCGCCCGGGCCGATCCCCGCATAGTCGCCATAGCGCCAGTAGGTGAGGTTGTGGCGGCTCTCTGCTCCCCGACGGGCGTGGTTGGAGACCTCGTAGGCCGGCATGCCGATCGCCGCGGTGATCTCCTGCGTCGCCTCGTAGAGCACGGCCGATTGTTCCCCGTCCGGAACGATCAGCTTGCCGGCCTTGTGCAGGCCGAAGAAGGGCGTGCCTTCCTCGATGGTGAGCTGATAGAGCGACAGATGGTCGACGGCATAGGAGACCGCCTCCTTCAACTCGCGCTCCCATTCCTCGACCGTCTGCTTCGGCCGCGCATAGATCAGGTCGAAGGACATGCGCGGGAAGATCTCGCGCGCCAGCCGCACCGCCTTCAGCGCGTCTTCCACATTGTGCAAGCGGCCGAGGAATTTGAGGTCGCGGTCGTTCAGCGCCTGGACGCCGAGCGAGACGCGGTTGACGCCCGCCGCCCGGTAGCCGTGGAAGCGCGTCGCCTCGACGCTCGAAGGGTTGGCTTCCATGGTGATCTCGATACCGTCAGGCACATGCCAGTGCCGGGCGATGCCGTCGAGGATCGCCGCGACCGTCGCCGGCTCCATCAGCGATGGCGTGCCGCCGCCCATGAAGATGCTGGTCACGGTTCGCGGGCCGGACAGGGCCCCCACGGACGCCATCTCCTGAAGGAAGGCGGCGACGAAGCGCGGCTGGTCCACCGGCTGGTGACGGACATGGCTGTTGAAGTCGCAATAGGGGCATTTGGCCGCGCAGAACGGCCAGTGCAC 15 | >NC_012587.1_8 16 | ATAGACCCCGAAGCCGGGGTCCATGTCGTCACCGATTGCCGAAAGCGAGGCCGTCTGGATCATTGGCTTCTCAGGCGCCGAGGCAGGTCTCGGCAAACAGCTTGAAGGCGCGGGCGCGATGCGACAGGGCTTCGCTGTTGCCCGGCTTCCAGCCGTGCTTCTCTTCCGCGCTCATCTCGCCGAATGTGGTGTCGTAGCCCGTGGGTTGGAAGACCGGATCGTAGCCGAAGCCGCTGGTTCCGCGCGGCGGCCATACGACATGGCCCTCGACCTCGCCGCGAAAGAGCTCGACATGTCCATCCGGCCAGGCAAGGCAGAGCACCGAGACGAAACGCGCCGTCCGCTCTTCCGGCTTCGTCGCGCCCTTCTCGCTCAGCTCTCTTTCGACCTTTTCCATCGCCATGGCGAAGTCGCGGCTGCCGTCGTCGCGCTCCGCCCAGTTTGCCGTGTAGACGCCTGGCGCCCCGCCGAGCGCATCGATGGCAAGGCCGGAATCGTCCGAAAGGGCCGGCAGACCCGAGGCCCTGGCCGAGGCGAGCGCCTTGATCGTCGCGTTTTCCTCGAAGGTCGTGCCGGTCTCGTCCGGCTCGACGAAATTGAGATCGGCCGCTGATTTGGCTTCGAAGCCGAGCGGCCCGATCAAGTCGCGGATTTCGCGGATCTTGCCGGCATTGTGGCTCGCGACCACGAGCGTCTTGTCAACGAGTTTGCGCATCTGGTTCCGTTCCGTCCAAACTTTCAAAGCCCATCAATCGATGTCCCACAGGCCAGGTTCCGCGCACTCCAGGCTGTTACCGGCCGGGTCGCGGAAATAGAACGAGCGGGCGCCGTTCGGCCAGCGGATATCCGCCTCGATCGCCACGCCGGCCGCCTCCAGCTTCGCCTTCCAGGCCTCGAGCGCCGGCGCTCCAACGCGGAAGCACATGTGACCCTTGCCTTGCGTGCCGTGCGACGGCACGGGCAGCGCGCCGGCAGCGGGCGGTTTCACCGTTTCCGCCGC 17 | >NC_012587.1_9 18 | GTTGAAGATCAACAGCACGCCGTCGCCGCAGCGGAAAAAGACGTGGCGGTTGCCGACGCGGGTGATCCGGGCGAGCCCGAGGACGTTGCCGTAGAACGCCTCGGCGGCGTCGAGATCGTCCGCGTAGAGCGCGGTTTCGAGGATGCCTTCAAGAGCCGCAGCCAACGTCGTTCCTCTCAGCTTGCAGTGCCAGGGCGCAAACCGCCCGCACGCCCTAGCCGGCGACCGCCTGCTTCTGCAACGCGACGAGTTCGGCAATGCCGTCCTTGGCGAGTTGCATCAGGCTTGCGAATTCTTCCTCGGAGAAGGGCTTCCCTTCCGCCGTTCCCTGGATCTCGACCAGGCCACCGCTGCCCGTCATGACGAAATTGGCGTCGGTTTCGGCGGCCGAGTCTTCGAGATAGTCGAGGTCCACCACCGCCTGGTTGGCGAAAATGCCGCAGGAAATCGCGGCGACATGGTCCTTGAGTACCCTCTCGACCTTGATCATGTTGCGCGCTTCCATCCACTTCAGACAGTCGTGCAGCGCGATCCAGGCGCCGGTGATCGAGGCGGTGCGGGTGCCGCCATCCGCCTGGATGACGTCGCAGTCGATCGAGATCTGACGCTCGCCGAGCGCCGGCAGATCGACGACGGCTCGGAGCGAACGGCCGATCAGCCGCTGGATTTCCTGCGTGCGGCCGCTCTGCTTGCCCGTGGACGCTTCGCGCCTCATGCGCTCGCCGGTCGCCCGCGGCAGCATGCCGTATTCTGCCGTGATCCAGCCCTTGCCGCCGTTGCGCAGCCAGGCGGGAACCTTATCTTCGAGGCTCGCCGTGCAGAGCACATGCGTGTCGCCGAAGCGCACGAGGCAGGAACCCTCCGCATGCTTGGAGAAATTGCGCTCGAAGGAAACCTTGCGCATTTGGTCGGTTTTTCTGCCGGATGGCCGCATCGTAAACTCCTGTCGTTCTGCTGAGCCTTCTAGATCATGGCGGGGGTAAAGGGAACCAGTTGATAT 19 | >NC_012587.1_10 20 | CGGGGCAGGGGAGTGATTTTTCCCTTTTGCCATTGCCCCGCGCGACCACTATATTCTGCGGAGGACACGAACGGTTGTTTGCATGGAGTGACATGGTACTGCGCAACCCTGAAAAGAAGGGGATCGCATCGACGCTGGATGAGCGCTCCGGCGAGATCTTCCGCCGCATCGTGGAGACCTACCTGGAAAGCGGCGAGCCGCTCGGATCCCGCAATCTGTCGCGCCTCCTGCCGATGTCGCTGTCGCCCGCCTCGGTGCGCAATGTGATGAGCGATCTCGAGGATCTGGGCCTCATTTATTCGCCCCATGTCAGCGCAGGGCGGTTGCCGACCCAGACGGGGCTGCGCTTCTTCGTCGATGCTTTCATGCAGGTCGGCAACCTTTCCGCCGAAGAGCGGACCTCGATCGAGCGGCAGGTGCGGCGCGCCGACCGTGATCAGCCGATCGACAGCCTTCTTGCCGAGGCAAGCCAGATGCTTTCCGGCATGTCCCGCGGCGCCGGCCTGGTCATCACCACCAAAAGCGACCCGGTGCTGAAGCATGTCGAGTTCATTCGGCTCGCGCCGACCAAGGCGCTTGCGGTCCTGGTCGGCGACCACGATCAAGTGGAAAACCGGATTATCGAGCTGCCGGCGGGCATCACCAGCGCGCAGCTCACCGAGGCGGCAAACTTCGTCAATGCGCATCTTGCCGGGCAAACCATTCCCGAACTGCGGTCCCAGCTCGAGAAGGTCAAGGAGACGGTGCGCGGCGAACTCGACGCGCTGTCGCAGGATCTGGTTGAGCGCGGGCTGGCAATCTGGTCGGGCAGCGAGGGCGACGAGAAGCCGGCGCGCCTTATCGTGCGCGGCCGCGCCAACCTGCTCGAAGGGCTGGAAGGTACGGAAGACATCGAGCGGCTGCGCATGCTGTTCGACGATCTCGAAAAGAAGGACAGCCTGATCGAGCTCCTCGATCTTGCCGAAAGCGGTCCGGGCGTGCGCATCTTCATCGGCTCGGA 21 | -------------------------------------------------------------------------------- /seeker/seeker.py: -------------------------------------------------------------------------------- 1 | """ 2 | Model classes and functions for viral or bacterial sequence prediction. 3 | """ 4 | # Imports -------------------------------------------------------------------------------------------------------------- 5 | import os 6 | import re 7 | import sys 8 | 9 | import numpy as np 10 | from pkg_resources import resource_filename 11 | from collections import OrderedDict 12 | 13 | import random 14 | 15 | # Minimize TF logs, has to be done in this order 16 | # logging.info("Importing Tensorflow") 17 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 18 | from tensorflow import get_logger, autograph 19 | get_logger().setLevel('ERROR') 20 | autograph.set_verbosity(0) 21 | 22 | # Import what's needed from tensorflow 23 | from tensorflow.keras.models import load_model 24 | from tensorflow.keras.preprocessing.sequence import pad_sequences 25 | 26 | 27 | # Constants ------------------------------------------------------------------------------------------------------------ 28 | DEFAULT_NUC_ORDER = {y: x for x, y in enumerate(["A", "T", "C", "G"])} 29 | NUCLEOTIDES = sorted([x for x in DEFAULT_NUC_ORDER.keys()]) 30 | SEGMENT_LENGTH = 1000 31 | PHAGE_THRESHOLD = 0.5 32 | 33 | 34 | # Functions ------------------------------------------------------------------------------------------------------------ 35 | def load_lstm_model(model_path): 36 | """ 37 | Loads Keras model. 38 | :param model_path: Path to H5 model. 39 | :return: Keras model. 40 | """ 41 | model_loaded = load_model(model_path) 42 | return model_loaded 43 | 44 | 45 | def random_base(match): 46 | """ 47 | Generate a random base. 48 | :return: Random base. 49 | """ 50 | return random.choice(NUCLEOTIDES) 51 | 52 | 53 | def handle_non_ATGC(sequence): 54 | """ 55 | Handle non ATGCs. 56 | :param sequence: String input. 57 | :return: String output (only ATCGs), with randomly assigned bp to non-ATGCs. 58 | """ 59 | ret = re.sub('[^ATCG]', random_base, sequence) 60 | assert len(ret) == len(sequence) 61 | return ret 62 | 63 | 64 | def pad_sequence(sequence, source_sequence, length=SEGMENT_LENGTH): 65 | """ 66 | Pad sequence by repeating it. 67 | :param sequence: Segmented sequence to pad. 68 | :param source_sequence: Original, complete sequence. 69 | :param length: Length of sequence to pad up to. 70 | :return: Padded sequence of lenth length. 