Version Compatibility

Reference examples tested with: pyOpenMS 3.1+, pandas 2.2+, numpy 1.26+

Before using code patterns, verify installed versions match. If versions differ:

Python: pip show <package> then help(module.function) to check signatures
R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Peptide Identification -- Confidence Is a Property of a Ranked List, Not a Single PSM

"Identify peptides from my MS/MS spectra" -> Match tandem mass spectra against a protein database, then control false discovery rate by target-decoy competition and act on a q-value -- because a raw match score is meaningless in isolation; only the list-level error rate is interpretable.

Python: pyopenms.SimpleSearchEngineAlgorithm().search(...) for in-process database search, FalseDiscoveryRate for q-values
CLI: comet, msfragger, sage, MSGFPlus for high-throughput database searching, percolator/mokapot for rescoring
R: mzID::mzID() + flatten() or mzR::openIDfile() + psms() to read mzIdentML search results

Scope: this skill owns spectrum-to-peptide matching and PSM/peptide-level FDR. Protein grouping and protein-level (picked) FDR -> protein-inference. PTM site localization and open-search mod discovery follow-up -> ptm-analysis. DIA peptide-centric extraction and scoring -> dia-analysis. FDR-filtered IDs feeding intensities -> quantification. mzML/raw loading -> data-import. OUT OF SCOPE: protein inference, PTM localization scoring, DIA peptide-centric pipelines, label-free/TMT quantification.

The Single Most Important Modern Insight -- A q-value Is a Verdict on the List, a Raw Score Is Not Even Comparable

Identification confidence is a property of a ranked LIST controlled by target-decoy competition, never a property of one PSM. The number to act on is a q-value (list-level) or PEP (per-PSM), NOT the engine's raw score. XCorr (Comet), hyperscore (MSFragger/X!Tandem), Andromeda score (MaxQuant), and SpecEValue (MS-GF+) live on different scales, are charge- and length-dependent, and are frequently not even monotone in true probability within a single engine -- which is exactly why rescoring (Percolator/mokapot) exists. "1% FDR" answers "what fraction of the list I keep is wrong," NOT "I am 99% sure of this one ID." The catastrophic error is thresholding on a raw score, or comparing scores across engines.
A q-value is valid only if (a) the decoy DB is a faithful null, (b) targets and decoys competed in ONE concatenated search, and (c) there are enough PSMs for the decoy count to be stable. Generate decoys at the PROTEIN level then digest (so decoy peptides obey the same enzyme rules), matching the target in size and composition. Concatenated competition gives FDR = (#decoys above threshold) / (#targets above threshold) -- one decoy above threshold estimates one false target. Separate target/decoy searches instead need either the simple Elias-Gygi 2x-decoy estimator FDR = 2 * #decoy / (#target + #decoy) or the more refined mix-max estimator (Keich, Kertesz-Farkas & Noble 2015) -- two distinct options for the separate-search setting, NOT the same formula. Mixing the concatenated and separate forms up is the most common silent FDR error.
PEP and q-value answer different questions; filtering at "PEP <= 0.01" is far stricter than "q <= 0.01." PEP (posterior error probability, local FDR) is the probability that THIS PSM is wrong; q-value is the FDR of the list cut at this PSM. FDR is the average of PEP over the accepted set (Kall 2008). The worst PSM in a 1%-FDR list typically has a PEP of 10-50%. Use q-value for list cutoffs; use PEP only for per-ID decisions (e.g. picking one PTM site). And PSM-FDR at 1% does NOT give 1% peptide-FDR or 1% protein-FDR -- each level needs its own estimation; hand protein-level control to protein-inference.

