DeepNOG: fast and accurate protein orthologous group assignment

deepnog is a Python package for assigning proteins to orthologous groups (eggNOG 5) with deep networks.

Installation

Installation from PyPI

The current release of deepnog can be installed from PyPI:

pip install deepnog

For typical use cases, and quick start, this is sufficient. Note that this guide assumes Linux, and may work under macOS. We currently don’t provide detailed instructions for Windows.

Alternative: Installation from bioconda

Alternatively, deepnog can be installed from bioconda with channel setup as described in the bioconda docs like this:

conda install deepnog

Note: Previously, bioconda required installing PyTorch from Facebook’s pytorch channel manually. Since PyTorch is now available on conda-forge, this is not necessary anymore.

Dependencies and model files

All package dependencies of deepnog are automatically installed by pip or conda. We also require model files (= networks parameters/weights), which are too large for GitHub/PyPI/bioconda. Models are hosted on separate servers, and downloaded automatically by deepnog, when required. By default, models are cached in $HOME/deepnog_data/.

You can change this path by setting the DEEPNOG_DATA environment variable. Choose among the following options to do so:

# Set data path temporarily
DEEPNOG_DATA="/custom/path/models" deepnog infer sequences.fa

# Set data path for the current shell
export DEEPNOG_DATA="/custom/path/models"

# Set data path permanently
printf "\n# Set path to DeepNOG models\nexport DEEPNOG_DATA=\"/custom/path/models\"\n" >> ~/.bashrc

Installation from source

You can always grab the latest version of deepnog directly from GitHub:

cd install_dir
git clone git@github.com:univieCUBE/deepnog.git
cd deepnog
pip install -e .

This is the recommended approach, if you want to contribute to the development of deepnog.

Supported platforms

deepnog currently supports all major operating systems:

• Linux

• MacOS X

• Windows

Quick Start Example

The following example shows all these steps for predicting protein orthologous groups with the command line interface of deepnog as well as using the Python API. Please make sure you have installed deepnog (installation instructions).

CLI Usage Example

Using deepnog from the command line is the simple, and preferred way of interacting with the deepnog package.

Here, we assign orthologous groups (OGs) of proteins using a model trained on the eggNOG 5.0 database and using only bacterial OGs (default settings), and redirect the output from stdout to a file:

deepnog infer input.fa > assignments.csv

Alternatively, the output file and other settings can be specified explicitly like so:

deepnog infer input.fa --out prediction.csv -db eggNOG5 --tax 2

For a detailed explanation of flags and further settings, please consult the User Guide.

Note that deepnog masks predictions below a certain confidence threshold. The default confidence threshold baked into the model at 0.99 can be overridden from the command line interface:

deepnog infer input.fa --confidence-threshold 0.8 > assignments.csv

The output comma-separated values (CSV) file assignments.csv then looks something like:

sequence_id,prediction,confidence
WP_004995615.1,COG5449,0.99999964
WP_004995619.1,COG0340,1.0
WP_004995637.1,COG4285,1.0
WP_004995655.1,COG4118,1.0
WP_004995678.1,COG0184,1.0
WP_004995684.1,COG1137,1.0
WP_004995690.1,COG0208,1.0
WP_004995697.1,,
WP_004995703.1,COG0190,1.0

The file contains a single line for each protein in the input sequence file, and the following fields:

• sequence_id, the name of the input protein sequence.

• prediction, the name of the predicted protein OG. Empty if masked by confidence threshold.

• confidence, the confidence value (0-1 inclusive) that deepnog ascribes to this assignment. Empty if masked by confidence threshold.

API Example Usage

import torch
from deepnog.data import ProteinIterableDataset
from deepnog.learning import predict
from deepnog.utils import create_df, get_config, get_weights_path, load_nn, set_device


PROTEIN_FILE = '/path/to/file.faa'
DATABASE = 'eggNOG5'
TAX = 2
ARCH = 'deepnog'
CONF_THRESH = 0.99

# load protein sequence file into a ProteinIterableDataset
dataset = ProteinIterableDataset(PROTEIN_FILE, f_format='fasta')

# Construct path to saved parameters deepnog model.
weights_path = get_weights_path(
    database=DATABASE,
    level=str(TAX),
    architecture=ARCH,
)

# Set up device for prediction
device = set_device('auto')
torch.set_num_threads(1)

# Load neural network parameters
model_dict = torch.load(weights_path, map_location=device)

# Lookup where to find the chosen network
config = get_config()
module = config['architecture'][ARCH]['module']
cls = config['architecture'][ARCH]['class']

# Load neural network model and class names
model = load_nn((module, cls), model_dict, 'infer', device)
class_labels = model_dict['classes']

# perform prediction
preds, confs, ids, indices = predict(
    model=model,
    dataset=dataset,
    device=device,
    batch_size=1,
    num_workers=1,
    verbose=3
)

# Construct results (a pandas DataFrame)
df = create_df(
    class_labels=class_labels,
    preds=preds,
    confs=confs,
    ids=ids,
    indices=indices,
    threshold=CONF_THRESH
)

The research article DeepNOG: Fast and accurate protein orthologous group assignment has been accepted by Bioinformatics and is expected to be published online in late 2020.

Citation will be added here as soon as it is available.

User Guide

Concepts

deepnog is a command line tool written in Python 3. It uses deep networks for extremely fast protein orthology assignments. Currently, it is based on a deep convolutional network architecture called DeepNOG trained on the root and bacterial level of the eggNOG 5.0 database (Huerta-Cepas et al. (2019)).

Two subcommand are available:

• deepnog infer for assigning sequences to orthologous groups, using precomputed models, and

• deepnog train for training such models (e.g. other taxonomic levels or future versions of eggNOG, different orthology databases, etc.)

deepnog infer for orthology assignments

Input Data

DeepNOG expects a protein sequence file as input. It is tested for the FASTA file format, but in general should support all file formats supported by the Bio.SeqIO module of Biopython. Compressed files (.gz or .xz) are supported as well. Protein sequences without IDs in the input data file are skipped and not used for the following assignment phase. Furthermore, if two sequences in the input data file have the same associated ID, only the sequence encountered first in the input data file will be kept and all others discarded before the output file is created. The user will be notified if such cases are encountered.

Assignment Phase

In the assignment phase, deepnog loads a predefined deep network and the corresponding trained weights (defaults to DeepNOG trained on eggNOG 5.0, bacterial level). Then it performs the assignment by forwarding the input sequences through the network performing the calculations either on a CPU or GPU. deepnog offers single-process data loading aimed for calculations on a single CPU core to produce as little overhead as possible. Additionally, it offers parallel multiprocess data loading aimed for very fast GPU calculations. This is to provide the GPU with data following up the previous forward pass fast enough such that the GPU does not experience idling. In its default parametrization, deepnog is optimized for single core CPU calculations, or massively parallel GPU calculations.

Output Data

As an output deepnog generates a CSV file which consists of three columns:

1. The unique name or ID of the protein extracted from the sequence file,

2. the assigned orthologous group, and

3. the network’s confidence in the assignment.

Each deep network model has the possibility to define an assignment confidence threshold below which, the network’s output layer is treated as having predicted that the input protein sequence is not associated to any orthologous group in the model. Therefore, if the highest assignment confidence for any OG for a given input protein sequence is below this threshold, the assignment is left empty. Per default, using DeepNOG on eggNOG 5.0, the prediction confidence threshold is set to a strict 99%. This threshold can be adjusted by the user.

deepnog train for creating custom models

For details on training new models, see New models/architectures.

Deepnog CLI Documentation

Invocation:

deepnog infer SEQUENCE_FILE [options] > assignments.csv

Basic Commands

These options may be commonly tuned for a basic invocation for orthologous group assignment.

positional arguments:
  SEQUENCE_FILE         File containing protein sequences for classification.

optional arguments:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  -db {eggNOG5, cog2020}, --database {eggNOG5, cog2020}
                        Orthologous group/family database to use. (default:
                        eggNOG5)
  -t {1,2,[]}, --tax {1,2}
                        Taxonomic level to use in specified database
                        (1 = root, 2 = bacteria) (default: 2)
  -o FILE, --out FILE   Store orthologous group assignments to output file.
                        Per default, write predictions to stdout. (default: None)
  -c FLOAT, --confidence-threshold FLOAT
                        The confidence value below which predictions are
                        masked by deepnog. By default, apply the confidence
                        threshold saved in the model if one exists, and else
                        do not apply a confidence threshold. (default: None)

Advanced Commands

These options are unlikely to require manual tuning for the average user.

