diff --git a/src/preprocessing.py b/src/preprocessing.py
index 98fb80a..caf8019 100644
--- a/src/preprocessing.py
+++ b/src/preprocessing.py
@@ -1,4 +1,4 @@
-from typing import List
+from typing import List, Tuple
 
 from Bio.motifs import create
 from Bio.SeqIO import parse
@@ -8,10 +8,10 @@ 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 BASES, BATCH_SIZE, EPOCHS, TEST_DATASET, TRAIN_DATASET
+from constants import *
 
 
-def generate_example(sequence, weight_matrix) -> bytes:
+def generate_example(sequence, reference_sequence, weight_matrix) -> bytes:
     """
     Create a binary-string for each sequence containing the sequence and the bases' frequency
     """
@@ -19,6 +19,9 @@ def generate_example(sequence, weight_matrix) -> bytes:
         "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"][0]])),
         "C_counts": Feature(float_list=FloatList(value=[weight_matrix["C"][0]])),
         "G_counts": Feature(float_list=FloatList(value=[weight_matrix["G"][0]])),
@@ -36,16 +39,19 @@ def encode_sequence(sequence) -> List[int]:
     return encoded_sequence
 
 
-def read_fastq(filepath) -> List[bytes]:
+def read_fastq(data_file, label_file) -> List[bytes]:
     """
-    Parse a FASTQ file and generate a List of serialized Examples
+    Parses a data and a label FASTQ files and generates a List of serialized Examples
     """
     examples = []
-    with open(filepath) as handle:
-        for row in parse(handle, "fastq"):
-            sequence = str(row.seq)
-            motifs = create(row.seq)
-            example = generate_example(sequence=sequence, weight_matrix=motifs.pwm)
+    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
 
@@ -54,7 +60,7 @@ def create_dataset(filepath) -> None:
     """
     Create a training and test dataset with a 70/30 split respectively
     """
-    data = read_fastq(filepath)
+    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:
@@ -70,6 +76,7 @@ def process_input(byte_string) -> Example:
     """
     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),
@@ -78,12 +85,19 @@ def process_input(byte_string) -> Example:
     return parse_single_example(byte_string, features=schema)
 
 
-def read_dataset() -> TFRecordDataset:
+def read_dataset(filepath) -> TFRecordDataset:
     """
     Read TFRecords files and generate a dataset
     """
-    data_input = TFRecordDataset(filenames=[TRAIN_DATASET, TEST_DATASET])
+    data_input = TFRecordDataset(filenames=filepath)
     dataset = data_input.map(map_func=process_input)
-    shuffled_dataset = dataset.shuffle(buffer_size=10000, reshuffle_each_iteration=True)
+    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