Tandem mass spectrometry (MS/MS) followed by database search is the method of choice for protein identification in proteomic studies. Database searching methods employ spectral matching algorithms and statistical models to identify and quantify proteins in a sample. In general, these methods do not utilize any information other than spectral data for protein identification. However, considering the wealth of external data available for many biological systems, analysis methods can incorporate such information to improve the sensitivity of protein identification. In this study, we present a method to utilize Global Proteome Machine Database identification frequencies and RNA-seq transcript abundances to adjust the confidence scores of protein identifications. The method described is particularly useful for samples with low-to-moderate proteome coverage (i.e., <2000-3000 proteins), where we observe up to an 8% improvement in the number of proteins identified at a 1% false discovery rate.