--verbose INT       Define verbosity of DeepNOGs output written to stdout
                    or stderr. 0 only writes errors to stderr which cause
                    DeepNOG to abort and exit. 1 also writes warnings to
                    stderr if e.g. a protein without an ID was found and
                    skipped. 2 additionally writes general progress
                    messages to stdout.3 includes a dynamic progress bar
                    of the prediction stage using tqdm. (default: 3)
-ff STR, --fformat STR
                    File format of protein sequences. Must be supported by
                    Biopythons Bio.SeqIO class. (default: fasta)
-of {csv,tsv,legacy}  --outformat {csv,tsv,legacy}
                    The file format of the output file produced by
                    deepnog. (default: csv)
-d {auto,cpu,gpu}, --device {auto,cpu,gpu}
                    Define device for calculating protein sequence
                    classification. Auto chooses GPU if available,
                    otherwise CPU. (default: auto)
-nw INT, --num-workers INT
                    Number of subprocesses (workers) to use for data
                    loading. Set to a value <= 0 to use single-process
                    data loading. Note: Only use multi-process data
                    loading if you are calculating on a gpu (otherwise
                    inefficient)! (default: 0)
-a {deepnog}, --architecture {deepnog}
                    Network architecture to use for classification.
                    (default: deepnog)
-w FILE, --weights FILE
                    Custom weights file path (optional) (default: None)
-bs INT, --batch-size INT
                    The batch size determines how many sequences are
                    processed by the network at once. If 1, process the
                    protein sequences sequentially (recommended
                    on CPUs). Larger batch sizes speed up the inference and
                    training on GPUs. Batch size can influence the
                    learning process.
--test_labels TEST_LABELS_FILE
                    Measure model performance on a test set.
                    If provided, this file must contain the ground-truth
                    labels for the provided sequences.
                    Otherwise, only perform inference.

API Documentation

This is the API documentation for deepnog.

DeepNOG

DeepNOG is a deep learning based command line tool to infer orthologous groups of given protein sequences. It provides a number of models for eggNOG orthologous groups, and allows to train additional models for eggNOG or other databases.

deepnog.client package

deepnog.client.client module

Authors

• Roman Feldbauer

• Lukas Gosch

Date

2019-10-18

Usage

python client.py –help

Description

Provides the deepnog command line client and entry point for users.

DeepNOG predicts protein families/orthologous groups of given protein sequences with deep learning.

Since version 1.2, model training is available as well.

File formats supported: Preferred: FASTA DeepNOG supports protein sequences stored in all file formats listed in https://biopython.org/wiki/SeqIO but is tested for the FASTA-file format only.

Architectures supported:

Databases supported:

• eggNOG 5.0, taxonomic level 1 (root)

• eggNOG 5.0, taxonomic level 2 (bacteria)

• Additional databases will be trained on demand/users can add custom databases using the training facilities.

deepnog.client.client.main()

DeepNOG command line tool.

deepnog.data package

deepnog.data.dataset module

Author: Lukas Gosch

Date: 2019-10-03

Description:

Dataset classes and helper functions for usage with deep network models written in PyTorch.

class deepnog.data.dataset.ProteinDataset(*args: Any, **kwargs: Any)

Bases: torch.utils.data.Dataset

Protein dataset with sequences and labels for training.

If sequences and labels are provided as files rather than objects, loads and stores all proteins from input files during construction. While this comes at the price of some delay, it allows to truly shuffle the complete dataset during training.

Parameters

• sequences (list, str, Path) – Protein sequences as list of Biopython Seq, or path to fasta file containing the sequences.

• labels (DataFrame, str, Path, optional) – Protein orthologous group labels as DataFrame, or str to CSV file containing such a dataframe. This is required for training, and ignored during inference. Must be in CSV format with header line and index column, that is, compatible to be read by pandas.read_csv(…, index_col=0). The labels are expected in a column named “eggnog_id” or in the last column, and sequence IDs in a column “protein_id”.

• f_format (str, optional) – File format in which to expect the protein sequences. Must be supported by Biopython’s Bio.SeqIO class.

• label_encoder (LabelEncoder, optional) – The label encoder maps str class names to numerical labels. Provide a label encoder during validation.

• verbose (int, optional) – Control verbosity of logging.

class deepnog.data.dataset.ProteinIterableDataset(*args: Any, **kwargs: Any)

Bases: torch.utils.data.IterableDataset

Protein dataset holding the proteins to classify.

Does not load and store all proteins from a given sequence file but only holds an iterator to the next sequence to load.

Thread safe class allowing for multi-worker loading of sequences from a given datafile.

Parameters

• file (str) – Path to file storing the protein sequences.

• labels_file (str, optional) – Path to file storing labels associated to the sequences. This is required for training, and ignored during inference. Must be in CSV format with header line and index column, that is, compatible to be read by pandas.read_csv(…, index_col=0). The labels are expected in a column named “eggnog_id” or in the last column.

• f_format (str) – File format in which to expect the protein sequences. Must be supported by Biopython’s Bio.SeqIO class.

• label_encoder (LabelEncoder, optional) – The label encoder maps str class names to numerical labels. Provide a label encoder during validation.

class deepnog.data.dataset.ProteinIterator(file_, labels: pandas.DataFrame, aa_vocab, f_format, n_skipped: Union[int, deepnog.utils.sync.SynchronizedCounter] = 0, num_workers=1, worker_id=0)

Bases: object

Iterator allowing for multiprocess data loading of a sequence file.

ProteinIterator is a wrapper for the iterator returned by Biopython’s Bio.SeqIO class when parsing a sequence file. It specifies custom __next__() method to support single- and multi-process data loading.

In the single-process loading case, nothing special happens, the ProteinIterator sequentially iterates over the data file. In the end, it informs the main module about the number of skipped sequences (due to empty ids) through setting a global variable in the main module.

In the multi-process loading case, each ProteinIterator loads a sequence and then skips the next few sequences dedicated to the other workers. This works by each worker skipping num_worker - 1 data samples for each call to __next__(). Furthermore, each worker skips worker_id data samples in the initialization.

The ProteinIterator class also makes sure that a unique ID is set for each SeqRecord obtained from the data-iterator. This allows unambiguous handling of large protein datasets which may have duplicate IDs from merging multiple sources or may have no IDs at all. For easy and efficient sorting of batches of sequences as well as for direct access to the original IDs, the index is stored separately.

Parameters

• file (str) – Path to sequence file, from which an iterator over the sequences will be created with Biopython’s Bio.SeqIO.parse() function.

• labels (pd.DataFrame) – Dataframe storing labels associated to the sequences. This is required for training, and ignored during inference. Must contain ‘protein_id’ and ‘label_num’ columns providing identifiers and numerical labels.

• aa_vocab (dict) – Amino-acid vocabulary mapping letters to integers

• f_format (str) – File format in which to expect the protein sequences. Must be supported by Biopython’s Bio.SeqIO class.

• num_workers (int) – Number of workers set in DataLoader or one if no workers set. If bigger or equal to two, the multi-process loading case happens.

• worker_id (int) – ID of worker this iterator belongs to

class deepnog.data.dataset.ShuffledProteinIterableDataset(*args: Any, **kwargs: Any)

Bases: deepnog.data.dataset.ProteinIterableDataset

Shuffle an iterable ProteinDataset by introducing a shuffle buffer.

Parameters

• file (str) – Path to file storing the protein sequences.

• labels_file (str, optional) – Path to file storing labels associated to the sequences. This is required for training, and ignored during inference. Must be in CSV format with header line and index column, that is, compatible to be read by pandas.read_csv(…, index_col=0). The labels are expected in a column named “eggnog_id” or in the last column.

• f_format (str) – File format in which to expect the protein sequences. Must be supported by Biopython’s Bio.SeqIO class.

