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In shotgun proteomics, tandem mass spectrometry is used to identify peptides derived from proteins. After the peptides are detected, proteins are reassembled via a reference database of protein or gene information. Redundancy and homology between protein records in databases make it challenging to assign peptides to proteins that may or may not be in an experimental sample. Here, a probability model is introduced for determining the likelihood that peptides are correctly assigned to proteins. This model derives consistent probability estimates for assembled proteins. The probability scores make it easier to confidently identify proteins in complex samples and to accurately estimate false-positive rates. The algorithm based on this model is robust in creating protein complements from peptides from bovine protein standards, yeast, Ustilago maydis cell lysates, and Arabidopsis thaliana leaves. It also eliminates the side effects of redundancy and homology from the reference databases by employing a new concept of peptide grouping and by coherently distinguishing distinct peptides from unique records and shared peptides from homologous proteins. The software that runs the algorithm, called PANORAMICS, provides a tool to help analyze the data based on a researcher's knowledge about the sample. The software operates efficiently and quickly compared to other software platforms.

Probability Model for Assessing Proteins Assembled from Peptide Sequences Inferred from Tandem Mass Spectrometry Data