Motivation: In mass spectrometry-based shotgun proteomics, proteinquantification and protein identification are two major computational problems.To quantify the protein abundance, a list of proteins must be firstly inferredfrom the sample. Then the relative or absolute protein abundance is estimatedwith quantification methods, such as spectral counting. Until now, researchershave been dealing with these two processes separately. In fact, they are twosides of same coin in the sense that truly present proteins are those proteinswith non-zero abundances. Then, one interesting question is if we regard theprotein inference problem as a special protein quantification problem, is itpossible to achieve better protein inference performance? Contribution: In this paper, we investigate the feasibility of using proteinquantification methods to solve the protein inference problem. Proteininference is to determine whether each candidate protein is present in thesample or not. Protein quantification is to calculate the abundance of eachprotein. Naturally, the absent proteins should have zero abundances. Thus, weargue that the protein inference problem can be viewed as a special case ofprotein quantification problem: present proteins are those proteins withnon-zero abundances. Based on this idea, our paper tries to use three verysimple protein quantification methods to solve the protein inference problemeffectively. Results: The experimental results on six datasets show that these threemethods are competitive with previous protein inference algorithms. Thisdemonstrates that it is plausible to take the protein inference problem as aspecial case of protein quantification, which opens the door of devising moreeffective protein inference algorithms from a quantification perspective.
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