Proteins are dominant executors of living processes. Compared to genetic 
variations, changes in the molecular structure and state of a protein (i.e. 
proteoforms) are more directly related to pathological changes in diseases. 
Characterizing proteoforms involves identifying and locating primary structure 
alterations (PSAs) in proteoforms, which is of practical importance for the 
advancement of the medical profession. With the development of mass spectrometry 
(MS) technology, the characterization of proteoforms based on top-down MS 
technology has become possible. This type of method is relatively new and faces 
many challenges. Since the proteoform identification is the most important 
process in characterizing proteoforms, we comprehensively review the existing 
proteoform identification methods in this study. Before identifying proteoforms, 
the spectra need to be preprocessed, and protein sequence databases can be 
filtered to speed up the identification. Therefore, we also summarize some 
popular deconvolution algorithms, various filtering algorithms for improving the 
proteoform identification performance and various scoring methods for localizing 
proteoforms. Moreover, commonly used methods were evaluated and compared in this 
review. We believe our review could help researchers better understand the 
current state of the development in this field and design new efficient 
algorithms for the proteoform characterization.