• label_encoder (LabelEncoder, optional) – The label encoder maps str class names to numerical labels. Provide a label encoder during validation.

• buffer_size (int) – How many objects will be buffered, i.e. are available to choose from.

References

Adapted from code by Sharvil Nanavati, see https://discuss.pytorch.org/t/how-to-shuffle-an-iterable-dataset/64130/5

deepnog.data.dataset.collate_sequences(batch: Union[List[deepnog.data.dataset.sequence], deepnog.data.dataset.sequence], zero_padding: bool = True, min_length: int = 36, random_padding: bool = False) → deepnog.data.dataset.collated_sequences

Collate and zero-pad encoded sequence.

Parameters

• batch (namedtuple, or list of namedtuples) – Batch of protein sequences to classify stored as a namedtuple sequence.

• zero_padding (bool) – Zero-pad protein sequences, that is, append zeros until every sequence is as long as the longest sequences in batch. NOTE: currently unused. Zero-padding is always performed.

• min_length (int, optional) – Zero-pad sequences to at least min_length. By default, this is set to 36, which is the largest kernel size in the default DeepNOG architecture.

• random_padding (bool, optional) – Zero pad sequences by prepending and appending zeros. The fraction is determined randomly. This may counter detrimental effects, when short sequences would always have long zero-tails, otherwise.

Returns

batch – Input batch zero-padded and stored in namedtuple collated_sequences.

Return type

NamedTuple

deepnog.data.dataset.gen_amino_acid_vocab(alphabet=None)

Create vocabulary for protein sequences.

A vocabulary is defined as a mapping from the amino-acid letters in the alphabet to numbers. As this mapping is aware of zero-padding, it maps the first letter in the alphabet to 1 instead of 0.

Parameters

alphabet (str) – Alphabet to use for vocabulary. If None, use ‘ACDEFGHIKLMNPQRSTVWYBXZJUO’ (equivalent to deprecated Biopython’s ExtendedIUPACProtein).

Returns

vocab – Mapping of amino acid characters to numbers.

Return type

dict

deepnog.data.split module

class deepnog.data.split.DataSplit(X_train: pandas.DataFrame, X_val: pandas.DataFrame, X_test: pandas.DataFrame, y_train: pandas.DataFrame, y_val: pandas.DataFrame, y_test: pandas.DataFrame, uniref_train: Optional[pandas.DataFrame], uniref_val: Optional[pandas.DataFrame], uniref_test: Optional[pandas.DataFrame])

Bases: object

Class for returned data, labels, and groups after train/val/test split.

X_test: pandas.DataFrame

X_train: pandas.DataFrame

X_val: pandas.DataFrame

uniref_test: Optional[pandas.DataFrame]

uniref_train: Optional[pandas.DataFrame]

uniref_val: Optional[pandas.DataFrame]

y_test: pandas.DataFrame

y_train: pandas.DataFrame

y_val: pandas.DataFrame

deepnog.data.split.group_train_val_test_split(df: pandas.DataFrame, train_ratio: float = 0.96, validation_ratio: float = 0.02, test_ratio: float = 0.02, random_state: int = 123, with_replacement: bool = True, verbose: int = 0) → deepnog.data.split.DataSplit

Create training/validation/test split for deepnog experiments.

Takes UniRef cluster IDs into account, that is, makes sure that sequences from the same cluster go into the same set. In other words, training, validation, and test sets are disjunct in terms of UniRef clusters.

Parameters

• df (pandas DataFrame) – Must contain ‘string_id’, ‘eggnog_id’, ‘uniref_id’ columns

• train_ratio (float) – Fraction of total sequences for training set

• validation_ratio (float) – Fraction of total sequences for validation set

• test_ratio (float) – Fraction of total sequences for test set

• random_state (int) – Set random state for reproducible results

• with_replacement (bool) – By default, scikit-learn GroupShuffleSplit samples objects with replacement. Disabling replacement removes

• verbose (int) – Level of logging verbosity

Returns

data_split – Split X, y, groups

Return type

NamedTuple

deepnog.data.split.train_val_test_split(df: pandas.DataFrame, train_ratio: float = 0.96, validation_ratio: float = 0.02, test_ratio: float = 0.02, stratify: bool = True, shuffle: bool = True, random_state: int = 123, verbose: int = 0) → deepnog.data.split.DataSplit

Create training/validation/test split for deepnog experiments.

Does not take UniRef clusters into account. Do not use for UniRef50/90 experiments.

Parameters

• df (pandas DataFrame) – Must contain ‘string_id’, ‘eggnog_id’ columns

• train_ratio (float) – Fraction of total sequences for training set

• validation_ratio (float) – Fraction of total sequences for validation set

• test_ratio (float) – Fractino of total sequences for test set

• stratify (bool) – Stratify the splits according to the orthology labels

• shuffle (bool) – Shuffle the sequences

• random_state (int) – Set random state for reproducible results

• verbose (int) – Level of logging verbosity

Returns

data_split – Split X, y, groups

Return type

DataSplit

deepnog.learning package

deepnog.learning.inference module

Author: Roman Feldbauer

Date: 2020-02-19

Description:

Predict orthologous groups of protein sequences.

deepnog.learning.inference.predict(model, dataset, device='cpu', batch_size=16, num_workers=4, verbose=3)

Use model to predict zero-indexed labels of dataset.

Also handles communication with ProteinIterators used to load data to log how many sequences have been skipped due to having empty sequence ids.

Parameters

• model (nn.Module) – Trained neural network model.

• dataset (ProteinIterableDataset) – Data to predict protein families for.

• device ([str, torch.device]) – Device of model.

• batch_size (int) – Forward batch_size proteins through neural network at once.

• num_workers (int) – Number of workers for data loading.

• verbose (int) – Define verbosity.

Returns

• preds (torch.Tensor, shape (n_samples,)) – Stores the index of the output-node with the highest activation

• confs (torch.Tensor, shape (n_samples,)) – Stores the confidence in the prediction

• ids (list[str]) – Stores the (possible empty) protein labels extracted from data file.

• indices (list[int]) – Stores the unique indices of sequences mapping to their position in the file

deepnog.learning.training module

Author: Roman Feldbauer

Date: 2020-06-03

Description:

Training deep networks for protein orthologous group prediction.

deepnog.learning.training.fit(architecture, module, cls, training_sequences, validation_sequences, training_labels, validation_labels, *, data_loader_params: Optional[dict] = None, iterable_dataset: bool = False, n_epochs: int = 15, shuffle: bool = False, learning_rate: float = 0.01, learning_rate_params: Optional[dict] = None, l2_coeff: Optional[float] = None, optimizer_cls=torch.optim.Adam, device: Union[str, torch.device] = 'auto', tensorboard_dir: Union[None, str] = 'auto', log_interval: int = 100, random_seed: Optional[int] = None, save_each_epoch: bool = True, out_dir: Optional[pathlib.Path] = None, experiment_name: Optional[str] = None, config_file: Optional[str] = None, verbose: int = 2) → deepnog.learning.training.train_val_result

Perform training and validation of a given model, data, and hyperparameters.

Parameters

• architecture (str) – Network architecture, must be available in deepnog/models

• module (str) – Python module containing the network definition (inside deepnog/models/).

• cls (str) – Python class name of the network (inside deepnog/models/{module}.py).

• training_sequences (str, Path) – File with training set sequences

• validation_sequences (str, Path) – File with validation set sequences

• training_labels (str, Path) – File with class labels (orthologous groups) of training sequences

• validation_labels (str, Path) – File with class labels (orthologous groups) of validation sequences

• data_loader_params (dict) – Parameters passed to PyTorch DataLoader construction

• iterable_dataset (bool, default False) – Use an iterable dataset that does not load all sequences in advance. While this saves memory and does not involve the delay at start, random sampling is impaired, and requires a shuffle buffer.

• n_epochs (int) – Number of training passes over the complete training set

• shuffle (bool) – Shuffle the training data. This does NOT shuffle the complete data set, which requires having all sequences in memory, but uses a shuffle buffer (default size: 2**16), from which sequences are drawn.

• learning_rate (float) – Learning rate, the central hyperparameter of deep network training. Too high values may lead to diverging solutions, while too low values result in slow learning.