The FDR Vocabulary, Precisely

FDR: the expected proportion of false positives among ALL accepted items at a threshold -- a property of the whole list.
q-value: the minimum FDR at which a given PSM is still accepted; monotone after taking the running minimum from the bottom of the ranked list. Filter on q <= 0.01.
PEP (local FDR): the probability that THIS PSM is wrong given its score. Local, per-PSM; FDR is the integral of PEP over the accepted set (Kall 2008, "two sides of the same coin").
The estimator must match the search mode. Concatenated target-decoy competition (TDC): FDR = #decoy / #target (no factor 2 -- one best hit per spectrum already resolves the competition). Separate target and decoy searches: either the simple Elias-Gygi 2x-decoy estimator FDR = 2 * #decoy / (#target + #decoy), or the more refined mix-max estimator (Keich, Kertesz-Farkas & Noble 2015). Mix-max is a distinct, calibrated-score procedure for the separate-search setting -- it is NOT a rename of the 2x formula.

Tool Taxonomy

| Tool / method | Citation | Mechanism / role | When | |---|---|---|---| | Comet | Eng 2013 | XCorr + E-value; SEQUEST lineage, open-source | Robust default, TPP pipelines; pairs with Percolator | | X!Tandem | -- | hyperscore + refinement passes | Legacy/free; semi-tryptic refinement niche | | MS-GF+ | Kim & Pevzner 2014 | SpecEValue via generating-function DP | Calibrated cross-instrument E-value; ETD/CID, low-res, non-standard enzymes | | MaxQuant / Andromeda | Cox 2011 | binomial probability score; integrated MBR/LFQ/TMT | All-in-one quant pipeline (LFQ, TMT, SILAC); GUI | | MSFragger | Kong 2017 | hyperscore via fragment-ion indexing (~100x faster) | Open/mass-tolerant search, PTM discovery, huge datasets; core of FragPipe | | Sage | Lazear 2023 | hyperscore-style, Rust, rescoring-native | Modern scalable open-source pipelines; emits Percolator-ready features | | MetaMorpheus | Solntsev 2018 | calibration + G-PTM-D multinotch | PTM discovery with built-in calibration; proteoform-aware | | pFind 3 | Chi 2018 | open-search engine | Maximal unrestricted-PTM/mutation discovery | | Percolator | Kall 2007 | semi-supervised SVM re-rank on decoy negatives | Boost IDs at fixed FDR; non-tryptic/PTM/large search spaces | | mokapot | Fondrie & Noble 2021 | Percolator in Python; swappable XGBoost classifier | Python pipelines, Sage output, custom features | | MS2Rescore + DeepLC + MS2PIP | Declercq 2022; Bouwmeester 2021; Gabriels 2019 | predicted-RT + predicted-intensity rescoring features | Sharpen target/decoy separation; immunopeptidomics | | Spectral-library search | -- | match empirical reference spectra (intensity + RT) | Faster/more specific for known peptides -> spectral-libraries | | Protein grouping / protein FDR | Savitski 2015; The 2016 | picked / picked-group FDR | route OUT -> protein-inference | | PTM site localization | -- | per-site PEP, localization scoring | route OUT -> ptm-analysis |

Decision Tree by Scenario

| Scenario | Recommended | Why | |---|---|---| | Standard DDA, clean FDR, scriptable | Comet or Sage + Percolator/mokapot at q <= 0.01 | well-validated; rescoring boosts IDs at fixed FDR | | Cross-instrument / varied fragmentation / odd enzyme | MS-GF+ | SpecEValue is calibrated so a threshold means the same everywhere | | Discover unknown PTMs / mass shifts | MSFragger open search (-150..+500 Da) | fragment indexing makes wide-window search feasible; then closed search on discovered mods -> ptm-analysis | | Huge dataset, reproducible, cloud-scale | Sage (rescoring-native) | Rust speed; emits Percolator features directly | | All-in-one with quant in the same tool | MaxQuant/Andromeda | integrated LFQ/TMT/SILAC and MBR | | Non-tryptic (immunopeptidomics, degradomics) | any engine + Percolator/MS2Rescore | rescoring gains are largest where search space explodes | | Few PSMs (single-protein pulldown) | do NOT trust decoy FDR; inspect spectra manually | decoy counts too noisy below ~hundreds of PSMs | | Need per-site / per-ID confidence | act on PEP, not q-value | q-value is list-level; PEP is local |

Default when uncertain: concatenated target-decoy search with Comet or Sage, rescore with Percolator/mokapot, filter at q <= 0.01, and hand protein-level FDR to protein-inference.