71 | """ 72 | assert len(sequence) < length, len(sequence) 73 | assert sequence == source_sequence or len(source_sequence) > length 74 | if len(source_sequence) > length: 75 | ret = source_sequence[len(source_sequence)-len(sequence)-(length-len(sequence)): 76 | len(source_sequence)-len(sequence)]+sequence 77 | 78 | else: 79 | assert sequence == source_sequence 80 | ret = (source_sequence * (int(length / len(sequence)) + 1))[:length] 81 | assert len(ret) == length 82 | return ret 83 | 84 | 85 | def frag2matrix(fragment, frag_max=SEGMENT_LENGTH): 86 | """ 87 | Convert sequence fragment to one-hot binary matrix to use in model converted from Matlab. 88 | :param fragment: Sequence fragment to convert. 89 | :param frag_max: Fragment should be at most this length. 90 | :return: Numpy matrix representing one-hot encoded sequence. 91 | """ 92 | assert len(fragment) <= frag_max 93 | 94 | # Create a dataframe to represent the sequence 95 | ret = np.zeros((frag_max, 4)) 96 | 97 | # Set each base in the matrix 98 | for idx, base in enumerate(fragment): 99 | ret[idx, DEFAULT_NUC_ORDER[base]] = 1 100 | 101 | return np.array(ret) 102 | 103 | 104 | def frags2matrices(fragments, frag_max=SEGMENT_LENGTH): 105 | """ 106 | Convert list of sequence fragments to one-hot binary matrices to use in model converted from Matlab. 107 | :param fragments: List of sequences to convert. 108 | :param frag_max: Fragments should be at most this length. 109 | :return: Array of numpy matrices representing one-hot encoded sequence. 110 | """ 111 | matrices = [] 112 | 113 | for frag in fragments: 114 | matrices.append(frag2matrix(frag, frag_max)) 115 | 116 | return np.array(pad_sequences(matrices, maxlen=frag_max, dtype='float', padding="post")) 117 | 118 | 119 | def encode_sequence(sequence, nuc_order=None): 120 | """ 121 | Encode a sequence to integers for use in Python LSTM model. 122 | :param sequence: Sequence to encode. 123 | :param nuc_order: Order of nucleotides for encoding. 124 | :return: Encoded sequence as integers. 125 | """ 126 | if nuc_order is None: 127 | nuc_order = DEFAULT_NUC_ORDER 128 | 129 | sequence = sequence.upper() 130 | accepted_nucleotides = "".join(nuc_order.keys()) 131 | 132 | assert re.match('^[{}]+$'.format(accepted_nucleotides), sequence) is not None, \ 133 | "Only {} allowed".format(accepted_nucleotides) 134 | 135 | encoded_seq = [(nuc_order[x] + 1) for x in sequence] 136 | encoded_seq = np.array([encoded_seq]) 137 | 138 | return encoded_seq 139 | 140 | 141 | def encode_sequences(sequences, nuc_order=None, segment_length=SEGMENT_LENGTH): 142 | """ 143 | Encode a sequence to integers for use in model. 144 | :param sequences: List of sequences to encode. 145 | :param nuc_order: Order of nucleotides for encoding. 146 | :param segment_length: Segments should be at most this length. 147 | :return: Encoded sequence as integers. 148 | """ 149 | encoded_seqs = [] 150 | for sequence in sequences: 151 | assert len(sequence) <= segment_length 152 | encoded_seqs.append(encode_sequence(sequence, nuc_order)[0]) 153 | 154 | return np.array(pad_sequences(encoded_seqs, maxlen=segment_length, padding="post")) 155 | 156 | 157 | def segment_sequence(sequence, segment_length=SEGMENT_LENGTH): 158 | """ 159 | Convert a sequence into list of equally sized segments. 160 | :param sequence: Sequence of A, C, G, T. 161 | :param segment_length: Length of segment to divide into. 162 | :return: List of segments of size segment_length. 