Many methods, including many prototype methods, have been proposed in recent years to address this problem.
This list is far from complete and other methods including many treatment options are currently excluded.
However, data produced by current state-of-the-art methods include many false positives, which can hinder the analyses needed to derive biological insights.
However, most segmentation methods include many parameters to be carefully tuned and/or fail to exploit the unsurpassed resolution of the data.
Indeed, the differentially acetylated targets we identified through both peak-calling algorithms and criteria-based selection methods included many known and novel noncoding RNAs.
Those less familiar with CTs should realise that this us a class of methods including many different approaches, and that these different approaches may result in considerable differences in classifications.
Commonly, the employed methods include many manual or partly automated steps like protein precipitation, dilution, solid phase extraction, evaporation, and derivatization preceding a gas chromatography (GC /mass spectrometry (MS) or liquid chromatography (LC)/MS analysis.
While the second method sometimes includes many steps and requires using very expensive reagents, the third method is a one-step technique and if it is necessary, allows introducing more than one functional group, differing in their chemical nature (Table 1).
Our evaluation uses similar supervised and semi-supervised methods but includes many more unsupervised methods, distinguishes between experimental types and performs repeats, resulting in a more complete picture.
