Add usage instructions

.gitignore

@@ -1,2 +1 @@
 *.tfrecords
-*tfevents*

README.md

@@ -2,15 +2,10 @@
 
 locimend is a tool that corrects DNA sequencing errors using Deep Learning.
 
-The goal is to provide a correct DNA sequence, when a sequence containing errors is provided.
-
-It provides both a command-line program and a REST API.
-
 ## Technologies
 
 - Tensorflow
 - Biopython
-- FastAPI
 
 ## Installation
 
@@ -42,70 +37,13 @@ cd locimend
 nix-shell
 ```
 
-5. Install the dependencies via poetry:
-
-```bash
-poetry install
-```
-
 After running these commands, you will find yourself in a shell that
 contains all the needed dependencies.
 
 ## Usage
 
-### Training the model
+The following creates the dataset, trains the Deep Learning model and shows the accuracy:
-
-The following command creates the trains the Deep Learning model and shows the accuracy and AUC:
 
 ```bash
-poetry run python locimend/main.py train <data file> <label file>
+poetry run python src/model.py
 ```
-
-- <data file>: FASTQ file containing the sequences with errors
-- <label file>: FASTQ file containing the sequences without errors
-
-Both files must contain the canonical and read simulated sequences in the same positions (same row).
-
-A dataset is provided to train the model, in order to proceed execute the following command:
-
-```bash
-poetry run python locimend/main.py train data/curesim-HVR.fastq data/HVR.fastq
-```
-
-
-### Inference
-
-A trained model is provided, which can be used to infer the correct sequences. There are two ways to interact with it:
-
-- Command-line execution
-- REST API
-
-#### Command-line
-
-The following command will infer the correct sequence, and print it:
-
-```bash
-poetry run python locimend/main.py infer "<DNA sequence>"
-```
-
-#### REST API
-
-It is also possible to serve the model via a REST API, to start the web server run the following command:
-
-```bash
-poetry run api
-```
-
-The API can be accessed at http://localhost:8000, with either a GET or POST request:
-
-| Request | Endpoint | Payload |
-|:----:|:-----:|:-----:|
-| GET     | / | Sequence as a path parameter (in the URL) |
-| POST     | /| JSON |
-
-For a POST request the JSON must have the following structure:
-
-```json
-{"sequence": "<DNA sequence>"}
-```
-

default.nix

@@ -1,5 +0,0 @@
-{ sources ? import ./nix/sources.nix, pkgs ? import sources.nixpkgs { } }:
-
-with pkgs;
-
-poetry2nix.mkPoetryApplication { projectDir = ./.; }

docker.nix

@@ -1,14 +0,0 @@
-{ sources ? import ./nix/sources.nix, pkgs ? import sources.nixpkgs { } }:
-
-with pkgs;
-
-let locimend = callPackage ./default.nix { };
-
-in {
-  docker = dockerTools.streamLayeredImage {
-    name = "locimend";
-    contents = [ locimend ];
-    config.Cmd = [ "api" ];
-  };
-
-}

locimend/api.py

@@ -1,27 +0,0 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
-from uvicorn import run
-
-from locimend.model import infer_sequence
-
-app = FastAPI()
-
-
-class Input(BaseModel):
-    sequence: str
-
-
-@app.get("/{sequence}")
-async def get_sequence_path(sequence: str):
-    correct_sequence = await infer_sequence(sequence)
-    return {"sequence": correct_sequence}
-
-
-@app.post("/")
-async def get_sequence_body(sequence: Input):
-    correct_sequence = await infer_sequence(sequence.sequence)
-    return {"sequence": correct_sequence}
-
-
-def main():
-    run(app, host="0.0.0.0")

locimend/hyperparameters.py

@@ -1,24 +0,0 @@
-class Hyperparameters:
-    def __init__(
-        self,
-        data_file,
-        label_file,
-        train_dataset="data/train_data.tfrecords",
-        test_dataset="data/test_data.tfrecords",
-        eval_dataset="data/eval_data.tfrecords",
-        epochs=100,
-        batch_size=64,
-        learning_rate=0.004,
-        l2_rate=0.001,
-        max_length=80,
-    ):
-        self.data_file = data_file
-        self.label_file = label_file
-        self.train_dataset = train_dataset
-        self.eval_dataset = eval_dataset
