Bump dependencies

This reverts commit 0912600fdc.

.gitignore

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

README.md

@@ -2,10 +2,15 @@
 
 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
 
@@ -37,5 +42,70 @@ 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 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>
+```
+
+- <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

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

docker.nix

@@ -0,0 +1,14 @@
+{ 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" ];
+  };
+
+}

docs/experiments.org

@@ -1,72 +0,0 @@
-#+TITLE: Tensorflow experiments
-#+AUTHOR: Amin Kasrou Aouam
-#+PROPERTY: header-args :session poetry-session
-* Experiments
-
-#+begin_src elisp :results silent
-(pyvenv-activate "~/.cache/pypoetry/virtualenvs/locimend-hM_4JND0-py3.8/")
-#+end_src
-
-In this notebook we'll extract knowledge from our generated dataset. First, let's import our dependencies:
-
-#+begin_src python
-from tensorflow_io import genome
-#+end_src
-
-#+RESULTS:
-: 2021-05-06 20:41:53.592058: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nix/store/9ilyrqidrjbqvmnn8ykjc7lygdd86g7q-gcc-10.2.0-lib/lib:
-: 2021-05-06 20:41:53.592101: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
-
-
-Tensorflow I/O is an extension that contains a module for genome parsing, we'll use it to import the sequences contained in our FASTQ files:
-
-#+begin_src python :results silent
-def parse_data(filepath):
-    HVR = genome.read_fastq(filename=filepath)
-    return HVR.sequences, HVR.raw_quality
-#+end_src
-
-Let's import both the immuneSIM generated HVR dataset and the CuReSim processed one, which contains sequencing errors (mostly indels):
-
-#+begin_src python
-original_HVR, _ = parse_data("../data/HVR.fastq")
-processed_HVR, _ = parse_data("../data/CuReSim-HVR.fastq")
-print(original_HVR)
-print(processed_HVR)
-#+end_src
-
-#+RESULTS:
-#+begin_example
-tf.Tensor(
-[b'TGTGCCAGCAGCTTAACCATCGGACGCAGTACTTCGGGCCAGGCACGCGGCTCCTGG'
- b'TGTGCCAGCAGCTTAACCATCGGACGCAGTACTTCGGGCCAGGCACGCGGCTCCTGG'
- b'TGTGCCAGCAGCTTAACCATCGGACGCAGTACTTCGGGCCAGGCACGCGGCTCCTGG'
- b'TGTGCCAGCAGCTTAACCATCGGACGCAGTACTTCGGGCCAGGCACGCGGCTCCTGG'
- b'TGTGCCAGCAGCTTAACCATCGGACGCAGTACTTCGGGCCAGGCACGCGGCTCCTGG'
- b'TGTGCCAGCAGCTTAACCATCGGACGCAGTACTTCGGGCCAGGCACGCGGCTCCTGG'
- b'TGTGCCAGCAGCTTAACCATCGGACGCAGTACTTCGGGCCAGGCACGCGGCTCCTGG'
- b'TGTGCCAGCAGCTTAACCATCGGACGCAGTACTTCGGGCCAGGCACGCGGCTCCTGG'
- b'TGTGCCAGCAGCTTAACCATCGGACGCAGTACTTCGGGCCAGGCACGCGGCTCCTGG'
- b'TGTGCCAGCAGCTTAACCATCGGACGCAGTACTTCGGGCCAGGCACGCGGCTCCTGG'], shape=(10,), dtype=string)
-tf.Tensor(
-[b'GCGCCAGCAGCTATTGGATATGGACTAGCTACTC'
- b'TGTGCCAGCAGTGATGTGGTGACATGGGTGCGTAGCAATCAGCCAGCATG'
- b'GCGCCAGCAGCTTGGATAGGACTAGCTACTT'
- b'TGTGCCAGCAGTGAATGGGTGACAGGGTGCGTAGCATCAGCCCCAGCATTT'
- b'TTGCGCAGCAGCTTGGATAGGACTAGCTACTT'
- b'TGTGCCAGCAGTGAATGGGGACAGGGGCGTAGCAATCAGCCCCAGCATTT'
- b'TTGCGCCAGCAGCTTGGATAGGACTAGCTACTT'
- b'TGTGCAGCAGTGAATGGGGACAGGGGCGTAGCAATCAGCCCCAGCATTT'
- b'TGCGCCAGCAGCTTGGATAGGACTAGCTACTT'
- b'TGTGCCAGCAGTGAATGGGGACAGGGGCGTAGCAATCAGCCCAGCATTT'
- b'TTGCGCCAGCAGCTTGGATAGGACTAGCTACTT'
- b'TGTGCCAGCAGTGAATGGGGACAGGGGCGTAGCAATCAGCCCCAGCATTT'
- b'TGCGCCAGCAGCTTGGATAGGACTAGCTACTT'
- b'TGTGCCAGCAGTGAATGGGGACAGGGGCGTAGCAATCAGCCCCAGCATTT'
- b'TGCGCCAGCAGCTTGGATAGGACTAGCTACTT'
- b'TGTGCCAGCAGTGAATGGGGACAGGGGCGTAGCAATCAGCCCCAGCATTT'
- b'TGCGCCAGCAGCTTGGATAGGACTAGCTACTT'
- b'TGTGCCAGCAGTGAATGGGGACAGGGGCGTAGCAATCAGCCCCAGCATTT'
- b'TGCGCCAGCAGCTTGGATAGGACTAGCTACTT'
- b'TGTGCCAGCAGTGAATGGGGACAGGGGCGTAGCAATCAGCCCCAGCATTT'], shape=(20,), dtype=string)
-#+end_example

