Protein kinases are integral to cellular signaling and their dysfunction can lead to the development of cancer. Breast cancer is a heterogeneous disease that is responsible for many deaths each year where abnormalities in kinases have been observed. Many studies focus on a select few kinases and the interaction of all 518 kinases is not well understood. Wide-scale molecular profiling of kinases, including signature-finding and network reconstruction, is a necessary step towards understanding how these genes get deregulated and progress in disease. High-throughput analysis techniques were applied to quantify the expression of protein kinases in various types of breast cancer including: cell lines, mouse models, and a patient tumor. Using RNA-seq, we analyzed the expression of over 70% of kinases in each breast cancer cell type. An emerging technique that combines the use of multiplexed inhibitor beads with mass spectrometry (MIB/MS) was used to measure kinase activity changes upon treatment with kinase inhibitors. MIB/MS found 40-50% of the RNA-seq expressed kinases to be active. Statistical pattern recognition and multivariate methods were tested to find possible kinase activity signatures for breast cancer subtypes. We also constructed simple networks for triple negative breast cancer (TNBC) samples using correlation calculations of MIB/MS data across drug treatments. We found that many of the kinase outliers and nodes overlapped with a previously determined kinase signature for a reprogrammed TNBC kinome, but the lack of experimental replicates disallows us from making statistically significant predictions from the data. Dealing with high-throughput experiments made us realize that organization of the generated data is another important challenge. Data should be made openly available because someone else may find unique characteristics or results that were initially missed. We developed Kinome DB, a protein kinase website that compiles information directly related to kinase experiments. While we were unable to draw concrete conclusions regarding kinase activity signatures, we tested the viability of several methods to evaluate MIB/MS data and characterized the breast cancer kinase transcriptome using RNA-seq. These are necessary steps for determining new kinase drug targets and rational combination therapies for treating breast cancer.