Database Search with pyOpenMS

Goal: Match tandem mass spectra in an mzML file against a protein FASTA and produce scored PSMs as idXML.

Approach: SimpleSearchEngineAlgorithm actually scores spectra (the hand-rolled ProteaseDigestion loop only digests, it never matches a spectrum). The FASTA must already contain target + decoy sequences concatenated for downstream FDR; decoys carry a recognizable prefix.

from pyopenms import SimpleSearchEngineAlgorithm, IdXMLFile

protein_ids = []
peptide_ids = []
search = SimpleSearchEngineAlgorithm()
# spectra are scored against in-silico fragment ions of every candidate peptide
search.search('sample.mzML', 'human_target_decoy.fasta', protein_ids, peptide_ids)

# protein_ids FIRST in load/store -- the OpenMS argument order is fixed
IdXMLFile().store('search_results.idXML', protein_ids, peptide_ids)

Annotate Target/Decoy and Estimate FDR with pyOpenMS

Goal: Convert raw PSM scores into q-values and keep only PSMs at 1% FDR.

Approach: PeptideIndexing maps each PSM back to proteins and flags target vs decoy from the decoy prefix; FalseDiscoveryRate.apply runs the concatenated competition; IDFilter keeps q <= 0.01. This is the real pyOpenMS path -- not a hand-rolled decoy/target ratio of unknown provenance.

from pyopenms import PeptideIndexing, FalseDiscoveryRate, IDFilter, FASTAFile

fasta = []
FASTAFile().load('human_target_decoy.fasta', fasta)
indexer = PeptideIndexing()
params = indexer.getParameters()
params.setValue('decoy_string', 'DECOY_')      # must match the decoy prefix in the FASTA
params.setValue('decoy_string_position', 'prefix')
indexer.setParameters(params)
indexer.run(fasta, protein_ids, peptide_ids)   # sets target/decoy flags on every hit

FalseDiscoveryRate().apply(peptide_ids)         # concatenated competition -> per-PSM q-value as the new score
IDFilter().filterHitsByScore(peptide_ids, 0.01) # 0.01 = 1% FDR, the community list-level standard
IDFilter().removeDecoyHits(peptide_ids)

FDR from a Results Table (concatenated competition, made explicit)

Goal: Compute q-values from any engine's PSM table when the search was a single concatenated target-decoy search.

Approach: Rank by score, walk down accumulating target and decoy counts, FDR = decoys/targets, then take the running minimum from the bottom to get monotone q-values. The decoy/target form is correct ONLY for concatenated competition; separate searches need either the Elias-Gygi 2x-decoy form or the mix-max estimator (Keich, Kertesz-Farkas & Noble 2015).

import pandas as pd

psms = pd.read_csv('search_results.tsv', sep='\t')
psms['is_decoy'] = psms['protein'].str.startswith(('DECOY_', 'REV_', 'XXX_'))
psms = psms.sort_values('score', ascending=False).reset_index(drop=True)

# concatenated target-decoy competition: each decoy above threshold estimates one false target
targets = (~psms['is_decoy']).cumsum()
decoys = psms['is_decoy'].cumsum()
psms['fdr'] = decoys / targets
psms['qvalue'] = psms['fdr'][::-1].cummin()[::-1]   # running min from the bottom -> monotone q-values

kept = psms[(psms['qvalue'] <= 0.01) & (~psms['is_decoy'])]   # 1% list-level FDR

Per-Method Failure Modes

Concatenated vs separate FDR formula mismatch

Trigger: applying #decoy/#target to separately-searched targets and decoys, or 2*decoy/(target+decoy) to concatenated competition. Mechanism: the factor of 2 accounts for false hits that could land in either independent database; concatenated competition already resolves that by a single best hit per spectrum. Symptom: systematically under- or over-estimated FDR; irreproducible ID counts. Fix: confirm the search mode; concatenated -> #decoy/#target; separate -> Elias-Gygi 2x-decoy or the mix-max estimator (Keich, Kertesz-Farkas & Noble 2015). In Percolator, mix-max is the default for separate-search input and -Y/--post-processing-tdc selects target-decoy competition instead; concatenated input forces TDC automatically.

