The study of post-translational methylation is hampered by the fact that large-scale LC-MS/MS experiments produce high methylpeptide false discovery rates (FDRs).1 The use of heavy-methyl SILAC can drastically reduce these FDRs; however this approach is limited by a lack of heavy-methyl SILAC compatible software. To fill this gap we recently developed MethylQuant. Here we describe an updated version of MethylQuant, which we have recently made available.2 We demonstrate its methylpeptide validation and quantification capabilities and provide guidelines for its best use. Using reference heavy-methyl SILAC datasets, we show that MethylQuant predicts with statistical significance the true or false positive status of methylpeptides in samples of varying complexity, degree of methylpeptide enrichment, and heavy to light mixing ratios. We introduce methylpeptide confidence indicators – MethylQuant Confidence and MethylQuant Score – and demonstrate their strong performace in complex samples characterized by a lack of methylpeptide enrichment. For these challenging datasets, MethylQuant identifies 882 of 1165 true positive methylpeptide spectrum matches (i.e. >75% sensitivity) at high specificity (<2% FDR), and achieves near-perfect specificity at 41% sensitivity. We also demonstrate that MethylQuant produces high accuracy relative quantification data that is tolerant of interference from co-eluting peptide ions. Together MethylQuant’s capabilities provide a path toward routine, accurate characterizations of the methylproteome using heavy-methyl SILAC.