Fuzzy Clustering of Likelihood Curves for Finding Interesting Patterns in Expression Profiles

Peptides derived from proteins are routinely analysed in so-called bottom-up proteome studies to determine the amounts of corresponding proteins. Such studies easily sequence and analyse thousands of peptides per hour by the combination of liquid chromatography and mass spectrometry instruments (LC-MS). However, quantified peptides belonging to the same protein do not necessarily exhibit the same regulatory information in all cases. Several causes can produce these regulatory inconsistencies at the peptide level. Quantitative data might be simply influenced by specific properties of the analytical procedure. However, it can also indicate meaningful biological processes such as the post-translational modification (PTM) of amino acids regulated in individual protein regions. This article describes a fuzzy clustering approach allowing the automatic detection of regulatory peptide clusters within individual proteins. The approach utilises likelihood curves to summarise the regulatory information of each peptide, based on a noise model of the used analytical workflow. The shape of these curves directly correlates with both the regulatory information and the underlying data quality, serving as a representative starting point for fuzzy clustering of peptide data assigned to one protein.