diff --git a/tests/test_sequences.py b/tests/test_sequences.py
new file mode 100644
index 00000000..8866214e
--- /dev/null
+++ b/tests/test_sequences.py
@@ -0,0 +1,67 @@
+import pytest
+import numpy as np
+import torch
+from Bio.Seq import Seq
+from Bio.Data import CodonTable
+from netam.sequences import (
+    AA_STR_SORTED,
+    CODONS,
+    CODON_AA_INDICATOR_MATRIX,
+    aa_onehot_tensor_of_str,
+    nt_idx_array_of_str,
+    nt_subs_indicator_tensor_of,
+    translate_sequences,
+)
+
+
+def test_nucleotide_indices_of_codon():
+    assert nt_idx_array_of_str("AAA").tolist() == [0, 0, 0]
+    assert nt_idx_array_of_str("TAC").tolist() == [3, 0, 1]
+    assert nt_idx_array_of_str("GCG").tolist() == [2, 1, 2]
+
+
+def test_aa_onehot_tensor_of_str():
+    aa_str = "QY"
+
+    expected_output = torch.zeros((2, 20))
+    expected_output[0][AA_STR_SORTED.index("Q")] = 1
+    expected_output[1][AA_STR_SORTED.index("Y")] = 1
+
+    output = aa_onehot_tensor_of_str(aa_str)
+
+    assert output.shape == (2, 20)
+    assert torch.equal(output, expected_output)
+
+
+def test_translate_sequences():
+    # sequence without stop codon
+    seq_no_stop = ["AGTGGTGGTGGTGGTGGT"]
+    assert translate_sequences(seq_no_stop) == [str(Seq(seq_no_stop[0]).translate())]
+
+    # sequence with stop codon
+    seq_with_stop = ["TAAGGTGGTGGTGGTAGT"]
+    with pytest.raises(ValueError):
+        translate_sequences(seq_with_stop)
+
+
+def test_indicator_matrix():
+    reconstructed_codon_table = {}
+    indicator_matrix = CODON_AA_INDICATOR_MATRIX.numpy()
+
+    for i, codon in enumerate(CODONS):
+        row = indicator_matrix[i]
+        if np.any(row):
+            amino_acid = AA_STR_SORTED[np.argmax(row)]
+            reconstructed_codon_table[codon] = amino_acid
+
+    table = CodonTable.unambiguous_dna_by_id[1]  # 1 is for the standard table
+
+    assert reconstructed_codon_table == table.forward_table
+
+
+def test_subs_indicator_tensor_of():
+    parent = "NAAA"
+    child = "CAGA"
+    expected_output = torch.tensor([0, 0, 1, 0], dtype=torch.float)
+    output = nt_subs_indicator_tensor_of(parent, child)
+    assert torch.equal(output, expected_output)