-        self.test_dataset = test_dataset
-        self.epochs = epochs
-        self.batch_size = batch_size
-        self.learning_rate = learning_rate
-        self.l2_rate = l2_rate
-        self.max_length = max_length

locimend/main.py

@@ -1,40 +0,0 @@
-from asyncio import run
-from argparse import ArgumentParser, Namespace
-from time import time
-
-from locimend.model import infer_sequence, train_model
-
-
-def parse_arguments() -> Namespace:
-    parser = ArgumentParser()
-    subparsers = parser.add_subparsers(dest="task")
-    parser_train = subparsers.add_parser("train")
-    parser_infer = subparsers.add_parser("infer")
-    parser_train.add_argument(
-        "data_file", help="FASTQ file containing the sequences with errors"
-    )
-    parser_train.add_argument(
-        "label_file", help="FASTQ file containing the sequences without errors"
-    )
-    parser_infer.add_argument("sequence", help="DNA sequence with errors")
-    return parser.parse_args()
-
-
-async def execute_task(args):
-    if args.task == "train":
-        start_time = time()
-        train_model(data_file=args.data_file, label_file=args.label_file)
-        end_time = time()
-        print(f"Training time: {end_time - start_time}")
-    else:
-        prediction = await infer_sequence(sequence=args.sequence)
-        print(f"Error-corrected sequence: {prediction}")
-
-
-def main() -> None:
-    args = parse_arguments()
-    run(execute_task(args))
-
-
-if __name__ == "__main__":
-    main()

locimend/model.py

@@ -1,89 +0,0 @@
-from random import seed
-
-from numpy import argmax
-from tensorflow import one_hot
-from tensorflow.keras import Model, Sequential
-from tensorflow.keras.layers import Dense, Dropout, Input, Masking
-from tensorflow.keras.losses import categorical_crossentropy
-from tensorflow.keras.models import load_model
-from tensorflow.keras.optimizers import Adam
-from tensorflow.keras.regularizers import l2
-from tensorflow.random import set_seed
-
-from locimend.hyperparameters import Hyperparameters
-from locimend.preprocessing import (
-    BASES,
-    dataset_creation,
-    decode_sequence,
-    encode_sequence,
-)
-
-
-def build_model(hyperparams) -> Model:
-    """
-    Build the CNN model
-    """
-    model = Sequential(
-        [
-            Input(shape=(hyperparams.max_length, len(BASES))),
-            Masking(mask_value=-1),
-            Dense(
-                units=256, activation="relu", kernel_regularizer=l2(hyperparams.l2_rate)
-            ),
-            Dropout(rate=0.3),
-            Dense(
-                units=128, activation="relu", kernel_regularizer=l2(hyperparams.l2_rate)
-            ),
-            Dropout(rate=0.3),
-            Dense(
-                units=64, activation="relu", kernel_regularizer=l2(hyperparams.l2_rate)
-            ),
-            Dropout(rate=0.3),
-            Dense(units=len(BASES), activation="softmax"),
-        ]
-    )
-    model.compile(
-        optimizer=Adam(hyperparams.learning_rate),
-        loss=categorical_crossentropy,
-        metrics=["accuracy", "AUC"],
-    )
-    return model
-
-
-def show_metrics(model, eval_dataset, test_dataset) -> None:
-    """
-    Show the model metrics
-    """
-    eval_metrics = model.evaluate(eval_dataset, verbose=0)
-    test_metrics = model.evaluate(test_dataset, verbose=0)
-    print(f"Eval metrics {eval_metrics}")
-    print(f"Test metrics {test_metrics}")
-
-
-def train_model(data_file, label_file, seed_value=42) -> None:
-    """
-    Create a dataset, a model and runs training and evaluation on it
-    """
-    seed(seed_value)
-    set_seed(seed_value)
-    hyperparams = Hyperparameters(data_file=data_file, label_file=label_file)
-    train_data, eval_data, test_data = dataset_creation(hyperparams)
-    model = build_model(hyperparams)
-    print("Training the model")
-    model.fit(train_data, epochs=hyperparams.epochs, validation_data=eval_data)
-    print("Training complete. Obtaining the model's metrics...")
