In mass spectrometry-based shotgun proteomics, protein quantification and 
protein identification are two major computational problems. To quantify the 
protein abundance, a list of proteins must be firstly inferred from the raw 
data. Then the relative or absolute protein abundance is estimated with 
quantification methods, such as spectral counting. Until now, most researchers 
have been dealing with these two processes separately. In fact, the protein 
inference problem can be regarded as a special protein quantification problem in 
the sense that truly present proteins are those proteins whose abundance values 
are not zero. Some recent published papers have conceptually discussed this 
possibility. However, there is still a lack of rigorous experimental studies to 
test this hypothesis. In this paper, we investigate the feasibility of using 
protein quantification methods to solve the protein inference problem. Protein 
inference methods aim to determine whether each candidate protein is present in 
the sample or not. Protein quantification methods estimate the abundance value 
of each inferred protein. Naturally, the abundance value of an absent protein 
should be zero. Thus, we argue that the protein inference problem can be viewed 
as a special protein quantification problem in which one protein is considered 
to be present if its abundance is not zero. Based on this idea, our paper tries 
to use three simple protein quantification methods to solve the protein 
inference problem effectively. The experimental results on six data sets show 
that these three methods are competitive with previous protein inference 
algorithms. This demonstrates that it is plausible to model the protein 
inference problem as a special protein quantification task, which opens the door 
of devising more effective protein inference algorithms from a quantification 
perspective. The source codes of our methods are available at: 
http://code.google.com/p/protein-inference/.