163 | """ 164 | assert len(sequence) > 200, "Sequence is fewer than 200 bases, minimum input is 200 bases" 165 | ret = [] 166 | 167 | for start_idx in range(0, len(sequence), segment_length): 168 | fragment = sequence[start_idx:(start_idx + segment_length)] 169 | assert len(fragment) <= segment_length 170 | if len(fragment) < segment_length: 171 | fragment = pad_sequence(fragment, sequence, segment_length) 172 | ret.append(fragment.upper()) 173 | 174 | return ret 175 | 176 | 177 | def segment_fasta(fasta, segment_lengths=SEGMENT_LENGTH): 178 | """ 179 | Parse Fasta into segments. 180 | :param fasta: File handle in Fasta format. 181 | :param segment_lengths: Length of segments for model. 182 | :return: Dictionary of Fasta name -> list of segments. 183 | """ 184 | ret = OrderedDict() 185 | seq_name = None 186 | seq_value = None 187 | 188 | count = 0 189 | for line in fasta: 190 | line = line.strip() 191 | if line.startswith(">"): 192 | # Reached a new sequence in Fasta, if this is not the first sequence let's save the previous one 193 | random.seed(243789) 194 | if seq_name is not None: # This is not the first sequence 195 | assert seq_value is not None, seq_name 196 | 197 | # Save the sequence to a dictionary 198 | ret[seq_name] = segment_sequence(seq_value, segment_lengths) 199 | count += 1 200 | print("Read Fasta entry {}, total {} entries read".format(seq_name, count), file=sys.stderr) 201 | 202 | seq_name = line.lstrip(">").split()[0] 203 | seq_value = "" 204 | else: 205 | seq_value += handle_non_ATGC(line) 206 | 207 | # Write last entry 208 | ret[seq_name] = segment_sequence(seq_value) 209 | count += 1 210 | print("Read Fasta entry {}, total {} entries read".format(seq_name, count), file=sys.stderr) 211 | 212 | return ret 213 | 214 | 215 | def convolute_scores(score, window_size=None): 216 | """ 217 | Returns a convolution of the score with an average moving window. 218 | :param score: scores to convolute. 219 | :param window_size: Window size for convolution. 220 | :return: Convolution in list format. 221 | """ 222 | if window_size is None: 223 | window_size = 10 if len(score>20) else 3 224 | 225 | cv = [np.mean(score[i:i+window_size]) for i in range(len(score))] 226 | 227 | return cv 228 | 229 | 230 | def get_prophage_coords(sc, seq, phage_threshold=0.5, skip=50, edge=15000, 231 | windsz=10, interval=SEGMENT_LENGTH, plenMN=0): 232 | """ 233 | Given a list of scores, return predicted prophage coordinates. 234 | Prophage parameters should be set according to users needs. 235 | 236 | Parameters to modify sensitivity of detection: 237 | :param sc: list of scores assigned to segments of the sequence. 238 | :param seq: Complete sequence. 239 | :param phage_threshold: Seeker threshold for phage selection 240 | :param skip: segments to skip to concatenate high confidence regions 241 | :param edge: sequence edges to add to high confidence region 242 | :param windsz: Window size 243 | :param interval: intervals of segments 244 | :param plenMN: minimal length of detected prophage to consider 245 | :return: List of prophage coordinates. 