A method for the comprehensive proteomic analysis of membrane proteins

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Semi-supervised learning for peptide identification from shotgun proteomics datasets (2007, methodology): “Peptides were separated using a 12-step MudPIT (multidimensional protein identification technology) program as described previously[9].”
Zinc-finger nucleases (2011, neutral): “Recent examples can be found in organelle proteomics on the mitochondria (Taylor et al. 2002, Lescuyer et al. 2003), on the golgi (Bell et al. 2001) or on the peroxisome (Yi et al. 2002).”
Identifying biological themes within lists of genes with EASE (2003, positive): “The predicted 454 transmembrane proteins in a proteomics study by Wu et al. [25] were analyzed with the categorical over-representation function of EASE for enriched themes with respect to the total of 1,610 proteins identified in a crude brain homogenate.”
Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry* (2009, neutral): “Protein Identification False Discovery Rates for”