MSFragger is a novel peptide identification tool that significantly improves the speed and comprehensiveness of shotgun proteomics analysis. It introduces a fragment-ion indexing method, enabling over 100-fold faster speed compared to existing tools. MSFragger supports open database searches, allowing the identification of peptides and their modified forms, which is crucial for understanding post-translational modifications (PTMs) and uncovering differences in modification rates across samples. The tool is demonstrated to be effective in large-scale proteomic datasets, including protein-RNA crosslinked peptide data and affinity purification experiments, where it increases the number of identified spectra by up to 300%. MSFragger also addresses the issue of false discovery rate (FDR) estimation in traditional narrow window searches, which can be inaccurate due to unaccounted peptide modifications. The tool is platform-independent and can be integrated into existing data analysis pipelines, making it a valuable resource for comprehensive proteomics studies.MSFragger is a novel peptide identification tool that significantly improves the speed and comprehensiveness of shotgun proteomics analysis. It introduces a fragment-ion indexing method, enabling over 100-fold faster speed compared to existing tools. MSFragger supports open database searches, allowing the identification of peptides and their modified forms, which is crucial for understanding post-translational modifications (PTMs) and uncovering differences in modification rates across samples. The tool is demonstrated to be effective in large-scale proteomic datasets, including protein-RNA crosslinked peptide data and affinity purification experiments, where it increases the number of identified spectra by up to 300%. MSFragger also addresses the issue of false discovery rate (FDR) estimation in traditional narrow window searches, which can be inaccurate due to unaccounted peptide modifications. The tool is platform-independent and can be integrated into existing data analysis pipelines, making it a valuable resource for comprehensive proteomics studies.