-    show_metrics(model, eval_data, test_data)
-    model.save("trained_model")
-
-
-async def infer_sequence(sequence) -> str:
-    """
-    Predict the correct sequence, using the trained model
-    """
-    model = load_model("trained_model")
-    encoded_sequence = encode_sequence(sequence)
-    one_hot_encoded_sequence = one_hot(encoded_sequence, depth=len(BASES))
-    prediction = model.predict(one_hot_encoded_sequence)
-    encoded_prediction = argmax(prediction, axis=1)
-    final_prediction = decode_sequence(encoded_prediction)
-    return final_prediction

locimend/preprocessing.py

@@ -1,148 +0,0 @@
-from typing import Dict, List, Tuple
-
-from Bio.pairwise2 import align
-from Bio.SeqIO import parse
-from numpy.random import random
-from tensorflow import Tensor, int64, one_hot
-from tensorflow.data import AUTOTUNE, TFRecordDataset
-from tensorflow.io import TFRecordWriter, VarLenFeature, parse_single_example
-from tensorflow.sparse import to_dense
-from tensorflow.train import Example, Feature, Features, Int64List
-
-BASES = "ACGT-"
-
-
-def align_sequences(sequence, label) -> Tuple[str, str]:
-    """
-    Align the altered sequence with the reference sequence to obtain a same length output
-    """
-    alignments = align.globalxx(label, sequence)
-    best_alignment = alignments[0]
-    aligned_seq, aligned_label, _, _, _ = best_alignment
-    return aligned_seq, aligned_label
-
-
-def encode_sequence(sequence) -> List[int]:
-    """
-    Encode the DNA sequence using the indices of the BASES constant
-    """
-    encoded_sequence = [BASES.index(element) for element in sequence]
-    return encoded_sequence
-
-
-def decode_sequence(sequence) -> str:
-    """
-    Decode an index encoded sequence back to the human readable format
-    """
-    decoded_list = [BASES[element] for element in sequence]
-    sequence = "".join(decoded_list)
-    return sequence
-
-
-def prepare_sequences(sequence, label):
-    """
-    Align and encode the sequences to obtain a fixed length output in order to perform batching
-    """
-    encoded_sequences = []
-    aligned_seq, aligned_label = align_sequences(sequence, label)
-    for item in [aligned_seq, aligned_label]:
-        encoded_sequences.append(encode_sequence(item))
-    return encoded_sequences[0], encoded_sequences[1]
-
-
-def generate_example(sequence, label) -> bytes:
-    """
-    Create a binary-string for each sequence containing the sequence and the bases' counts
-    """
-    processed_seq, processed_label = prepare_sequences(sequence, label)
-    schema = {
-        "sequence": Feature(int64_list=Int64List(value=processed_seq)),
-        "label": Feature(int64_list=Int64List(value=processed_label)),
-    }
-    example = Example(features=Features(feature=schema))
-    return example.SerializeToString()
-
-
-def read_fastq(hyperparams) -> List[bytes]:
-    """
-    Parses a data and a label FASTQ files and generates a List of serialized Examples
-    """
-    examples = []
-    with open(hyperparams.data_file) as data, open(hyperparams.label_file) as labels:
-        for element, label in zip(parse(data, "fastq"), parse(labels, "fastq")):
-            example = generate_example(sequence=str(element.seq), label=str(label.seq))
-            examples.append(example)
-    return examples
-
-
-def create_dataset(hyperparams, dataset_split=[0.8, 0.1, 0.1]) -> None:
-    """
-    Create a training, evaluation and test dataset with a 80/10/10 split respectively
-    """
-    data = read_fastq(hyperparams)
