MOTIVATION: Although proteomics has rapidly developed in the past decade, 
researchers are still in the early stage of exploring the world of complex 
proteoforms, which are protein products with various primary structure 
alterations resulting from gene mutations, alternative splicing, 
post-translational modifications, and other biological processes. Proteoform 
identification is essential to mapping proteoforms to their biological functions 
as well as discovering novel proteoforms and new protein functions. Top-down 
mass spectrometry is the method of choice for identifying complex proteoforms 
because it provides a 'bird's eye view' of intact proteoforms. The combinatorial 
explosion of various alterations on a protein may result in billions of possible 
proteoforms, making proteoform identification a challenging computational 
problem.
RESULTS: We propose a new data structure, called the mass graph, for efficient 
representation of proteoforms and design mass graph alignment algorithms. We 
developed TopMG, a mass graph-based software tool for proteoform identification 
by top-down mass spectrometry. Experiments on top-down mass spectrometry 
datasets showed that TopMG outperformed existing methods in identifying complex 
proteoforms.
AVAILABILITY AND IMPLEMENTATION: 
http://proteomics.informatics.iupui.edu/software/topmg/.
CONTACT: xwliu@iupui.edu.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.