Environment & data I/O

This page covers how to set up the Biotite environment and move data in and out of a run — the input formats it reads, where to read and write files, and how to export results. Pinning dependencies and keeping paths storage-friendly are what most affect cold-start time and clean syncing.

Recommended dependencies

biotite[full]>=1.1
numpy>=1.26
pandas>=2.2
matplotlib>=3.8        # Optional plotting
mdanalysis>=2.7        # Optional trajectory parsing

Pin these in requirements.txt for a local environment. The chat's code workspace already ships Biotite, NumPy and pandas.

Input formats

• Sequences: FASTA, GenBank, EMBL
• Structures: PDB, mmCIF, MMTF
• Compressed archives: .tar.gz / .zip for batch uploads (extract to inputs/)

Storage-friendly paths

• Read inputs from inputs/ and write outputs to outputs/.
• Use unique run folders, e.g., outputs/run-<timestamp>/.
• Keep filenames ASCII to avoid path issues when syncing to storage.

Simple batch pattern

find inputs/sequences -name "*.fasta" -print0 | while IFS= read -r -d '' file; do
  python scripts/parse_sequences.py "$file" "outputs/$(basename "$file").json"
done

parse_sequences.py should validate the file encoding and raise clear errors for empty/invalid records.

Exporting structures

• Prefer mmCIF for downstream pipelines that need robust metadata.
• Compress large outputs: gzip outputs/*.pdb to cut transfer size.
• Include a SUMMARY.md with counts, RMSD stats, and any skipped files.