246 | """ 247 | EPS = 0.1 248 | if np.mean(sc) > (phage_threshold-EPS/2): 249 | sc2 = sc 250 | sc = [max(i-(np.mean(sc)-phage_threshold+EPS),0) for i in sc2] 251 | 252 | addL = edge+5000 253 | addR = edge+10000 254 | CNT = 0; ToStop = 0; WS = windsz 255 | try: 256 | while ToStop == 0: 257 | covsc = convolute_scores(sc, WS-CNT) 258 | phlocs = [j for j, i in enumerate(covsc) if i > phage_threshold] 259 | 260 | starts = [] 261 | ends = [] 262 | prev = phlocs[0] 263 | for i in phlocs: 264 | if len(starts) > len(ends): 265 | if i > prev + skip: 266 | ends.append(prev * interval) 267 | starts.append(i * interval) 268 | elif len(starts) == len(ends): 269 | starts.append(prev * interval) 270 | prev = i 271 | if len(starts) > len(ends): 272 | ends.append(prev * interval) 273 | 274 | ret = zip(*[(max(starts[i]-addL, 0), min(ends[i]+addR, len(seq))) 275 | for i, j in enumerate(np.subtract(ends,starts)) if j>=plenMN]) 276 | NumPred = len(starts) 277 | 278 | if (1 <= NumPred <= 10) or CNT < 1: 279 | ToStop=1 280 | if NumPred<=1: 281 | CNT += 1 282 | if NumPred>=10: 283 | CNT -= 1 284 | 285 | except IndexError: # No prophages detected 286 | ret = [], [] 287 | 288 | return ret 289 | 290 | 291 | # Classes -------------------------------------------------------------------------------------------------------------- 292 | class SeekerModel: 293 | """ 294 | An instance of a sequence predictor that can differentiate bacterial DNA from phage DNA using python LSTM trained 295 | model. 296 | """ 297 | def __init__(self, LSTM_type="matlab", model_path=None,): 298 | """ 299 | Initialize instance of Seeker. Loads model and sets sequence prep function. 300 | :param LSTM_type: Which model to use. Python, Matlab or prophage. 301 | :param model_path: Supply path to model. Defaults to precompiled models in package. 302 | """ 303 | LSTM_type = LSTM_type.lower() 304 | assert LSTM_type in {"python", "matlab","prophage"}, \ 305 | "LSTM type must be python, matlab, or prophage not {}".format(LSTM_type) 306 | 307 | # Based on the LSTM type we can set the model and which function to use for sequence prep 308 | if LSTM_type == 'python' and model_path is None: 309 | model_path = resource_filename(__name__, "models/model.h5") 310 | self.__seq_prep = encode_sequences 311 | elif LSTM_type == 'matlab' and model_path is None: 312 | model_path = resource_filename(__name__, "models/MatModel0.h5") 313 | self.__seq_prep = frags2matrices 314 | elif LSTM_type == 'prophage' and model_path is None: 315 | model_path = resource_filename(__name__, "models/MatModelPRO.h5") 316 | self.__seq_prep = frags2matrices 317 | 318 | self.LSTM_type = LSTM_type 319 | self.model = load_lstm_model(model_path) 320 | 321 | def _score_fragments(self, fragments): 322 | """ 323 | Assigns scores for a list of all 1000 nucleotide fragments denoting whether it is bacteria or phage. 324 | :param fragments: List of all 1000 nucleotide fragment, IE several strings of A, C, G, T with length 1000. 325 | :return: Scores between 0 and 1, with 1 indicating phage and 0 indicating bacteria. 326 | """ 327 | assert {len(x) for x in fragments} == {1000} 328 | return self.model.predict(self.__seq_prep(fragments))[:, 1] 329 | 330 | def score_fasta_segments(self, segments_dict): 331 | """ 332 | Score sequences from a dictionary where each value is a list of sequence fragments. 333 | :param segments_dict: Dictionary of sequence name -> list of fragment sequences. 334 | :return: Returns a dictionary of sequence name -> list of fragment scores. 