• learning_rate_params (dict) – Parameters passed to the learning rate Scheduler.

• l2_coeff (float) – If not None, regularize training by L2 norm of network weights

• optimizer_cls – Class of PyTorch optimizer

• device (torch.device) – Use either ‘cpu’ or ‘cuda’ (GPU) for training/validation.

• tensorboard_dir (str) – Save online learning statistics for tensorboard in this directory.

• log_interval (int, optional) – Print intermediary results after log_interval minibatches

• random_seed (int) – Set a random seed for numpy/pytorch for reproducible results.

• save_each_epoch (bool) – Save the network after each training epoch

• out_dir (Path) – Path to the output directory used to save models during training

• experiment_name (str) – Prefix of model files saved during training

• config_file (str) – Override path to config file, e.g. for custom models in unit tests

• verbose (int) – Increasing levels of messages

Returns

results –

A namedtuple containing:

• the trained deep network model

• training dataset

• evaluation statistics

• the ground truth labels (y_true)

• the predicted labels (y_pred).

Return type

namedtuple

deepnog.models package

deepnog.models.deepencoding module

deepnog.models.deepfam module

Author: Lukas Gosch, Roman Feldbauer

class deepnog.models.deepfam.DeepFam(*args: Any, **kwargs: Any)

Bases: torch.nn.Module

Convolutional network for protein family prediction.

PyTorch implementation of DeepFam architecture (original: TensorFlow).

Parameters

model_dict (dict) – Dictionary storing the hyperparameters and learned parameters of the model.

forward(x)

Forward a batch of sequences through network.

Parameters

x (Tensor, shape (batch_size, sequence_len)) – Sequence or batch of sequences to classify. Assumes they are translated using a vocabulary. (See gen_amino_acid_vocab in dataset.py)

Returns

out – Confidence of sequence(s) beeing in one of the n_classes.

Return type

Tensor, shape (batch_size, n_classes)

class deepnog.models.deepfam.DeepFamAblation1(*args: Any, **kwargs: Any)

Bases: deepnog.models.deepfam.DeepFamAblationBase

Ablation study of DeepFam to DeepNOG transition.

Change 1: WordEmbedding instead of PseudoOneHot Change 2: SELU instead of BN/ReLU Change 3: Drop the fully connected layer between ConvNet and classification Combinations: 12, 13, 23, 123 (=DeepNOG).

Parameters

model_dict (dict) – Dictionary storing the hyperparameters and learned parameters of the model.

forward(x)

Forward a batch of sequences through network.

Parameters

x (Tensor, shape (batch_size, sequence_len)) – Sequence or batch of sequences to classify. Assumes they are translated using a vocabulary. (See gen_amino_acid_vocab in dataset.py)

Returns

out – Confidence of sequence(s) being in one of the n_classes.

Return type

Tensor, shape (batch_size, n_classes)

class deepnog.models.deepfam.DeepFamAblation12(*args: Any, **kwargs: Any)

Bases: deepnog.models.deepfam.DeepFamAblationBase

Ablation study of DeepFam to DeepNOG transition.

Change 1: WordEmbedding instead of PseudoOneHot Change 2: SELU instead of BN/ReLU Change 3: Drop the fully connected layer between ConvNet and classification Combinations: 12, 13, 23, 123 (=DeepNOG).

Parameters

model_dict (dict) – Dictionary storing the hyperparameters and learned parameters of the model.

forward(x)

Forward a batch of sequences through network.

Parameters

x (Tensor, shape (batch_size, sequence_len)) – Sequence or batch of sequences to classify. Assumes they are translated using a vocabulary. (See gen_amino_acid_vocab in dataset.py)

Returns

out – Confidence of sequence(s) being in one of the n_classes.

Return type

Tensor, shape (batch_size, n_classes)

class deepnog.models.deepfam.DeepFamAblation123(*args: Any, **kwargs: Any)

Bases: deepnog.models.deepfam.DeepFamAblationBase

Ablation study of DeepFam to DeepNOG transition.

Change 1: WordEmbedding instead of PseudoOneHot Change 2: SELU instead of BN/ReLU Change 3: Drop the fully connected layer between ConvNet and classification Combinations: 12, 13, 23, 123 (=DeepNOG).

Parameters

model_dict (dict) – Dictionary storing the hyperparameters and learned parameters of the model.

forward(x)

Forward a batch of sequences through network.

Parameters

x (Tensor, shape (batch_size, sequence_len)) – Sequence or batch of sequences to classify. Assumes they are translated using a vocabulary. (See gen_amino_acid_vocab in dataset.py)

Returns

out – Confidence of sequence(s) beeing in one of the n_classes.

Return type

Tensor, shape (batch_size, n_classes)

class deepnog.models.deepfam.DeepFamAblation13(*args: Any, **kwargs: Any)

Bases: deepnog.models.deepfam.DeepFamAblationBase

Ablation study of DeepFam to DeepNOG transition.

Change 1: WordEmbedding instead of PseudoOneHot Change 2: SELU instead of BN/ReLU Change 3: Drop the fully connected layer between ConvNet and classification Combinations: 12, 13, 23, 123 (=DeepNOG).

Parameters

model_dict (dict) – Dictionary storing the hyperparameters and learned parameters of the model.

forward(x)

Forward a batch of sequences through network.

Parameters

x (Tensor, shape (batch_size, sequence_len)) – Sequence or batch of sequences to classify. Assumes they are translated using a vocabulary. (See gen_amino_acid_vocab in dataset.py)

Returns

out – Confidence of sequence(s) being in one of the n_classes.

Return type

Tensor, shape (batch_size, n_classes)

class deepnog.models.deepfam.DeepFamAblation2(*args: Any, **kwargs: Any)

Bases: deepnog.models.deepfam.DeepFamAblationBase

Ablation study of DeepFam to DeepNOG transition.

Change 1: WordEmbedding instead of PseudoOneHot Change 2: SELU instead of BN/ReLU Change 3: Drop the fully connected layer between ConvNet and classification Combinations: 12, 13, 23, 123 (=DeepNOG).

Parameters

model_dict (dict) – Dictionary storing the hyperparameters and learned parameters of the model.

forward(x)

Forward a batch of sequences through network.

Parameters

x (Tensor, shape (batch_size, sequence_len)) – Sequence or batch of sequences to classify. Assumes they are translated using a vocabulary. (See gen_amino_acid_vocab in dataset.py)

Returns

out – Confidence of sequence(s) being in one of the n_classes.

Return type

Tensor, shape (batch_size, n_classes)

class deepnog.models.deepfam.DeepFamAblation23(*args: Any, **kwargs: Any)

Bases: deepnog.models.deepfam.DeepFamAblationBase

Ablation study of DeepFam to DeepNOG transition.

Change 1: WordEmbedding instead of PseudoOneHot Change 2: SELU instead of BN/ReLU Change 3: Drop the fully connected layer between ConvNet and classification Combinations: 12, 13, 23, 123 (=DeepNOG).

Parameters

model_dict (dict) – Dictionary storing the hyperparameters and learned parameters of the model.

forward(x)

Forward a batch of sequences through network.

Parameters

x (Tensor, shape (batch_size, sequence_len)) – Sequence or batch of sequences to classify. Assumes they are translated using a vocabulary. (See gen_amino_acid_vocab in dataset.py)

Returns

out – Confidence of sequence(s) beeing in one of the n_classes.

Return type

Tensor, shape (batch_size, n_classes)

class deepnog.models.deepfam.DeepFamAblation3(*args: Any, **kwargs: Any)

Bases: deepnog.models.deepfam.DeepFamAblationBase

Ablation study of DeepFam to DeepNOG transition.

Change 1: WordEmbedding instead of PseudoOneHot Change 2: SELU instead of BN/ReLU Change 3: Drop the fully connected layer between ConvNet and classification Combinations: 12, 13, 23, 123 (=DeepNOG).

Parameters

model_dict (dict) – Dictionary storing the hyperparameters and learned parameters of the model.

forward(x)

Forward a batch of sequences through network.