-    with TFRecordWriter(hyperparams.train_dataset) as training, TFRecordWriter(
-        hyperparams.test_dataset
-    ) as test, TFRecordWriter(hyperparams.eval_dataset) as evaluation:
-        for element in data:
-            if random() < dataset_split[0]:
-                training.write(element)
-            elif random() < dataset_split[0] + dataset_split[1]:
-                evaluation.write(element)
-            else:
-                test.write(element)
-
-
-def transform_features(parsed_features) -> Dict[str, Tensor]:
-    """
-    Transform the parsed features of an Example into a list of dense one hot encoded Tensors
-    """
-    features = {}
-    sparse_features = ["sequence", "label"]
-    for element in sparse_features:
-        features[element] = to_dense(parsed_features[element])
-        features[element] = one_hot(features[element], depth=len(BASES))
-    return features
-
-
-def process_input(byte_string) -> Tuple[Tensor, Tensor]:
-    """
-    Parse a byte-string into an Example object
-    """
-    schema = {
-        "sequence": VarLenFeature(dtype=int64),
-        "label": VarLenFeature(dtype=int64),
-    }
-    parsed_features = parse_single_example(byte_string, features=schema)
-    features = transform_features(parsed_features)
-    return features["sequence"], features["label"]
-
-
-def read_dataset(filepath, hyperparams) -> TFRecordDataset:
-    """
-    Read TFRecords files and generate a dataset
-    """
-    data_input = TFRecordDataset(filenames=filepath)
-    dataset = data_input.map(map_func=process_input, num_parallel_calls=AUTOTUNE)
-    shuffled_dataset = dataset.shuffle(buffer_size=10000, seed=42)
-    batched_dataset = shuffled_dataset.padded_batch(
-        batch_size=hyperparams.batch_size,
-        padded_shapes=(
-            [hyperparams.max_length, len(BASES)],
-            [hyperparams.max_length, len(BASES)],
-        ),
-        padding_values=-1.0,
-    )
-    return batched_dataset
-
-
-def dataset_creation(
-    hyperparams,
-) -> Tuple[TFRecordDataset, TFRecordDataset, TFRecordDataset]:
-    """
-    Generate the TFRecord files and split them into training, validation and test data
-    """
-    create_dataset(hyperparams)
-    train_data = read_dataset(hyperparams.train_dataset, hyperparams)
-    eval_data = read_dataset(hyperparams.eval_dataset, hyperparams)
-    test_data = read_dataset(hyperparams.test_dataset, hyperparams)
-    return train_data, eval_data, test_data

nix/sources.json

@@ -17,10 +17,10 @@
         "homepage": "",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "f930ea227cecaed1f1bdb047fef54fe4f0721c8c",
+        "rev": "0d337eb6b77c8911cd02ed92e63fcc2a8949b404",
-        "sha256": "04khbc44lppzz0m2g56zr7vafv4fvnb7rfbz7c03dqw6k99svj1c",
+        "sha256": "1xm6ss7j3zscpiczz3kxjad3jd1qvy5zpm35kqri6p9mp4jzna1x",
         "type": "tarball",
-        "url": "https://github.com/NixOS/nixpkgs/archive/f930ea227cecaed1f1bdb047fef54fe4f0721c8c.tar.gz",
+        "url": "https://github.com/NixOS/nixpkgs/archive/0d337eb6b77c8911cd02ed92e63fcc2a8949b404.tar.gz",
         "url_template": "https://github.com/<owner>/<repo>/archive/<rev>.tar.gz"
     }
 }

poetry.lock

@@ -1,913 +0,0 @@
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-
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-
-[package.dependencies]
-six = ">=1.6.1,<2.0"
-
-[[package]]
-name = "biopython"
-version = "1.78"
-description = "Freely available tools for computational molecular biology."
