FDR Calculation for Mass Spectrometry
False discovery rate is a key quality measure in proteomics. It estimates how many accepted identifications may be incorrect. Mass spectrometry searches usually compare spectra against target and decoy sequences. Target matches represent possible real peptides. Decoy matches represent controlled false hits. Their ratio gives a practical error estimate.
This calculator helps review that estimate before reporting peptide, spectrum, or protein discoveries. It accepts direct target and decoy counts. It also accepts optional scored rows. You can choose whether higher scores or lower scores are better. The tool then filters accepted matches at your threshold and compares the observed FDR with your desired limit.
Target decoy analysis is useful because search engines can produce confident looking random matches. A decoy database is designed to behave like the real database, but it should not contain real analytes. When decoy matches pass the same threshold as targets, they reveal how often false matches survive filtering. The correction factor lets you adjust for single, paired, or custom decoy designs.
Use the target denominator method for common target decoy reporting. Use the total accepted method when your workflow defines FDR against all accepted records. Use the stabilized method for very small experiments, where one extra decoy can change the percentage strongly. Always match the method to your lab protocol.
The optional scored list is helpful for threshold tuning. Paste one row per match with a score and label. The calculator sorts the rows, builds cumulative target and decoy counts, and finds a threshold that reaches the selected FDR goal while keeping many targets. This is useful during method development or when comparing search settings.
FDR is an estimate, not a guarantee for each individual peptide. Good reporting still needs proper database choice, mass tolerance control, enzyme settings, modification review, and replicate checks. Protein level FDR can differ from PSM level FDR because protein inference groups many peptides together. For publication, record the level, formula, threshold, database design, and software settings beside the final percentage. Clear records make the analysis easier to audit and reproduce.
When exporting results, include the example table and your calculated table together. This gives reviewers context. It also helps teams compare batches, instruments, digestion runs, and database search updates later.