flake.lock

@@ -0,0 +1,41 @@
+{
+  "nodes": {
+    "flake-utils": {
+      "locked": {
+        "lastModified": 1631561581,
+        "narHash": "sha256-3VQMV5zvxaVLvqqUrNz3iJelLw30mIVSfZmAaauM3dA=",
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "rev": "7e5bf3925f6fbdfaf50a2a7ca0be2879c4261d19",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "type": "github"
+      }
+    },
+    "nixpkgs": {
+      "locked": {
+        "lastModified": 1634044603,
+        "narHash": "sha256-JX9/U/ci9Gw1fhWjEB3HfzDK8bAbcfQcTO6fEJmgFfo=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "15847b4b4fc260fb400880aa3cbee65a65f252c5",
+        "type": "github"
+      },
+      "original": {
+        "id": "nixpkgs",
+        "type": "indirect"
+      }
+    },
+    "root": {
+      "inputs": {
+        "flake-utils": "flake-utils",
+        "nixpkgs": "nixpkgs"
+      }
+    }
+  },
+  "root": "root",
+  "version": 7
+}

flake.nix

@@ -0,0 +1,11 @@
+{
+  description =
+    "locimend is a tool that corrects DNA sequencing errors using Deep Learning";
+
+  inputs.flake-utils.url = "github:numtide/flake-utils";
+
+  outputs = { self, nixpkgs, flake-utils }:
+    flake-utils.lib.eachDefaultSystem (system:
+      let pkgs = nixpkgs.legacyPackages.${system};
+      in { devShell = import ./shell.nix { inherit pkgs; }; });
+}

locimend/api.py

@@ -0,0 +1,27 @@
+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

@@ -0,0 +1,24 @@
+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

@@ -0,0 +1,40 @@
+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

@@ -0,0 +1,89 @@
+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

@@ -0,0 +1,148 @@
+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": "0d337eb6b77c8911cd02ed92e63fcc2a8949b404",
-        "sha256": "1xm6ss7j3zscpiczz3kxjad3jd1qvy5zpm35kqri6p9mp4jzna1x",
+        "rev": "f930ea227cecaed1f1bdb047fef54fe4f0721c8c",
+        "sha256": "04khbc44lppzz0m2g56zr7vafv4fvnb7rfbz7c03dqw6k99svj1c",
         "type": "tarball",
-        "url": "https://github.com/NixOS/nixpkgs/archive/0d337eb6b77c8911cd02ed92e63fcc2a8949b404.tar.gz",
+        "url": "https://github.com/NixOS/nixpkgs/archive/f930ea227cecaed1f1bdb047fef54fe4f0721c8c.tar.gz",
         "url_template": "https://github.com/<owner>/<repo>/archive/<rev>.tar.gz"
     }
 }