-category = "main"
-optional = false
-python-versions = ">=3.6"
-
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-description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
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pyproject.toml

@@ -9,16 +9,11 @@ license = "GPL-3.0-or-later"
 python = "3.8.*"
 tensorflow = "^2.4.1"
 biopython = "^1.78"
-fastapi = "^0.66.0"
-uvicorn = "^0.14.0"
 
 [tool.poetry.dev-dependencies]
 isort = "^5.8.0"
 pyflakes = "^2.3.1"
 
-[tool.poetry.scripts]
-api = "locimend.api:main"
-
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"

src/constants.py

@@ -0,0 +1,9 @@
+BASES = "ACGT"
+TRAIN_DATASET = "data/train_data.tfrecords"
+TEST_DATASET = "data/test_data.tfrecords"
+EVAL_DATASET = "data/eval_data.tfrecords"
+EPOCHS = 1000
+BATCH_SIZE = 256
+LEARNING_RATE = 0.004
+L2 = 0.001
+LOG_DIR = "logs"

src/model.py

@@ -0,0 +1,91 @@
+from random import seed
+
+from tensorflow.keras import Model, Sequential, layers
+from tensorflow.keras.callbacks import TensorBoard
+from tensorflow.keras.losses import sparse_categorical_crossentropy
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.regularizers import l2
+from tensorflow.random import set_seed
+
+from constants import *
+from preprocessing import dataset_creation
+
+
+def build_model() -> Model:
+    """
+    Build the CNN model
+    """
+    model = Sequential(
+        [
+            layers.Conv1D(
+                filters=16,
+                kernel_size=5,
+                activation="relu",
+                kernel_regularizer=l2(L2),
+            ),
+            layers.MaxPool1D(pool_size=3, strides=1),
+            layers.Conv1D(
+                filters=16,
+                kernel_size=3,
+                activation="relu",
+                kernel_regularizer=l2(L2),
+            ),
+            layers.MaxPool1D(pool_size=3, strides=1),
+            layers.Flatten(),
+            layers.Dense(
+                units=16,
+                activation="relu",
+                kernel_regularizer=l2(L2),
+            ),
+            layers.Dropout(rate=0.3),
+            layers.Dense(
+                units=16,
+                activation="relu",
+                kernel_regularizer=l2(L2),
+            ),
+            layers.Dropout(rate=0.3),
+            # FIXME Change output size
+            layers.Dense(units=len(BASES), activation="softmax"),
+        ]
+    )
+    model.compile(
+        optimizer=Adam(LEARNING_RATE),
+        loss=sparse_categorical_crossentropy,
+        metrics=["accuracy"],
+    )
+    return model
+
+
+def show_metrics(model, eval_dataset, test_dataset) -> None:
+    """
+    Show the model metrics
+    """
+    eval_metrics = model.evaluate(eval_dataset, verbose=0)
+    test_metrics = model.evaluate(test_dataset, verbose=0)
+    print(f"Final eval metrics - loss: {eval_metrics[0]} - accuracy: {eval_metrics[1]}")
+    print(f"Final test metrics - loss: {test_metrics[0]} - accuracy: {test_metrics[1]}")
+
+
+def run(data_file, label_file, seed_value=42) -> None:
+    """
+    Create a dataset, a model and runs training and evaluation on it
+    """
+    seed(seed_value)
+    set_seed(seed_value)
+    train_data, eval_data, test_data = dataset_creation(data_file, label_file)
+    tensorboard = TensorBoard(log_dir=LOG_DIR, histogram_freq=1, profile_batch=0)
+    model = build_model()
+    print("Training the model")
+    model.fit(
+        train_data,
+        epochs=EPOCHS,
+        validation_data=eval_data,
+        callbacks=[tensorboard],
+        verbose=0,
+    )
+    print("Training complete. Obtaining final metrics...")
