Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry

Reiter, Lukas; Claassen, Manfred; Schrimpf, Sabine; Jovanović, Marko; Schmidt, Alexander; Buhmann, Joachim M.; Hengartner, Michael O.; Aebersold, Ruedi

doi:10.1074/mcp.m900317-mcp200

Cited by 297 publications

(326 citation statements)

References 42 publications

(61 reference statements)

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“…Protein inference was performed using in-modified IsoformResolver software aiming for consistent protein assignment of peptides across experiments (19). False discovery rates (FDR) for peptide and protein identifications (threshold at 5%) were established using Mayu software (20). FDR of protein identification was based on "unique" (assigned to a single globally inferred protein) spectra exclusively.…”

Section: Methodsmentioning

confidence: 99%

Strain Differences in Presynaptic Function

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Rotaru

et al. 2015

Journal of Biological Chemistry

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Background:The inbred mouse strain DBA/2J shows impaired hippocampal memory formation, which has been attributed to postsynaptic changes.Results: DBA/2J shows reduced expression of exocytosis proteins, paired-pulse facilitation, and number of synaptic vesicles. Conclusion: Proteomic, ultrastructural, and physiological investigations suggest a deficit in presynaptic function in DBA/2J. Significance: This is the first study describing the DBA/2J hippocampal proteome and ultrastructure.

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Section: Methodsmentioning

confidence: 99%

Strain Differences in Presynaptic Function

Lenselink

Rotaru

et al. 2015

Journal of Biological Chemistry

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“…False discovery rate (FDR) was estimated by searching the data against a database consisting of both forward and reversed sequences and set to Ͻ1% at the protein level using MAYU (27). Peptides corresponding to a Ͻ1% protein FDR rate were used in the calculations of quantities.…”

Section: Methodsmentioning

confidence: 99%

Defining, Comparing, and Improving iTRAQ Quantification in Mass Spectrometry Proteomics Data

Hultin‐Rosenberg

Forshed

Branca

et al. 2013

Molecular & Cellular Proteomics

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The purpose of this study was to generate a basis for the decision of what protein quantities are reliable and find a way for accurate and precise protein quantification. To investigate this we have used thousands of peptide measurements to estimate variance and bias for quantification by iTRAQ (isobaric tags for relative and absolute quantification) mass spectrometry in complex human samples. A549 cell lysate was mixed in the proportions 2:2:1:1:2:2:1:1, fractionated by high resolution isoelectric focusing and liquid chromatography and analyzed by three mass spectrometry platforms; LTQ Orbitrap Velos, 4800 MALDI-TOF/TOF and 6530 Q-TOF. We have investigated how variance and bias in the iTRAQ reporter ions data are affected by common experimental variables such as sample amount, sample fractionation, fragmentation energy, and instrument platform. Based on this, we have suggested a concept for experimental design and a methodology for protein quantification. By using duplicate samples in each run, each experiment is validated based on its internal experimental variation. The duplicates are used for calculating peptide weights, unique to the experiment, which is used in the protein quantification. By weighting the peptides depending on reporter ion intensity, we can decrease the relative error in quantification at the protein level and assign a total weight to each protein that reflects the protein quantitation confidence. We also demonstrate the usability of this methodology in a cancer cell line experiment as well as in a clinical data set of lung cancer tissue samples. In conclusion, we have in this study developed a methodology for improved protein quantification in shotgun proteomics and introduced a way to assess quantification for proteins with few peptides. The experimental design and developed algorithms decreased the relative protein quantification error in the analysis of complex biological samples. Recent developments in methods and instruments for mass spectrometry enable quantitative proteomics analysis of complex samples with good coverage (1-4). Several techniques for quantification by mass spectrometry exist, both using isotopic labeling and label free methods (5, 6). Quantification by isotopic labeling can be done on precursor ion level or by quantifying isobaric label fragments in fragment spectra. Isotope-coded affinity tag (7), isobaric tags for relative and absolute quantification (iTRAQ) 1 (8), and stable isotope labeling by amino acids in cell culture (SILAC) (9) are among the most commonly used labeling methods based on stable isotopes. iTRAQ allows for simultaneous relative quantification of up to eight samples within a single run. Quantification by mass spectrometry is however a challenge, and several factors contribute to the uncertainty in the quantitative estimate; differences in labeling efficiency, protein digestion, precursor mixing, ion suppression, peak detection, data preprocessing, and data analysis (10). The quality of quantitation methods can be measured in terms of precision ...

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“…Protein inference was performed using IsoformResolver software (Meyer-Arendt et al, 2011) aiming for consistent protein assignment of peptides across experiments. False discovery rates (FDRs) for peptide and protein identification were established using Mayu (Reiter et al, 2009). Peptide-spectrum matches were truncated at 5% FDR and protein identifications were truncated at 1% FDR.…”

Section: Nano-lc Ms/msmentioning

confidence: 99%