  Mass spectrometry-based proteomics has become the tool of choice for
identifying and quantifying the proteome of an organism. Though recent years
have seen a tremendous improvement in instrument performance and the
computational tools used, significant challenges remain, and there are many
opportunities for statisticians to make important contributions. In the most
widely used "bottom-up" approach to proteomics, complex mixtures of proteins
are first subjected to enzymatic cleavage, the resulting peptide products are
separated based on chemical or physical properties and analyzed using a mass
spectrometer. The two fundamental challenges in the analysis of bottom-up
MS-based proteomics are as follows: (1) Identifying the proteins that are
present in a sample, and (2) Quantifying the abundance levels of the identified
proteins. Both of these challenges require knowledge of the biological and
technological context that gives rise to observed data, as well as the
application of sound statistical principles for estimation and inference. We
present an overview of bottom-up proteomics and outline the key statistical
issues that arise in protein identification and quantification.
