10.1021/acs.jproteome.6b00724.s004 Sander Willems Sander Willems Maarten Dhaenens Maarten Dhaenens Elisabeth Govaert Elisabeth Govaert Laura De Clerck Laura De Clerck Paulien Meert Paulien Meert Christophe Van Neste Christophe Van Neste Filip Van Nieuwerburgh Filip Van Nieuwerburgh Dieter Deforce Dieter Deforce Flagging False Positives Following Untargeted LC–MS Characterization of Histone Post-Translational Modification Combinations American Chemical Society 2016 candidate isoforms histone PTM combinations LC positive Current quality controls unconsidered workflow Histone Post-Translational Modification Combinations Epigenetic changes Flagging False Positives histone post-translational modifications annotation alternative isobaric PTMCs 44 histone extracts combinatorial explosion 2016-11-07 00:00:00 Dataset https://acs.figshare.com/articles/dataset/Flagging_False_Positives_Following_Untargeted_LC_MS_Characterization_of_Histone_Post-Translational_Modification_Combinations/4291295 Epigenetic changes can be studied with an untargeted characterization of histone post-translational modifications (PTMs) by liquid chromatography–mass spectrometry (LC–MS). While prior information about more than 20 types of histone PTMs exists, little is known about histone PTM combinations (PTMCs). Because of the combinatorial explosion it is intrinsically impossible to consider all potential PTMCs in a database search. Consequentially, high-scoring false positives with unconsidered but correct alternative isobaric PTMCs can occur. Current quality controls can neither estimate the amount of unconsidered alternatives nor flag potential false positives. Here, we propose a conceptual workflow that provides such options. In this workflow, an in silico modeling of all candidate isoforms with known-to-exist PTMs is made. The most frequently occurring PTM sets of these candidate isoforms are determined and used in several database searches. This suppresses the combinatorial explosion while considering as many candidate isoforms as possible. Finally, annotations can be classified as unique or ambiguous, the latter implying false positives. This workflow was evaluated on an LC–MS data set containing 44 histone extracts. We were able to consider 60% of all candidate isoforms. Importantly, 40% of all annotations were classified as ambiguous. This highlights the need for a more thorough evaluation of modified peptide annotations.