Parameters

x (Tensor, shape (batch_size, sequence_len)) – Sequence or batch of sequences to classify. Assumes they are translated using a vocabulary. (See gen_amino_acid_vocab in dataset.py)

Returns

out – Confidence of sequence(s) being in one of the n_classes.

Return type

Tensor, shape (batch_size, n_classes)

deepnog.models.deepnog module

Author: Lukas Gosch, Roman Feldbauer

Date: 2019-10-09

Description: Convolutional networks for protein orthologous group assignment.

class deepnog.models.deepnog.AminoAcidWordEmbedding(*args: Any, **kwargs: Any)

Bases: torch.nn.Module

PyTorch nn.Embedding where each amino acid is considered one word.

Parameters

embedding_dim (int) – Embedding dimensionality.

forward(sequence)

Embed a given sequence.

Parameters

sequence (Tensor) – The sequence or a batch of sequences to embed. They are assumed to be translated to numerical values given a generated vocabulary (see gen_amino_acid_vocab in dataset.py)

Returns

x – The sequence (densely) embedded in a space of dimension embedding_dim.

Return type

Tensor

class deepnog.models.deepnog.DeepNOG(*args: Any, **kwargs: Any)

Bases: torch.nn.Module

Convolutional network for protein orthologous group prediction.

Compared to DeepFam, this architecture provides:

• learned amino acid embeddings

• self-normalizing network with SELU

• sequence length independence

• stream-lined output layer

This networks consists of an embedding layer which learns a D-dimensional embedding for each amino acid. For a sequence of length L, the embedding has dimension DxL. A 1-D convolution with C filters of F different kernel- sizes K_i are performed over the embedding resulting in Cx(L-K_i-1) output dimension for each kernel size. SeLU activation is applied on the output followed by AdaptiveMaxPooling1D Layer reducing the dimension to of the output layer to Cx1 and resulting in the NN being sequence length independent. The max-pooling layer is followed up by a classic dropout Layer and then by a dense layer with as many output nodes as orthologous groups/protein families to classify.

Parameters

model_dict (dict) – Dictionary storing the hyperparameters and learned parameters of the model.

Notes

This architecture’s working title was DeepEncoding. The old name was last available in deepnog 1.2.2.

forward(x)

Forward a batch of sequences through network.

Parameters

x (Tensor, shape (batch_size, sequence_len)) – Sequence or batch of sequences to classify. Assumes they are translated using a vocabulary. (See gen_amino_acid_vocab in dataset.py)

Returns

out – Confidence of sequence(s) being in one of the n_classes.

Return type

Tensor, shape (batch_size, n_classes)

deepnog.tests package

deepnog.tests.utils module

deepnog.tests.utils.get_deepnog_root() → pathlib.Path

Module contents

Description:

Helpers for deepnog tests.

Including:

• test data

• test network weights (parameters)

• some helper functions

Individual tests are located within the respective deepnog subpackages.

deepnog.utils package

deepnog.utils.bio module

deepnog.utils.bio.parse(p: pathlib.Path, fformat: str = 'fasta', alphabet=None) → Iterator

Parse a possibly compressed sequence file.

Parameters

• p (Path or str) – Path to sequence file

• fformat (str) – File format supported by Biopython.SeqIO.parse, e.g “fasta”

• alphabet (any) – Pass alphabet to SeqIO.parse

Returns

it – The SeqIO.parse iterator yielding SeqRecords

Return type

Iterator

deepnog.utils.config module

deepnog.utils.config.get_config(config_file: Optional[Union[pathlib.Path, str]] = None) → Dict

Get a config dictionary

If no file is provided, look in the DEEPNOG_CONFIG env variable for the path. If this fails, load a default config file (lacking any user customization).

This contains the available models (databases, levels). Additional config may be added in future releases.

deepnog.utils.io_utils module

Author: Roman Feldbauer

Date: 2020-02-19

Description:

Input/output helper functions

deepnog.utils.io_utils.create_df(class_labels: list, preds: torch.Tensor, confs: torch.Tensor, ids: List[str], indices: List[int], threshold: Optional[float] = None)

Creates one dataframe storing all relevant prediction information.

The rows in the returned dataframe have the same order as the original sequences in the data file. First column of the dataframe represents the position of the sequence in the datafile.

Parameters

• class_labels (list) – Store class name corresponding to an output node of the network.

• preds (torch.Tensor, shape (n_samples,)) – Stores the index of the output-node with the highest activation

• confs (torch.Tensor, shape (n_samples,)) – Stores the confidence in the prediction

• ids (list[str]) – Stores the (possible empty) protein labels extracted from data file.

• indices (list[int]) – Stores the unique indices of sequences mapping to their position in the file

• threshold (float) – If given, prediction labels and confidences are set to ‘’ if confidence in prediction is not at least threshold.

Returns

df – Stores prediction information about the input protein sequences. Duplicates (defined by their sequence_id) have been removed from df.

Return type

pandas.DataFrame

deepnog.utils.io_utils.get_data_home(data_home: Optional[str] = None, verbose: int = 0) → pathlib.Path

Return the path of the deepnog data dir.

This folder is used for large files that cannot go into the Python package on PyPI etc. For example, the network parameters (weights) files may be larger than 100MiB. By default the data dir is set to a folder named ‘deepnog_data’ in the user home folder. Alternatively, it can be set by the ‘DEEPNOG_DATA’ environment variable or programmatically by giving an explicit folder path. If the folder does not already exist, it is automatically created.

Parameters

• data_home (str | None) – The path to deepnog data dir.

• verbose (int) – Log or not.

Notes

Adapted from SKLEARN_DATAHOME.

deepnog.utils.io_utils.get_weights_path(database: str, level: str, architecture: str, data_home: Optional[str] = None, download_if_missing: bool = True, verbose: int = 0) → pathlib.Path

Get path to neural network weights.

This is a path on local storage. If the corresponding files are not present, download from remote storage. The default remote URL can be overridden by setting the environment variable DEEPNOG_REMOTE.

Parameters

• database (str) – The orthologous groups database. Example: eggNOG5

• level (str) – The taxonomic level within the database. Example: 2 (for bacteria)

• architecture (str) – Network architecture. Example: deepnog

• data_home (str, optional) – Specify another download and cache folder for the weights. By default all deepnog data is stored in ‘$HOME/deepnog_data’ subfolders.

• download_if_missing (boolean, default=True) – If False, raise an IOError if the data is not locally available instead of trying to download the data from the source site.

• verbose (int) – Log or not

Returns

weights_path – Path to file of network weights

Return type

Path

deepnog.utils.logger module

deepnog.utils.logger.get_logger(initname: str = 'deepnog', verbose: int = 0) → logging.Logger

This function provides a nicely formatted logger.

Parameters

• initname (str) – The name of the logger to show up in log.

• verbose (int) – Increasing levels of verbosity

References

Shamelessly stolen from phenotrex

deepnog.utils.metrics module

deepnog.utils.metrics.estimate_performance(df_true: pandas.DataFrame, df_pred: pandas.DataFrame) → Dict

Calculate various model performance measures.

Parameters

• df_true (pandas.DataFrame) – The ground truth labels. DataFrame must contain ‘sequence_id’ and ‘label’ columns.

• df_pred (pandas.DataFrame) – The predicted labels. DataFrame must contain ‘sequence_id’ and ‘prediction’ columns.

Returns

perf –

Performance estimates:

• macro_precision

• micro_precision

• macro_recall

• micro_recall

• macro_f1

• micro_f1

• accuracy

• mcc

Return type

dict

deepnog.utils.network module

Author: Roman Feldbauer

Date: 2020-02-19

Description:

Various utility functions

deepnog.utils.network.count_parameters(model, tunable_only: bool = True) → int

Count the number of parameters in the given model.

Parameters

• model (torch.nn.Module) – PyTorch model (deep network)

• tunable_only (bool, optional) – Count only tunable network parameters

References

https://stackoverflow.com/questions/49201236/check-the-total-number-of-parameters-in-a-pytorch-model

deepnog.utils.network.load_nn(architecture: Union[str, Sequence[str]], model_dict: Optional[dict] = None, phase: str = 'eval', device: Union[torch.device, str] = 'cpu', verbose: int = 0)

Import NN architecture and set loaded parameters.

