FDR, Match-Between-Runs, and Immunopeptidomics: A Plain-Language Guide

What is FDR in proteomics?

The false discovery rate (FDR) is the estimated fraction of reported identifications that are incorrect. In proteomics, controlling FDR is how we ensure that peptide and protein identifications can be trusted.

When a search engine matches a fragmentation spectrum to a peptide sequence, it assigns a score to that match. Some matches are correct — the spectrum really came from that peptide. Others are incorrect — the peptide sequence happens to fit well by chance, or another peptide matches even better. The FDR is the proportion of reported matches that fall into the incorrect category.

By convention, proteomics results are reported at 1% FDR — meaning that at most 1 in 100 reported identifications is expected to be wrong. This threshold applies at multiple levels: peptide-spectrum match (PSM) level, peptide level, and protein level. Each requires its own FDR control.

Why FDR matters for comparing tools

A search engine that reports 20% more peptide identifications is only doing something meaningful if those extra identifications are real — if the FDR is still accurately controlled at 1%. A tool that loosens its FDR threshold, or uses a procedure that underestimates the true error rate, can appear to find more peptides while actually reporting more errors. This is why FDR methodology is one of the most important — and most scrutinized — aspects of proteomics software.

The key question: When a tool says "1% FDR," is the observed error rate actually no worse than 1%? The only way to verify this independently is with an entrapment experiment — searching against a database containing a foreign proteome that cannot legitimately be present, and counting how many identifications fall in that foreign set.

The target-decoy approach

The target-decoy approach is the standard method for estimating FDR in proteomics. It works by searching against a combined database of real (target) and fake (decoy) sequences, then using the decoy hit rate to estimate the error rate among target hits.

Decoy sequences are typically generated by reversing each protein sequence in the database. Because reversed sequences cannot be correct matches, any decoy sequence that scores above a given threshold is, by definition, a false positive — a real sequence that happened to score well by chance. Counting decoys above the threshold and dividing by targets above the threshold gives the FDR estimate.

When decoys are used for two jobs at once

Most modern search tools also use a second-stage classifier — Percolator, PeptideProphet — to rescore identifications using dozens of features. To train this classifier, they use decoy sequences as negative examples. This means the same decoys are doing two jobs: training the classifier and measuring the FDR.

This creates a subtle but real problem. A classifier trained to recognize decoys can learn the specific patterns of reversed sequences rather than genuine incorrectness. When those same decoys are then used to estimate the FDR, the estimate is no longer fully independent — and the reported error rate can be lower than the true rate. This failure mode has been studied in the FDR-control literature and is one of the motivations behind the Tesorai Search design.

The Tesorai approach: The Tesorai model is trained entirely on real peptide-spectrum matches — never on decoys. Decoys are reserved exclusively for FDR estimation after scoring, making the error estimate fully independent of the model. An entrapment analysis confirmed that the measured false discovery proportion stays at or below the claimed FDR across thresholds from 0.1% to 10%.

What is match-between-runs (MBR)?

Match-between-runs (MBR) is a technique that transfers peptide identifications from one LC-MS/MS run to another, based on matching precursor mass, charge, and retention time — without requiring a fragmentation spectrum in the receiving run.

In a typical quantitative experiment, the same peptide may be fragmented and identified in some runs but not others — because data-dependent acquisition (DDA) selects precursors stochastically. MBR fills these "missing values" by saying: if a peptide was confidently identified by MS/MS in run A, and its precursor mass and retention time match something in run B, transfer the identity to run B.

MBR was popularized in MaxQuant and is now standard in many pipelines. It genuinely reduces missing values and can increase the number of peptides reported per run by 30–50%. But it comes with an important caveat that is not always made explicit.

The catch: transferred identifications are not FDR-controlled

The FDR you controlled applies to identifications made by fragmentation. A transferred identification was never fragmented in the run it lands in — it is inferred from a mass-to-charge ratio and retention time match, and those matches can be wrong. A co-eluting peptide of similar mass can produce a false transfer that your search engine's reported FDR does not account for.

This is not hypothetical. A controlled two-proteome experiment by Lim, Paulo, and Gygi found that MBR raised identifications by roughly 40%, but 44% of transferred identifications in yeast-free samples were falsely transferred from yeast-containing runs. The encouraging news is that downstream quantification filtering removed most of these; the cautionary news is that the false-transfer rate before filtering is large and uncaptured by the standard reported FDR.

Tools like IonQuant have introduced explicit statistical methods to estimate and control the false-transfer rate separately from the standard FDR. This is the right direction — MBR done properly requires its own statistical apparatus. Tesorai Search currently reports only identifications backed by a fragmentation spectrum in that run, which means the 12–68% additional peptides it finds relative to other tools cannot be MBR artifacts.

What is immunopeptidomics?

Immunopeptidomics is the mass spectrometry-based study of peptides presented on the surface of cells by MHC (major histocompatibility complex) molecules — the peptides that the immune system uses to survey cellular contents for signs of disease.‍

Every nucleated cell in the body continuously digests its own proteins and presents fragments of them on its surface via MHC class I molecules. CD8+ T cells patrol these surfaces, and when they recognize a peptide as foreign — from a virus, a mutation, or a cancer-specific alteration — they mount an immune response. The collection of peptides presented by a cell at any given time is called the immunopeptidome.

Characterizing the immunopeptidome by mass spectrometry has become a cornerstone of cancer immunotherapy research. It enables identification of neoepitopes — peptides derived from tumor-specific mutations — that can be targeted by T cells or used to design personalized vaccines. It is also essential for understanding which viral peptides are presented during infection, relevant to vaccine design and antiviral therapy.

Why immunopeptidomics is analytically challenging

Immunopeptidomics is substantially harder than standard proteomics for several reasons. MHC-presented peptides are short (8–11 amino acids for MHC class I; 13–25 for class II) and do not arise from enzymatic digestion — so search engines must use unspecific cleavage, generating a far larger search space. The peptides are also present at very low copy numbers, requiring highly sensitive instruments and analysis software. Finally, the diversity of MHC alleles across individuals means that different patients present different peptide repertoires, requiring careful database construction.

Why identification depth matters especially here: In immunopeptidomics, every additional identified peptide is a potential neoepitope — a target for immunotherapy. A search engine that identifies 20% more peptides from the same raw data is not just a technical improvement; it may reveal therapeutic targets that would otherwise be missed entirely.

Tesorai Search and immunopeptidomics

Tesorai Search supports immunopeptidomics workflows natively, using unspecific enzymatic digestion with configurable peptide length ranges (typically 8–15 amino acids for MHC class I). In benchmarks on published immunopeptidomics datasets — including Bassani-Sternberg et al. and Nowatzky et al. — Tesorai identified 62% and 59% more peptides than MaxQuant, respectively, and outperformed FragPipe, PEAKS, and Proteome Discoverer at 1% FDR.

Putting it together: what to look for in a proteomics pipeline

The three concepts above — FDR control, match-between-runs, and immunopeptidomics-specific challenges — converge on a single practical question: how do you know whether to trust your search results?

A rigorous proteomics pipeline should be able to answer three questions clearly:

1. Is the FDR estimate independent? Were decoys used only for error estimation, or also for training the classifier? If the same decoys trained the model and measured the error, the FDR may be optimistic.
2. Are additional identifications from real spectra? If a tool reports more peptides, are they each backed by a fragmentation spectrum in that run — or are some transferred from other runs via MBR without their own FDR control?
3. Has the FDR been independently verified? Does the tool provide entrapment analysis results showing that the observed error rate matches the claimed rate? A stated 1% FDR is only meaningful if it has been empirically tested.