+    show_metrics(model, eval_data, test_data)
+
+
+if __name__ == "__main__":
+    run(data_file="data/curesim-HVR.fastq", label_file="data/HVR.fastq")

src/preprocessing.py

@@ -0,0 +1,112 @@
+from typing import List, Tuple
+
+from Bio.motifs import create
+from Bio.SeqIO import parse
+from numpy.random import random
+from tensorflow import float32, int64
+from tensorflow.data import TFRecordDataset
+from tensorflow.io import FixedLenFeature, TFRecordWriter, parse_single_example
+from tensorflow.train import Example, Feature, Features, FloatList, Int64List
+
+from constants import *
+
+
+def generate_example(sequence, label, weight_matrix) -> bytes:
+    """
+    Create a binary-string for each sequence containing the sequence and the bases' frequency
+    """
+    schema = {
+        "sequence": Feature(
+            int64_list=Int64List(value=list(encode_sequence(sequence)))
+        ),
+        "label": Feature(int64_list=Int64List(value=list(encode_sequence(label)))),
+        "A_counts": Feature(float_list=FloatList(value=weight_matrix["A"])),
+        "C_counts": Feature(float_list=FloatList(value=weight_matrix["C"])),
+        "G_counts": Feature(float_list=FloatList(value=weight_matrix["G"])),
+        "T_counts": Feature(float_list=FloatList(value=weight_matrix["T"])),
+    }
+    example = Example(features=Features(feature=schema))
+    return example.SerializeToString()
+
+
+def encode_sequence(sequence) -> List[int]:
+    """
+    Encode the DNA sequence using the indices of the BASES constant
+    """
+    encoded_sequence = [BASES.index(element) for element in sequence]
+    return encoded_sequence
+
+
+def read_fastq(data_file, label_file) -> List[bytes]:
+    """
+    Parses a data and a label FASTQ files and generates a List of serialized Examples
+    """
+    examples = []
+    with open(data_file) as data, open(label_file) as labels:
+        for element, label in zip(parse(data, "fastq"), parse(labels, "fastq")):
+            motifs = create([element.seq])
+            example = generate_example(
+                sequence=str(element.seq),
+                label=str(label.seq),
+                weight_matrix=motifs.pwm,
+            )
+            examples.append(example)
+    return examples
+
+
+def create_dataset(
+    data_file, label_file, train_eval_test_split=[0.8, 0.1, 0.1]
+) -> None:
+    """
+    Create a training, evaluation and test dataset with a 80/10/30 split respectively
+    """
+    data = read_fastq(data_file, label_file)
+    with TFRecordWriter(TRAIN_DATASET) as training, TFRecordWriter(
+        TEST_DATASET
+    ) as test, TFRecordWriter(EVAL_DATASET) as evaluation:
+        for element in data:
+            if random() < train_eval_test_split[0]:
+                training.write(element)
+            elif random() < train_eval_test_split[0] + train_eval_test_split[1]:
+                evaluation.write(element)
+            else:
+                test.write(element)
+
+
+def process_input(byte_string) -> Example:
+    """
+    Parse a byte-string into an Example object
+    """
+    schema = {
+        "sequence": FixedLenFeature(shape=[], dtype=int64),
+        "label": FixedLenFeature(shape=[], dtype=int64),
+        "A_counts": FixedLenFeature(shape=[], dtype=float32),
+        "C_counts": FixedLenFeature(shape=[], dtype=float32),
+        "G_counts": FixedLenFeature(shape=[], dtype=float32),
+        "T_counts": FixedLenFeature(shape=[], dtype=float32),
+    }
+    return parse_single_example(byte_string, features=schema)
+
+
+def read_dataset(filepath) -> TFRecordDataset:
+    """
+    Read TFRecords files and generate a dataset
+    """
+    data_input = TFRecordDataset(filenames=filepath)
+    dataset = data_input.map(map_func=process_input)
+    shuffled_dataset = dataset.shuffle(buffer_size=10000, seed=42)
+    batched_dataset = shuffled_dataset.batch(batch_size=BATCH_SIZE).repeat(count=EPOCHS)
+    return batched_dataset
+
+
+def dataset_creation(
+    data_file, label_file
+) -> Tuple[TFRecordDataset, TFRecordDataset, TFRecordDataset]:
+    """
+    Generate the TFRecord files and split them into training, validation and test data
+    """
+    create_dataset(data_file, label_file)
+    train_data = read_dataset(TRAIN_DATASET)
+    eval_data = read_dataset(EVAL_DATASET)
+    test_data = read_dataset(TEST_DATASET)
+    return train_data, eval_data, test_data