Peak Property Preparation From CSV

peak-property-preparation-from-csv

Summary

Convert tabular nucleoside or peptide molecular data from CSV format into a peak properties dictionary compatible with SMITER's LC-MS/MS simulation pipeline. This preparation step abstracts chemical formulas and molecular identifiers into SMITER's internal representation, enabling downstream fragmentation modeling and synthetic spectrum generation.

When to use

You have a CSV file containing nucleoside or peptide molecular data (formulas, identifiers, retention times, intensities) that you want to simulate as LC-MS/MS runs. Use this skill as the mandatory first step before selecting a fragmentation model and noise injector in SMITER.

When NOT to use

• Input data is already in SMITER's native peak properties format (skip conversion).
• Molecular data lacks chemical formula information (SMITER's fragmentation is formula-based).
• CSV schema does not align with SMITER's expected columns (consult example_data in the repository for the required schema).

Inputs

• CSV file with molecular data (chemical formulas, identifiers, peak properties)
• SMITER library module (smiter.lib)

Outputs

• Peak properties dictionary (Python dict or SMITER-compatible object)
• Validated molecular metadata ready for fragmentation and noise injection

How to apply

Organize your input CSV with columns for molecular identifiers, chemical formulas, and peak properties (e.g., retention time, intensity). Call smiter.lib.csv_to_peak_properties on the CSV file to deserialize and validate the data into SMITER's peak properties dictionary format. This dictionary then becomes the input to downstream workflow steps: fragmentation model selection, noise injection, and gradient-parameter definition. The conversion abstracts the tabular format into SMITER's internal object model, allowing the modular fragmentation interface to select between nucleoside and peptide fragmentors without data re-formatting.

Related tools

• SMITER (LC-MS/MS simulation platform providing the csv_to_peak_properties conversion function and downstream fragmentation and noise models) — https://github.com/LeidelLab/SMITER
• Python (Runtime environment for SMITER library and CSV I/O operations)

Examples

from smiter.lib import csv_to_peak_properties; peak_props = csv_to_peak_properties('example_data.csv')

Evaluation signals

• Peak properties dictionary is non-empty and contains all expected molecular entries from the input CSV.
• Each entry in the dictionary has valid chemical formula strings (parseable by SMITER's formula engine).
• Dictionary keys and structure match SMITER's expected format for downstream fragmentation model input (e.g., accessible via smiter.synthetic_mzml.write_mzml).
• No validation errors or exceptions are raised during conversion; the function returns successfully.
• Downstream fragmentation model (PeptideFragmentor or NucleosideFragmentor) accepts the dictionary without schema errors.

Limitations

• Conversion relies on CSV schema correctness; malformed or missing columns will cause silent data loss or exceptions.
• Chemical formula validation is deferred to SMITER's fragmentation modules; invalid formulas may only be detected later in the simulation.
• The function does not perform retention time prediction or normalization; intensities and retention times are passed through as-is from the CSV.
• No support for metadata beyond chemical formulas and peak properties; additional experimental context (e.g., instrument parameters, quantification standards) must be handled separately.

Evidence

• [other] You can convert a csv file (as in example_data) to csv using smiter.lib.csv_to_peak_properties: "You can convert a csv file (as in example_data) to csv using smiter.lib.csv_to_peak_properties"
• [other] create the peak properties dict: "1. create the peak properties dict (You can convert a csv file (as in example_data) to csv using smiter.lib.csv_to_peak_properties)"
• [readme] It enables the simulation of any biomolecule since all calculations are based on the chemical formulas.: "It enables the simulation of any biomolecule since all calculations are based on the chemical formulas."
• [readme] SMITER offers several methods for peptide fragmentation and two models for nucleoside fragmentation: "By default, SMITER uses an established noise model and offers several methods for peptide fragmentation or two models for nucleoside fragmentation."