Thresholding on raw engine score

Trigger: filtering on XCorr/hyperscore/Andromeda score, or comparing scores from two engines. Mechanism: scores are uncalibrated, charge/length-dependent, and not monotone in true probability. Symptom: different cutoffs admit different real FDRs; cross-engine merges nonsensical. Fix: always convert to q-value (or SpecEValue/PEP) first; rescore with Percolator/mokapot.

Decoy FDR on too few PSMs

Trigger: reporting "0% FDR" from a single-protein pulldown or tiny PSM list. Mechanism: the decoy count is a noisy Poisson-like estimate; zero observed decoys does not mean zero false targets. Symptom: spuriously confident IDs from small experiments. Fix: below ~hundreds of PSMs, inspect spectra manually; do not act on the decoy q-value.

Open-search results used for clean FDR or quant

Trigger: taking IDs from a wide-window (-150..+500 Da) search as final, FDR-controlled results. Mechanism: wide windows admit "free" mass shifts that inflate random matches; the target-decoy null differs per mass-shift bin. Symptom: inflated, unreliable FDR on open-search output. Fix: treat open search as discovery; follow with a closed search restricted to the discovered mods -> ptm-analysis.

Rescoring overfitting

Trigger: custom features that leak label information, or training without proper cross-validation. Mechanism: the model learns the decoys, making rescored FDR optimistic. Symptom: ID counts jump but downstream validation fails. Fix: use Percolator/mokapot default cross-validation; predicted-feature rescoring (DeepLC/MS2PIP) is safer; validate with entrapment for high-stakes claims (Wen 2025).

DIA tool FDR taken at face value

Trigger: trusting a DIA tool's reported 1% peptide/protein FDR. Mechanism: entrapment shows several DIA tools do not reliably control FDR (Wen 2025). Symptom: real error rate exceeds the reported FDR. Fix: validate with entrapment for high-stakes DIA claims -> dia-analysis.

Quantitative Thresholds

| Threshold | Source | Rationale | |---|---|---| | Precursor tolerance 10-20 ppm (high-res Orbitrap) | -- | matches FT mass accuracy; tighter = fewer random candidates at fixed FDR | | Precursor tolerance -150..+500 Da (open search) | Kong 2017 | captures arbitrary PTM/mutation shifts; feasible only with fragment indexing | | Fragment tolerance 0.02 Da (HCD Orbitrap) / 0.6 Da (ion-trap CID) | -- | instrument-dependent; 0.6 Da on Orbitrap discards resolving power | | Missed cleavages 2 | -- | covers incomplete trypsin digestion without exploding search space | | PSM/peptide FDR 1% (q <= 0.01) | Elias & Gygi 2007 | community standard; list-level error, not per-PSM | | Decoy:target ratio 1:1 | Elias & Gygi 2007 | standard; unequal ratios need formula correction | | Min PSMs for trustworthy decoy FDR: hundreds+ | -- | below this the decoy count is too noisy | | Variable mods per peptide <= 2-3 | -- | each variable mod multiplies search space and random-match rate |

Common Errors

| Error / symptom | Cause | Solution | |---|---|---| | pyOpenMS "search" returns peptides but never scores spectra | used ProteaseDigestion, which only digests a FASTA | use SimpleSearchEngineAlgorithm().search(mzML, fasta, protein_ids, peptide_ids) | | IdXMLFile().load/store argument error | wrong order | protein_ids FIRST: IdXMLFile().load(path, protein_ids, peptide_ids) | | FDR ignores decoys / all q-values 0 | decoys not annotated before FalseDiscoveryRate | run PeptideIndexing with matching decoy_string first | | R: MSnbase::readMzIdData not found | that function name does not exist | use mzID::mzID(file) + flatten(), or mzR::openIDfile() + psms() (PSMatch/Spectra is the modern path) | | Percolator q-method mismatched to search mode | mix-max is the default for separate-search input | for separate searches, mix-max (default) or -Y/--post-processing-tdc for target-decoy competition; concatenated input forces TDC automatically; use --picked-protein for protein FDR | | 1% PSM FDR assumed to give 1% protein FDR | each level needs its own estimation | estimate protein-level (picked) FDR -> protein-inference | | "PEP <= 0.01" returns far fewer IDs than expected | PEP is per-PSM and far stricter than q-value | filter list cutoffs on q-value; reserve PEP for per-ID decisions |

