Statistical validation of peptide assignments from a large-scale shotgun proteomics experiment is a critical step, and various methods for evaluating significance based on decoy database search are in practice. False discovery rate (FDR) estimation of peptide assignments assesses global significance and corrects for multiple comparisons. Various approaches have been proposed for FDR estimation but unavailability of standard tools or libraries leads to development of many in-house scripts followed by manual steps that are error-prone and low-throughput. The ProteoStats library provides an open-source framework for developers with many FDR estimation and visualization features for several popular search algorithms. It also provides accurate q-values, which can be easily integrated in any proteomics pipeline to provide automated, accurate, high-throughput statistical validation and minimize manual errors.