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.