Parameters

• architecture (str or list-like of two str) – If single string: name of neural network module and class to import. E.g. ‘deepnog’ will load deepnog.models.deepnog.deepnog. Otherwise, separate module and class name of deep network to import. E.g. (‘deepthought’, ‘DeepNettigkeit’) will load deepnog.models.deepthought.DeepNettigkeit.

• model_dict (dict, optional) – Dictionary holding all parameters and hyper-parameters of the model. Required during inference, optional for training.

• phase (['train', 'infer', 'eval']) – Set network in training or inference=evaluation mode with effects on storing gradients, dropout, etc.

• device ([str, torch.device]) – Device to load the model into.

• verbose (int) – Increasingly verbose logging

Returns

model – Neural network object of type architecture with parameters loaded from model_dict and moved to device.

Return type

torch.nn.Module

deepnog.utils.network.set_device(device: Union[str, torch.device]) → torch.device

Set device (CPU/GPU) depending on user choice and availability.

Parameters

device ([str, torch.device]) – Device set by user as an argument to DeepNOG call.

Returns

device – Object containing the device type to be used for prediction calculations.

Return type

torch.device

deepnog.utils.sync module

Author: Roman Feldbauer

Date: 2020-02-19

Description:

Parallel processing helpers

class deepnog.utils.sync.SynchronizedCounter(init: int = 0)

Bases: object

A multiprocessing-safe counter.

Parameters

init (int, optional) – Counter starts at init (default: 0)

increment(n=1)

Obtain a lock before incrementing, since += isn’t atomic.

Parameters

n (int, optional) – Increment counter by n (default: 1)

increment_and_get_value(n=1) → int

Obtain a lock before incrementing, since += isn’t atomic.

Parameters

n (int, optional) – Increment counter by n (default: 1)

property value: int

Supported databases and taxonomic levels

The underpinnings of deepnog are database-agnostic. However, the tool requires specifically trained models for each database. If a database comprises multiple taxonomic levels, individual models are necessary. For example, eggNOG 5 features over 400 levels. The following list provides the currently available databases. (Alternatively, inspect deepnog_config.yml which will always contain up-to-date information.)

eggNOG 5

architecture	database	tax	macro_precision	macro_recall	macro_f1	accuracy	mcc
deepnog	eggNOG5	1	0.9743	0.9149	0.9369	0.9383	0.9383
deepnog	eggNOG5	2	0.9725	0.9374	0.9504	0.9463	0.9463
deepnog	eggNOG5	29	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	237	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	468	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	506	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	561	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	772	0.9375	0.9167	0.9143	0.9000	0.8768
deepnog	eggNOG5	815	0.7615	0.7685	0.7489	0.7660	0.7431
deepnog	eggNOG5	976	0.9876	0.9829	0.9829	0.9844	0.9844
deepnog	eggNOG5	1117	0.9915	0.9890	0.9887	0.9900	0.9900
deepnog	eggNOG5	1150	0.9630	0.9630	0.9556	0.9600	0.9557
deepnog	eggNOG5	1224	0.9767	0.9683	0.9690	0.9797	0.9797
deepnog	eggNOG5	1236	0.9865	0.9821	0.9821	0.9878	0.9878
deepnog	eggNOG5	1239	0.9724	0.9649	0.9651	0.9783	0.9783
deepnog	eggNOG5	1268	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	1297	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	1386	0.9748	0.9758	0.9716	0.9778	0.9776
deepnog	eggNOG5	1653	0.9015	0.8485	0.8325	0.8519	0.8443
deepnog	eggNOG5	1762	0.9942	0.9767	0.9812	0.9846	0.9844
deepnog	eggNOG5	2157	0.9755	0.9672	0.9669	0.9670	0.9669
deepnog	eggNOG5	2759	0.9776	0.9354	0.9506	0.9410	0.9410
deepnog	eggNOG5	3699	0.9809	0.9564	0.9614	0.9663	0.9657
deepnog	eggNOG5	4447	0.9648	0.9498	0.9490	0.9517	0.9513
deepnog	eggNOG5	4751	0.9852	0.9807	0.9800	0.9814	0.9813
deepnog	eggNOG5	4776	0.9000	0.7500	0.7778	0.8333	0.6325
deepnog	eggNOG5	4890	0.9869	0.9824	0.9817	0.9820	0.9820
deepnog	eggNOG5	4891	0.9692	0.9551	0.9544	0.9444	0.9419
deepnog	eggNOG5	5204	0.8615	0.8404	0.8327	0.8649	0.8605
deepnog	eggNOG5	5338	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	5653	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	5794	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	5878	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	6231	0.8815	0.8315	0.8469	0.8545	0.8460
deepnog	eggNOG5	6236	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	6656	0.9557	0.9278	0.9313	0.9380	0.9376
deepnog	eggNOG5	7147	0.9808	0.9615	0.9634	0.9722	0.9704
deepnog	eggNOG5	7214	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	7711	0.9840	0.9763	0.9759	0.9763	0.9763
deepnog	eggNOG5	7742	0.9869	0.9809	0.9804	0.9802	0.9802
deepnog	eggNOG5	7898	0.9881	0.9762	0.9773	0.9911	0.9895
deepnog	eggNOG5	8459	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	9263	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	9443	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	9989	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	28211	0.9849	0.9805	0.9800	0.9872	0.9872
deepnog	eggNOG5	28216	0.9944	0.9932	0.9929	0.9933	0.9933
deepnog	eggNOG5	28221	0.9803	0.9733	0.9728	0.9710	0.9709
deepnog	eggNOG5	28890	0.9898	0.9853	0.9848	0.9858	0.9857
deepnog	eggNOG5	29547	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	31979	0.9833	0.9736	0.9745	0.9729	0.9728
deepnog	eggNOG5	32066	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	33090	0.9779	0.9679	0.9683	0.9698	0.9698
deepnog	eggNOG5	33154	0.9788	0.9592	0.9639	0.9637	0.9637
deepnog	eggNOG5	33208	0.9783	0.9625	0.9656	0.9634	0.9634
deepnog	eggNOG5	33213	0.9775	0.9661	0.9670	0.9660	0.9660
deepnog	eggNOG5	33342	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	33554	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	33958	0.9865	0.9813	0.9803	0.9804	0.9802
deepnog	eggNOG5	35493	0.9835	0.9749	0.9753	0.9750	0.9750
deepnog	eggNOG5	38820	0.9712	0.9455	0.9474	0.9514	0.9508
deepnog	eggNOG5	40674	0.9889	0.9828	0.9832	0.9832	0.9832
deepnog	eggNOG5	41294	0.9667	0.9479	0.9472	0.9535	0.9512
deepnog	eggNOG5	50557	0.9354	0.9184	0.9171	0.9107	0.9097
deepnog	eggNOG5	68525	0.9875	0.9839	0.9836	0.9839	0.9839
deepnog	eggNOG5	69277	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	71274	0.9583	0.9167	0.9206	0.9655	0.9581
deepnog	eggNOG5	72273	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	72275	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	80864	0.9500	0.8750	0.8889	0.9231	0.8977
deepnog	eggNOG5	82115	0.9774	0.9519	0.9536	0.9603	0.9597
deepnog	eggNOG5	85004	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	85010	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	85013	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	85023	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	85025	0.9710	0.9829	0.9731	0.9697	0.9686
deepnog	eggNOG5	85026	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	91061	0.9862	0.9837	0.9827	0.9886	0.9886
deepnog	eggNOG5	91561	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	91835	0.9617	0.9562	0.9534	0.9549	0.9543
deepnog	eggNOG5	112252	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	117743	0.9930	0.9912	0.9906	0.9905	0.9905
deepnog	eggNOG5	117747	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	119060	0.9827	0.9634	0.9677	0.9652	0.9645
deepnog	eggNOG5	119089	0.9706	0.9412	0.9437	0.9600	0.9579
deepnog	eggNOG5	135613	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	135614	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	135619	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	135623	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	147545	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	147550	0.8464	0.8452	0.8318	0.8611	0.8537
deepnog	eggNOG5	155619	0.9500	0.9000	0.9042	0.9310	0.9242
deepnog	eggNOG5	183963	0.9685	0.9435	0.9455	0.9468	0.9457
deepnog	eggNOG5	186801	0.9870	0.9827	0.9826	0.9852	0.9851
deepnog	eggNOG5	186822	0.9895	0.9825	0.9832	0.9805	0.9802
deepnog	eggNOG5	186928	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	200643	0.9895	0.9855	0.9853	0.9843	0.9843
deepnog	eggNOG5	201174	0.9840	0.9795	0.9796	0.9836	0.9836
deepnog	eggNOG5	203691	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	204441	0.9583	0.9167	0.9206	0.9565	0.9439
deepnog	eggNOG5	204457	0.9497	0.9406	0.9351	0.9425	0.9409
deepnog	eggNOG5	213115	0.9524	0.8889	0.8889	0.9091	0.8976
deepnog	eggNOG5	267890	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	314146	0.9810	0.9613	0.9634	0.9630	0.9626
deepnog	eggNOG5	355688	1.0000	1.0000	1.0000	1.0000	0.0000
deepnog	eggNOG5	541000	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	544448	0.9333	0.9333	0.9200	0.9231	0.9104
deepnog	eggNOG5	768503	1.0000	1.0000	1.0000	1.0000	1.0000
deepnog	eggNOG5	909932	1.0000	1.0000	1.0000	1.0000	1.0000

