diff --git a/.gitignore b/.gitignore
index 5a2dce5..81fe56a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,3 @@
 .gitignore
 /venv
-/.pytest_cache
\ No newline at end of file
+/.pytest_cache__pycache__/
diff --git a/.jules/bolt.md b/.jules/bolt.md
index 8780446..2f0df2c 100644
--- a/.jules/bolt.md
+++ b/.jules/bolt.md
@@ -1,3 +1,7 @@
 ## 2024-05-23 - [Regex Pre-compilation in Loops]
 **Learning:** Pre-compiling regular expressions (`re.compile`) at the module level provides a significant performance boost (measured ~1.8x speedup) when the regex is used inside a tight loop or a pandas `apply` function, compared to compiling it repeatedly or implicitly inside the loop. Vectorized string operations in Pandas are usually faster, but in complex logic cases (multiple prioritized regex groups + fallback logic), a simple pre-compiled regex with `apply` can sometimes be cleaner and sufficiently fast, or even faster if the vectorized approach requires multiple passes or expensive intermediate structures.
 **Action:** Always check for regex usage in loops or `apply` calls. If found, refactor to use module-level pre-compiled patterns. When considering vectorization, benchmark against the optimized loop version, as the overhead of complex vectorization might outweigh the benefits for moderate dataset sizes.
+
+## 2024-05-23 - [Streaming IO for Large Files]
+**Learning:** `pyteomics` parsers (specifically `mgf` and `mztab`) are compatible with `io.TextIOWrapper`, allowing for streaming file processing. This avoids the memory overhead of reading and decoding entire files into memory (`read().decode()`) before parsing, which is critical for large proteomics datasets.
+**Action:** When handling file uploads or large text files, prefer wrapping the binary stream with `io.TextIOWrapper` instead of reading the full content into a string buffer.
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diff --git a/app.py b/app.py
index aa7990e..c76f4a9 100644
--- a/app.py
+++ b/app.py
@@ -31,10 +31,10 @@ def run_streamlit_app():
 
     # Process files only when both are uploaded
     if mgf_file and mztab_file:
-        # Decode uploaded file contents (Streamlit files are bytes by default)
-        # Use StringIO to create file-like objects for pyteomics parsers
-        spectra = load_mgf(io.StringIO(mgf_file.read().decode('utf-8')))
-        psm_df = load_mztab(io.StringIO(mztab_file.read().decode('utf-8')))
+        # Use TextIOWrapper to stream decoded text without reading entire file into memory
+        # This significantly reduces memory usage for large files compared to read().decode()
+        spectra = load_mgf(io.TextIOWrapper(mgf_file, encoding='utf-8'))
+        psm_df = load_mztab(io.TextIOWrapper(mztab_file, encoding='utf-8'))
 
         # Create mappings between PSMs and spectra
         mapped = map_psms_to_spectra(spectra, psm_df)
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diff --git a/tests/test_streaming_io.py b/tests/test_streaming_io.py
new file mode 100644
index 0000000..34b211b
--- /dev/null
+++ b/tests/test_streaming_io.py
@@ -0,0 +1,44 @@
+
+import pytest
+import io
+import pandas as pd
+from data_loading import load_mgf, load_mztab
+
+def test_streaming_io_compatibility():
+    """
+    Test that data loading functions can handle io.TextIOWrapper (streaming).
+    This ensures we don't regress on memory optimization where we avoid reading
+    the entire file into memory before parsing.
+    """
+    # Sample MGF content (bytes)
+    mgf_content = b"""BEGIN IONS
+TITLE=spec1
+PEPMASS=450.25
+1.0 10.0
+END IONS
+"""
+    # Sample mzTab content (bytes)
+    mztab_content = b"""MTD\tmzTab-version\t1.0.0
+MTD\tmzTab-mode\tSummary
+PSH\tsequence\tPSM_ID\tspectra_ref
+PSM\tPEP1\t1\tms_run[1]:index=0
+"""
+
+    # Simulate Streamlit UploadedFile (which provides a binary stream) for MGF
+    mgf_file = io.BytesIO(mgf_content)
+    # Wrap in TextIOWrapper as we do in the app
+    mgf_wrapper = io.TextIOWrapper(mgf_file, encoding='utf-8')
+
+    spectra = load_mgf(mgf_wrapper)
+    assert len(spectra) == 1
+    assert spectra[0]['title'] == 'spec1'
+
+    # Simulate Streamlit UploadedFile for mzTab
+    mztab_file = io.BytesIO(mztab_content)
+    # Wrap in TextIOWrapper
+    mztab_wrapper = io.TextIOWrapper(mztab_file, encoding='utf-8')
+
+    psm_df = load_mztab(mztab_wrapper)
+    assert isinstance(psm_df, pd.DataFrame)
+    assert len(psm_df) == 1
+    assert psm_df.iloc[0]['sequence'] == 'PEP1'