335 | """ 336 | ret = {} 337 | 338 | for name, segments in segments_dict.items(): 339 | print("Scoring Fasta sequence {}".format(name), file=sys.stderr) 340 | scores = self._score_fragments(segments) 341 | ret[name] = scores 342 | 343 | return ret 344 | 345 | def score_fasta(self, fasta_path): 346 | """ 347 | Score sequences from a Fasta file. 348 | :param fasta_path: Path to Fasta to score. 349 | :return: Returns a dictionary of sequence name -> list of fragment scores. 350 | """ 351 | with open(fasta_path) as fasta_file: 352 | segments_dict = segment_fasta(fasta_file) 353 | 354 | return self.score_fasta_segments(segments_dict) 355 | 356 | 357 | class SeekerFasta: 358 | """ 359 | A Fasta file to process with Seeker. 360 | """ 361 | def __init__(self, path_or_str, LSTM_type="python", seeker_model=None, load_seqs=True, is_fasta_str=False): 362 | """ 363 | Initialize SeekerFasta by loading and processing fasta at path. 364 | :param path_or_str: Either a path to a Fasta or a Fasta string. 365 | :param LSTM_type: Which LSTM implementation to use. Options are "python", "matlab", "prophage". 366 | :param seeker_model: Supply path to model. Defaults to precompiled models in package. 367 | :param load_seqs: If true, all sequences are saved to memory. 368 | :param is_fasta_str: Set to True if path_or_str is a Fasta string instead of a path. 369 | """ 370 | if seeker_model is None: 371 | seeker_model = SeekerModel(LSTM_type) 372 | 373 | if not is_fasta_str: 374 | assert os.path.isfile(path_or_str), "{} needs to be a path to a Fasta".format(path_or_str) 375 | self.path = path_or_str 376 | 377 | if load_seqs: 378 | self._seqs = segment_fasta(open(path_or_str)) 379 | self.scores = seeker_model.score_fasta_segments(self._seqs) 380 | else: 381 | self._seqs = None 382 | self.scores = seeker_model.score_fasta(path_or_str) 383 | else: 384 | self.path = None 385 | assert load_seqs 386 | self._seqs = segment_fasta(path_or_str.splitlines()) 387 | self.scores = seeker_model.score_fasta_segments(self._seqs) 388 | 389 | def phage_or_bacteria(self, phage_threshold=PHAGE_THRESHOLD, eval_str="{name}\t{kingdom}\t{score}"): 390 | """ 391 | For each sequence in Fasta, yields evaluation whether it is phage or bacteria. 392 | :param phage_threshold: Threshold to determine phage. 393 | :param eval_str: String template to use for output. 394 | :return: Iterator for each sequence entry. 395 | """ 396 | for name, scores in self.scores.items(): 397 | mean_score = sum(scores) / len(scores) 398 | if mean_score < phage_threshold: 399 | kingdom = "Bacteria" 400 | else: 401 | kingdom = "Phage" 402 | 403 | yield eval_str.format(name=name, kingdom=kingdom, score=round(mean_score, 2)) 404 | 405 | def meta2fasta(self, out_fasta_path="seeker_phage_contigs.fa", threshold=0.8, filter_func=lambda x: x[0:15] in x[-100:-1]): 406 | """ 407 | Saves contigs that are predicted as phages to a fasta file. 408 | 409 | The default Seeker threshold is set to 0.8 to detect phages with high confidence. This parameter should be set 410 | based on the user's specific goals. 411 | 412 | :param out_fasta_path: path for the output Fasta file. 413 | :param threshold: seeker threshold to use for phage prediction. 414 | :param filter_func: custom function the user can use to filter specific contigs. The default is circularity. 