diff --git a/doc/tutorial/sequence/index.rst b/doc/tutorial/sequence/index.rst
index ab1a96b83..c879c3e04 100644
--- a/doc/tutorial/sequence/index.rst
+++ b/doc/tutorial/sequence/index.rst
@@ -43,5 +43,5 @@ For an unambiguous DNA sequence, the alphabet comprises the four nucleobases.
     align_optimal
     align_heuristic
     align_multiple
-    annotations
-    profiles
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+    profiles
+    annotations
\ No newline at end of file
diff --git a/doc/tutorial/sequence/profiles.rst b/doc/tutorial/sequence/profiles.rst
new file mode 100644
index 000000000..8554b4588
--- /dev/null
+++ b/doc/tutorial/sequence/profiles.rst
@@ -0,0 +1,134 @@
+.. include:: /tutorial/preamble.rst
+
+Sequence profiles and position-specific scoring matrices
+========================================================
+
+Often sequences are not viewed in isolation:
+For example, if you investigate a protein family, you do not handle a single sequence,
+but an arbitrarily large collection of highly similar sequences.
+At some point handling those sequences as a mere collection of individual sequences
+becomes impractical for answering common questions, like which are the prevalent
+amino acids at a certain positions of the sequences.
+
+Sequence profiles
+-----------------
+
+.. currentmodule:: biotite.sequence
+
+This is where *sequence profiles* come into play:
+The condense the a collection of aligned sequences into a matrix that tracks the
+frequency of each symbol at each position of the alignment.
+Hence, asking the questions such as '*How frequent is an alanine at the n-th position?*'
+becomes a trivial indexing operation.
+
+As example for profiles, we will reuse the cyclotide sequence family from the
+:doc:`previous chapter <align_multiple>`.
+
+.. jupyter-execute::
+
+    from tempfile import NamedTemporaryFile
+    import biotite.sequence.io.fasta as fasta
+    import biotite.database.entrez as entrez
+
+    query = (
+        entrez.SimpleQuery("Cyclotide") &
+        entrez.SimpleQuery("cter") &
+        entrez.SimpleQuery("srcdb_swiss-prot", field="Properties") ^
+        entrez.SimpleQuery("Precursor")
+    )
+    uids = entrez.search(query, "protein")
+    temp_file = NamedTemporaryFile(suffix=".fasta", delete=False)
+    fasta_file = fasta.FastaFile.read(
+        entrez.fetch_single_file(uids, temp_file.name, "protein", "fasta")
+    )
+    sequences = {
+        # The cyclotide variant is the last character in the header
+        header[-1]: seq for header, seq in fasta.get_sequences(fasta_file).items()
+    }
+    # Extract cyclotide N as query sequence for later
+    query = sequences.pop("N")
+
+To create a profile, we first need to align the sequences, so corresponding symbols
+appear the same position.
+Then we can create a :class:`SequenceProfile` object from the :class:`Alignment`, which
+simply counts for each alignment column (i.e. the sequence position) the number of
+occurrences for each symbol.
+
+.. jupyter-execute::
+
+    import biotite.sequence as seq
+    import biotite.sequence.align as align
+
+    alignment, _, _, _ = seq.align.align_multiple(
+        list(sequences.values()),
+        align.SubstitutionMatrix.std_protein_matrix(),
+        gap_penalty=-5,
+    )
+    profile = seq.SequenceProfile.from_alignment(alignment)
+    count_matrix = profile.symbols
+    print(profile)
+
+Each row in the displayed count matrix
+(accessible via :attr:`SequenceProfile.symbols`) refers to a single position, i.e. a
+column in the input MSA, and each column refers to a symbol in the underlying alphabet
+(accessible via :attr:`SequenceProfile.alphabet`).
+For completeness it should be noted that :attr:`SequenceProfile.gaps` also tracks the
+gaps for each position in the alignment, but we will not further use this in this
+tutorial.
+
+.. jupyter-execute::
+
+    print(profile.to_consensus())
+
+Note that the information about the individual sequences is lost in the condensation
+process: There is no way to reconstruct the original sequences from the profile.
+
+Position-specific scoring matrices
+----------------------------------
+
+.. currentmodule:: biotite.sequence.align
+
+Sequence profiles can achieve even more:
+The substitution matrices we have seen so far assign a score to a pair of two symbols,
+regardless of their position in a sequence.
+However, as discussed above, the position is crucial information to determine how
+conserved a certain symbol is in a family of sequences.
+
+Hence, we extend the concept of substitution matrices to *position-specific* matrices,
+which assign a score to a symbol and a position (instead of another symbols).
+
+.. jupyter-execute::
+
+    # For a real analysis each amino acid background frequencies should be provided
+    log_odds = profile.log_odds_matrix(pseudocount=1)
+
+Now, we encounter a problem:
+To create a :class:`SubstitutionMatrix` object from the log-odds matrix, we require two
+:class:`Alphabet` objects:
+One is taken from the query sequence to be aligned to the profile, but which alphabet
+do we use for the positional axis of the matrix?
+Likewise, the alignment functions (e.g. :func:`align_optimal()`) require a two sequences
+to be aligned, but we only have one query sequence.
+
+.. currentmodule:: biotite.sequence
+
+To solve this problem :mod:`biotite.sequence` provides the :class:`PositionalSequence`
+which acts as a placeholder for the second sequence in the alignment.
+Its alphabet contains a unique symbol for each position, i.e. the alphabet has the
+sought length.
+
+.. jupyter-execute::
+
+    positional_seq = seq.PositionalSequence(profile.to_consensus())
+    matrix = align.SubstitutionMatrix(
+        positional_seq.alphabet,
+        profile.alphabet,
+        log_odds
+    )
+    alignment = align.align_optimal(
+        positional_seq, query, matrix, gap_penalty=-5, max_number=1
+    )[0]
+    print(alignment)
+
+More on positional sequences
+----------------------------
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