COG 2020

architecture	database	tax	macro_precision	macro_recall	macro_f1	accuracy	mcc
deepnog	cog2020	1	0.9334	0.9043	0.9125	0.9391	0.9390

Performance estimates are based on 96%/2%/2% stratified train/validation/test splits of all sequences within the corresponding database and tax. level. This likely overestimates generalization performance for distant homologs. See the research paper for details.

Deepnog New Models and Architectures

deepnog is developed with extensibility in mind, and allows to plug in additional models (for different taxonomic levels, or different orthology databases). It also supports addition of new network architectures.

In order to register a new network architecture, we recommend an editable installation with pip, as described in Installation from Source.

Training scripts

Starting with v1.2.0, deepnog ships with functions for training custom models. Consider we are training a DeepNOG model for eggNOG 5, level 1239 (Firmicutes):

deepnog train \
    -a "deepnog" \
    -o /path/to/output/ \
    -db "eggNOG5" \
    -t "1239" \
    --shuffle \
    train.faa.gz \
    val.faa.gz \
    train.csv.gz \
    val.csv.gz

Run deepnog train --help for additional options.

In order to assess the new model’s quality, run the following commands:

deepnog infer \
    -a "deepnog" \
    -w /path/to/output/MODEL_FILENAME.pth \
    -o /path/to/output/assignments.csv \
    --test_labels test.csv.gz \
    test.faa.gz
cat /path/to/output/assignments.performance.csv

This provides a number of performance measures, including accurcay, macro averaged precision and recall, among others.

Register new models

New models for additional taxonomic levels in eggNOG 5 or even different orthology databases using existing network architectures must be placed in the deepnog data directory as specified by the DEEPNOG_DATA environment variable (default: $HOME/deepnog_data).

The directory looks like this:

| deepnog_data
| ├── eggNOG5
| │   ├── 1
| │   |   └── deepnog.pth
| │   └── 2
| │       └── deepnog.pth
| ├── ...
|
|

In order to add a root level model for “MyOrthologyDB”, we place the serialized PyTorch parameters like this:

| deepnog_data
| ├── eggNOG5
| │   ├── 1
| │   |   └── deepnog.pth
| │   └── 2
| │       └── deepnog.pth
| ├── MyOrthologyDB
| |   └── 1
| |       └── deepnog.pth
| ├── ...
|

Register new network architectures

Create a Python module deepnog/models/<my_network.py>. You can use deepnog.py as a template. A new architecture MyNetworkA would look like so:

import torch.nn as nn


class MyNetworkA(nn.Module):
    """ A revolutionary network for orthology prediction. """
    def __init__(self, model_dict):
        super().__init__()
        param1 = model_dict['param1']
        param2 = model_dict['param2']
        param3 = model_dict.get('param3', 0.)
        ...
    def forward(self, x):
        ...
        return x

When the new module is in place, also edit deepnog/config/deepnog_config.py to expose the new network to the user:

architecture:
  netA:
    module: my_network
    class: MyNetworkA
    param1: 'settingXYZ'
    param2:
      - 2
      - 4
      - 8
    param3: 150
    # ... all hyperparameters required for class init

  deepnog:
    module: deepnog
    class: DeepNOG
    encoding_dim: 10
    kernel_size:
      - 8
      - 12
      - 16
      - 20
      - 24
      - 28
      - 32
      - 36
    n_filters: 150
    dropout: 0.3
    pooling_layer_type: 'max'

The new network can now be used in deepnog by specifying parameter -a netA.

Assuming we want to compare deepnog to netA, we add the trained network parameters like this:

| deepnog_data
| ├── eggNOG5
| │   ├── 1
| │   |   ├── deepnog.pth
| │   |   └── netA.pth
| │   └── 2
| │       ├── deepnog.pth
| │       └── netA.pth
| ├── MyOrthologyDB
| |   └── 1
| │       ├── deepnog.pth
| │       └── netA.pth
| ├── ...
|

Finally, expose the new models to the user by modifying deepnog/config/deepnog_config.py again. The relevant section is database.

database:
  eggNOG5:
    # taxonomic levels
    - 1
    - 2
    - 1236
    - 1239        # Example 1: Uncomment this line, if you created a Firmicutes model
  MyOrthologyDB:  # Example 2: Uncomment this line and the following, if you
    - 1           #            created a model for the '1' level of MyOrthologyDB.

Notes:

• Currently, a level must be provided, even if the database does not use levels. Simply use a placeholder 1 or similar.

• Indentation matters

File formats

deepnog uses standard file formats, as detailed below for eggNOG 5 (1239, Firmicutes) data.

Protein sequences

Protein sequences are expected in FASTA format. Each entry must contain a unique record ID. That is, a user_data.faa should look like this:

>1000569.HMPREF1040_0002
MMKHDDHVHQIRTEPIYAILGETFSRGRTNRQVAKALLGAGVRIIQYREKEKSWQEKYEE
ARDICQWCNEYGATFIMNDSIDLAIACEAPAIHVGQDDAPVAWVRRLAQRDIVVGVSTHT
IAEMKKAVRDGADYVGLGPMYQTTSKMDVHDIVADVDKAYALTLPIPVVTIGGIDLIHIR
QLYTEGFRSFAMISALVGATDIVEQIGAFRQVLQEKIDEC
>1000569.HMPREF1040_0003
MATTVGDIVTYLQGIAPLYLKEEWDNPGLLLGNQGDPVSSVLVTLDVMEGTVDYAIAEGI
SFIFSHHPLIMKGIKAIRTDSYDGRMYQKLLSHHIAVYAAHTNLDSATGGVNDVLAEHLQ
LQHVRPFIPGVSESLYKIAIYVPKGYGDAIREVLGKHDAGHLGAYSYCSFSVAGQGRFKP
LAGTHPFIGKRDVLETVEEERIETIVEGSRLGEVITAMLAVHPYEEPAYDIYPLYQQRTA
LGLGRLGELATPLSSMAAVQWVKEALHLTHVSYAGPMDRQIQTIAVLGGSGAEFIATAKA
AGATLYVTGDMKYHAAQEAIKQGILVVDAGHFGTEFPVIDRMKQNIEAENEKQGWHIQCV
VDPTAMDMIQRL

Compression is allowed (user_data.faa.gz, or user_data.faa.xz). For typical usage of deepnog infer for protein orthologous group assignments this is already sufficient.

Protein orthologous group labels

Training new models with deepnog train, or assessing model quality with deepnog infer --test_labels require providing the orthologous group labels.

File format is CSV (comma-separated values) with a preceding header line, and three columns (index, sequence record ID, orthologous group ID).

,string_id,eggnog_id
1543720,1121929.KB898683_gene1916,1V3NB
351865,536232.CLM_3459,1TPCN
[...]
1570381,1000569.HMPREF1040_0002,1V3ZR
744166,1000569.HMPREF1040_0003,1TQ27
[...]
426023,1423743.JCM14108_56,1TPGE

To construct some user_data.csv:

• Copy (do not modify) the header line.