poetry.lock

@@ -0,0 +1,929 @@
+[[package]]
+name = "absl-py"
+version = "0.14.1"
+description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+six = "*"
+
+[[package]]
+name = "asgiref"
+version = "3.4.1"
+description = "ASGI specs, helper code, and adapters"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+tests = ["pytest", "pytest-asyncio", "mypy (>=0.800)"]
+
+[[package]]
+name = "astunparse"
+version = "1.6.3"
+description = "An AST unparser for Python"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+six = ">=1.6.1,<2.0"
+
+[[package]]
+name = "biopython"
+version = "1.79"
+description = "Freely available tools for computational molecular biology."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+numpy = "*"
+
+[[package]]
+name = "cachetools"
+version = "4.2.4"
+description = "Extensible memoizing collections and decorators"
+category = "main"
+optional = false
+python-versions = "~=3.5"
+
+[[package]]
+name = "certifi"
+version = "2021.10.8"
+description = "Python package for providing Mozilla's CA Bundle."
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "charset-normalizer"
+version = "2.0.7"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+category = "main"
+optional = false
+python-versions = ">=3.5.0"
+
+[package.extras]
+unicode_backport = ["unicodedata2"]
+
+[[package]]
+name = "clang"
+version = "5.0"
+description = "libclang python bindings"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "click"
+version = "8.0.3"
+description = "Composable command line interface toolkit"
+category = "main"
+optional = false
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pyproject.toml

@@ -6,14 +6,19 @@ authors = ["coolneng <akasroua@gmail.com>"]
 license = "GPL-3.0-or-later"
 
 [tool.poetry.dependencies]
-python = "3.8.*"
+python = "3.9.*"
 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"

shell.nix

@@ -1,9 +1,9 @@
-{ sources ? import ./nix/sources.nix, pkgs ? import sources.nixpkgs { } }:
+{ pkgs ? import <nixpkgs> { } }:
 
 with pkgs;
 
 mkShell {
-  buildInputs = [ python38 poetry ];
+  buildInputs = [ python39 poetry ];
   shellHook = ''
     export LD_LIBRARY_PATH=${pkgs.stdenv.cc.cc.lib}/lib:$LD_LIBRARY_PATH
     unset SOURCE_DATE_EPOCH

src/constants.py

@@ -1,7 +0,0 @@
-BASES = "ACGT"
-TRAIN_DATASET = "data/train_data.tfrecords"
-TEST_DATASET = "data/test_data.tfrecords"
-EPOCHS = 1000
-BATCH_SIZE = 256
-LEARNING_RATE = 0.004
-L2 = 0.001

src/model.py

@@ -1,46 +0,0 @@
-from tensorflow.keras import Model, Sequential, layers
-from tensorflow.keras.regularizers import l2
-
-from constants import BASES
-
-
-def build_model(hyper_parameters) -> Model:
-    """
-    Builds the CNN model
-    """
-    return Sequential(
-        [
-            # Two convolutions + maxpooling blocks
-            layers.Conv1D(
-                filters=16,
-                kernel_size=5,
-                activation="relu",
-                kernel_regularizer=l2(hyper_parameters.l2),
-            ),
-            layers.MaxPool1D(pool_size=3, strides=1),
-            layers.Conv1D(
-                filters=16,
-                kernel_size=3,
-                activation="relu",
-                kernel_regularizer=l2(hyper_parameters.l2),
-            ),
-            layers.MaxPool1D(pool_size=3, strides=1),
-            # Flatten the input volume
-            layers.Flatten(),
-            # Two fully connected layers, each followed by a dropout layer
-            layers.Dense(
-                units=16,
-                activation="relu",
-                kernel_regularizer=l2(hyper_parameters.l2),
-            ),
-            layers.Dropout(rate=0.3),
-            layers.Dense(
-                units=16,
-                activation="relu",
-                kernel_regularizer=l2(hyper_parameters.l2),
-            ),
-            layers.Dropout(rate=0.3),
-            # Output layer with softmax activation
-            layers.Dense(units=len(BASES), activation="softmax"),
-        ]
-    )

src/preprocessing.py

@@ -1,103 +0,0 @@
-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, reference_sequence, 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)))
-        ),
-        "reference_sequence": Feature(
-            int64_list=Int64List(value=list(encode_sequence(reference_sequence)))
-        ),
-        "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),
-                reference_sequence=str(label.seq),
-                weight_matrix=motifs.pwm,
-            )
-            examples.append(example)
-    return examples
-
-
-def create_dataset(filepath) -> None:
-    """
-    Create a training and test dataset with a 70/30 split respectively
-    """
-    data = read_fastq(data_file, label_file)
-    train_test_split = 0.7
-    with TFRecordWriter(TRAIN_DATASET) as train, TFRecordWriter(TEST_DATASET) as test:
-        for element in data:
-            if random() < train_test_split:
-                train.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),
-        "reference_sequence": 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]:
-    create_dataset(data_file, label_file)
-    train_data = read_dataset(TRAIN_DATASET)
-    test_data = read_dataset(TEST_DATASET)
-    return train_data, test_data