Inference of Peptidoforms Documentation

This module implements the Inference of PeptidoForms (IPF) workflow.

IPF is a statistical framework for inferring peptidoforms (modified peptides) and their probabilities from mass spectrometry data. The module includes functions for precursor-level and peptidoform-level inference, Bayesian modeling, and signal propagation across aligned runs.

Key Features:

• Precursor-level inference using MS1 and MS2 data.

• Peptidoform-level inference using transition-level data.

• Bayesian modeling for posterior probability computation.

• Signal propagation across aligned runs.

• Model-based FDR estimation.

Functions:

• compute_model_fdr: Computes model-based FDR estimates from posterior error probabilities.

• prepare_precursor_bm: Prepares Bayesian model data for precursor-level inference.

• transfer_confident_evidence_across_runs: Propagates confident evidence across aligned runs.

• prepare_transition_bm: Prepares Bayesian model data for transition-level inference.

• apply_bm: Applies the Bayesian model to compute posterior probabilities.

• precursor_inference: Conducts precursor-level inference.

• peptidoform_inference: Conducts peptidoform-level inference.

• infer_peptidoforms: Orchestrates the IPF workflow.

Classes:

None

infer_peptidoforms	Orchestrates the Inference of PeptidoForms (IPF) workflow.
peptidoform_inference	Conducts peptidoform-level inference.
precursor_inference	Conducts precursor-level inference.
apply_bm	Applies the Bayesian model to compute posterior probabilities.
prepare_transition_bm	Prepares Bayesian model data for transition-level inference.
transfer_confident_evidence_across_runs	Propagates confident evidence across aligned runs.
prepare_precursor_bm	Prepares Bayesian model data for precursor-level inference.
compute_model_fdr	Computes model-based FDR estimates from posterior error probabilities.