415 | """ 416 | orgname = (list(self.scores.keys())) 417 | scores = [(list(self.scores.items()))[i][1].tolist() for i in range(len(self.scores.items()))] 418 | msc = [sum(scores[i]) / len(scores[i]) for i in range(len(scores))] 419 | predv = [i for i, j in enumerate(msc) if j > threshold] 420 | 421 | with open(out_fasta_path, 'w') as faout: 422 | for i in range(len(predv)): 423 | fseq = "".join(self._seqs[orgname[predv[i]].replace('\n', '')]) 424 | if filter_func(fseq): 425 | faout.write('>'+orgname[predv[i]].replace('\n', '') + ', av_score: ' + str(msc[predv[i]]) + '\n') 426 | faout.write(fseq + '\n') 427 | 428 | def save2bed(self, out_path, 429 | phage_threshold=0.5, skip=50, edge=15000, windsz=10, interval=SEGMENT_LENGTH, plenMN=0): 430 | """ 431 | Saves prophage coordinates to bed file format. Prophage parameters should be set according to users needs. 432 | :param out_path: Output path for BED. 433 | :param phage_threshold: Seeker threshold for phage selection 434 | :param skip: segments to skip to concatenate high confidence regions 435 | :param edge: sequence edges to add to high confidence region 436 | :param windsz: Window size 437 | :param interval: intervals of segments 438 | :param plenMN: minimal length of detected prophage to consider. 439 | """ 440 | with open(out_path, 'w') as bedout: 441 | print('# sequence\tstart\tend', file=bedout) 442 | 443 | for sequence_name, scores in self.scores.items(): 444 | starts, ends = get_prophage_coords(scores, ''.join(list(self._seqs[sequence_name])), 445 | phage_threshold=phage_threshold, 446 | skip=skip, 447 | edge=edge, 448 | windsz=windsz, 449 | interval=interval, 450 | plenMN=plenMN) 451 | for start, end in zip(starts, ends): 452 | print(sequence_name, start, end, sep="\t", file=bedout) 453 | 454 | def save2fasta(self, out_path, 455 | phage_threshold=0.5, skip=50, edge=15000, windsz=10, interval=SEGMENT_LENGTH, plenMN=0): 456 | """ 457 | Saves prophage locations to fasta file format. Prophage parameters should be set according to users needs. 458 | 459 | :param out_path: Output path for BED. 460 | :param phage_threshold: Seeker threshold for phage selection 461 | :param skip: segments to skip to concatenate high confidence regions 462 | :param edge: sequence edges to add to high confidence region 463 | :param windsz: Window size 464 | :param interval: intervals of segments 465 | :param plenMN: minimal length of detected prophage to consider. 466 | """ 467 | assert self._seqs is not None, "load_seqs must be set to true on initialization in order to create phage Fasta" 468 | with open(out_path, 'w') as faout: 469 | for sequence_name, scores in self.scores.items(): 470 | starts, ends = get_prophage_coords(scores, ''.join(list(self._seqs[sequence_name])), 471 | phage_threshold=phage_threshold, 472 | skip=skip, 473 | edge=edge, 474 | windsz=windsz, 475 | interval=interval, 476 | plenMN=plenMN) 477 | for start, end in zip(starts, ends): 478 | sequence = "".join(self._seqs[sequence_name])[start:end] 479 | print( 480 | ">{}:{}-{}\n{}".format(sequence_name, str(start), str(end), sequence), 481 | file=faout 482 | ) 483 | -------------------------------------------------------------------------------- /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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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. 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 | --------------------------------------------------------------------------------