References

Elias, J.E. & Gygi, S.P. 2007. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nature Methods 4(3):207-214.
Keich, U., Kertesz-Farkas, A. & Noble, W.S. 2015. Improved false discovery rate estimation procedure for shotgun proteomics. Journal of Proteome Research 14(8):3148-3161.
Kall, L., Canterbury, J.D., Weston, J., Noble, W.S. & MacCoss, M.J. 2007. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods 4(11):923-925.
Kall, L., Storey, J.D., MacCoss, M.J. & Noble, W.S. 2008. Posterior error probabilities and false discovery rates: two sides of the same coin. Journal of Proteome Research 7(1):40-44.
Eng, J.K., Jahan, T.A. & Hoopmann, M.R. 2013. Comet: an open-source MS/MS sequence database search tool. Proteomics 13(1):22-24.
Kim, S. & Pevzner, P.A. 2014. MS-GF+ makes progress towards a universal database search tool for proteomics. Nature Communications 5:5277.
Cox, J., Neuhauser, N., Michalski, A., Scheltema, R.A., Olsen, J.V. & Mann, M. 2011. Andromeda: a peptide search engine integrated into the MaxQuant environment. Journal of Proteome Research 10(4):1794-1805.
Kong, A.T., Leprevost, F.V., Avtonomov, D.M., Mellacheruvu, D. & Nesvizhskii, A.I. 2017. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nature Methods 14(5):513-520.
Lazear, M.R. 2023. Sage: an open-source tool for fast proteomics searching and quantification at scale. Journal of Proteome Research 22(11):3652-3659.
Solntsev, S.K., Shortreed, M.R., Frey, B.L. & Smith, L.M. 2018. Enhanced global post-translational modification discovery with MetaMorpheus. Journal of Proteome Research 17(5):1844-1851.
Chi, H., Liu, C., Yang, H. et al. 2018. Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine. Nature Biotechnology 36:1059-1061.
Fondrie, W.E. & Noble, W.S. 2021. mokapot: fast and flexible semisupervised learning for peptide detection. Journal of Proteome Research 20(4):1966-1971.
Bouwmeester, R., Gabriels, R., Hulstaert, N., Martens, L. & Degroeve, S. 2021. DeepLC can predict retention times for peptides that carry as-yet unseen modifications. Nature Methods 18:1363-1369.
Gabriels, R., Martens, L. & Degroeve, S. 2019. Updated MS2PIP web server delivers fast and accurate MS2 peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques. Nucleic Acids Research 47(W1):W295-W299.
Declercq, A., Bouwmeester, R., Hirschler, A., Carapito, C., Degroeve, S., Martens, L. & Gabriels, R. 2022. MS2Rescore: data-driven rescoring dramatically boosts immunopeptide identification rates. Molecular & Cellular Proteomics 21(8):100266.
Wen, B., Freestone, J., Riffle, M., MacCoss, M.J., Noble, W.S. & Keich, U. 2025. Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment. Nature Methods 22:1454-1463.

Related Skills

protein-inference - Group peptides to protein groups and control protein-level (picked) FDR
ptm-analysis - Open/variable-mod search follow-up and per-site PTM localization
dia-analysis - DIA peptide-centric extraction and scoring; entrapment FDR validation
quantification - FDR-filtered IDs feed label-free/TMT intensity quantification
spectral-libraries - Empirical and predicted spectral-library search as an ID alternative
data-import - Load mzML/raw MS data before identification
database-access/uniprot-access - Build the target FASTA (canonical vs isoform, contaminants)