• Provide an index in the first column (e.g. 1..N; currently unused, but required).

• Provide the sequence ID (e.g. eggNOG/STRING ID) in column 2.

• Provide its corresponding group label in column 3.

• Sequence IDs in column 2 must match the IDs used in the user_data.faa.

Assignment output

Orthologous group assignments are output in tabular format (comma-separated).

• Column 1: Sequence ID

• Column 2: Assignment/Orthologous group

• Column 3: Assignment confidence in 0..1 (higher=better).

Example:

sequence_id,prediction,confidence
1000565.METUNv1_00038,COG0466,1.0
1000565.METUNv1_00060,COG0500,0.20852506
1000565.METUNv1_00091,COG0810,0.9999591
1000565.METUNv1_00093,COG0659,1.0
1000565.METUNv1_00103,COG5000,0.70716757
1000565.METUNv1_00105,COG0346,0.9999982
1000565.METUNv1_00106,COG3791,1.0
1000565.METUNv1_00114,COG0239,1.0
1000565.METUNv1_00115,COG1643,1.0

Contributing

deepnog is free open source software. Contributions from the community are highly appreciated. Even small contributions improve the software’s quality.

Even if you are not familiar with programming languages and tools, you may contribute by filing bugs or any problems as a GitHub issue.

Git and branching model

We use git for version control (CVS), as do most projects nowadays. If you are not familiar with git, there are lots of tutorials on GitHub Guide. All the important basics are covered in the GitHub Git handbook.

Development of deepnog (mostly) follows this git branching model. We currently use one main branch: master. For any changes, a new branch should be created. This includes new feature, noncritical or critical bug fixes, etc.

Workflow

In case of large changes to the software, please first get in contact with the authors for coordination, for example by filing an issue. If you want to fix small issues (typos in the docs, obvious errors, etc.) you can - of course - directly submit a pull request (PR).

1. Create a fork of deepnog in your GitHub account.

   Simply click “Fork” button on https://github.com/univieCUBE/deepnog.

2. Clone your fork on your computer.

   $ git clone git@github.com:YOUR-ACCOUNT-GOES-HERE/deepnog.git && cd deepnog

3. Add remote upstream.

   $ git remote add upstream git@github.com:univieCUBE/deepnog.git

4. Create feature/bugfix branch.

   $ git checkout -b bugfix123 master

5. Implement feature/fix bug/fix typo/…

   Happy coding!

6. Create a commit with meaningful message

   If you only modified existing files, simply $ git commit -am "descriptive message what this commit does (in present tense) here"

7. Push to GitHub

   e.g. $ git push origin featureXYZ

8. Create pull request (PR)

   Git will likely provide a link to directly create the PR. If not, click “New pull request” on your fork on GitHub.

9. Wait…

   Several devops checks will be performed automatically (e.g. continuous integration (CI) with Github Actions, AppVeyor).

   The authors will get in contact with you, and may ask for changes.

10. Respond to code review.

    If there were issues with continuous integration, or the authors asked for changes, please create a new commit locally, and simply push again to GitHub as you did before. The PR will be updated automatically.

11. Maintainers merge PR, when all issues are resolved.

    Thanks a lot for your contribution!

Code style and further guidelines

• Please make sure all code complies with PEP 8

• All code should be documented sufficiently (functions, classes, etc. must have docstrings with general description, parameters, ideally return values, raised exceptions, notes, etc.)

• Documentation style is NumPy format.

• New code must be covered by unit tests using pytest.

• If you fix a bug, please provide regression tests (fail on old code, succeed on new code).

• It may be helpful to install deepnog in editable mode for development. When you have already cloned the package, switch into the corresponding directory, and

  pip install -e .
  

  (don’t omit the trailing period). This way, any changes to the code are reflected immediately. That is, you don’t need to install the package each and every time, when you make changes while developing code.

Testing

In deepnog, we aim for high code coverage. As of Feb 2020, more than 95% of all code lines are visited at least once when running the complete test suite. This is primarily to ensure:

• correctness of the code (to some extent) and

• maintainability (new changes don’t break old code).

Creating a new PR, ideally all code would be covered by tests. Sometimes, this is not feasible or only with large effort. Pull requests will likely be accepted, if the overall code coverage at least does not decrease.

Unit tests are automatically performed for each PR using CI tools online. This may take some time, however. To run the tests locally, you need pytest installed. From the deepnog directory, call

pytest deepnog/

to run all the tests. You can also restrict the tests to the subpackage you are working on, down to single tests. For example

pytest deepnog/tests/test_dataset.py --showlocals -v

only runs tests about datasets.

In order to check code coverage locally, you need the pytest-cov plugin.

pytest deepnog --cov=deepnog

Changelog

Next release

…

Fixes in 1.2.4

• Bioconda automatically installs PyTorch (see deepnog/#52 and bioconda/#27112)

1.2.3 - 2021-02-09

Added in 1.2.3

• Add citation for paper published in Bioinformatics (doi)

Changes in 1.2.3

• CI with Github Actions (Linux, macOS)

Fixes in 1.2.3

• Fixes a bug where custom trained models would not use dropout correctly see #44

• Fixes data usage in training with an iterable dataset without shuffling see #43

• Fixes several non-critical warnings see #50

• Several small fixes regarding updated libraries etc.

1.2.2 - 2020-12-10

Added in 1.2.2

• Install from bioconda

• Support for 109 taxonomic levels in eggNOG 5 (was three before) (e.g. deepnog infer -db eggnog5 -t 1239 for Firmicutes)

• Support for COG2020 (use deepnog infer -db cog2020 -t 1)

Fixes/changes in 1.2.2

• Requirement PyYAML

• Test class imports

• Exit on requesting unavailable device (instead of raising an error)

1.2.1 - 2020-08-28

Added in 1.2.1

• Training custom models: Users can now train additional models for further tax. levels of eggNOG 5 or even different orthology databases

• TensorBoard status reports: Follow training/validation loss online

• Support for configuration file (deepnog_config.yml)

• Model quality assessment

Changed in 1.2.1

• The command line invocation now uses two subcommands:

  • deepnog train for training new models, and

  • deepnog infer for general orthologous group assignment (and model quality assessment)

Fixed in 1.2.1

• Fixed packaging issue in 1.2.0 (which was subsequently removed altogether)

• Several additional bug fixes and smaller changes

1.1.0 - 2020-02-28

Added

• EggNOG5 root (tax 1) prediction

Changed

• Package structure changed for higher modularity. This will require changes in downstream usages.

• Remove network weights from the repository, because files are too large for github and/or PyPI. deepnog automatically downloads these from CUBE servers, and caches them locally.

• More robust inter-process communication in data loading

Fixes

• Fix error on very short amino acid sequences

• Fix error on unrecognized symbols in sequences (stop codons etc.)

• Fix multiprocess data loading from gzipped files

• Fix type mismatch in deepencoding embedding layer (Windows only)

Maintenance

• Continuous integration on

  • Travis (Linux, MacOS)

  • AppVeyor (Windows)

• Codecov coverage reports

• LGTM code quality/security reports

• Documentation on ReadTheDocs

• Upload to PyPI, thus enabling $ pip install deepnog.

1.0.0 - 2019-10-18

The first release of deepnog to appear in this changelog. It already contains the following features:

• EggNOG5 bacteria (tax 2) prediction

• DeepEncoding architecture

• CPU and GPU support

• Runs on all major platforms (Linux, MacOS, Windows)

Getting started

Get started with deepnog in a breeze. Find how to install the package and see all core functionality applied in a single quick start example.

User Guide

The User Guide introduces the main concepts of deepnog. It also contains complete CLI and API documentations of the package.

Development

There are several possibilities to contribute to this free open source software. We highly appreciate all input from the community, be it bug reports or code contributions.

Source code, issue tracking, discussion, and continuous integration appear on our GitHub page.

What’s new

To see what’s new in the latest version of deepnog, have a look at the changelog.

Indices and tables

• Index

• Module Index

• Search Page