Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups

Abstract

Estimating false discovery rates (FDRs) of protein identification continues to be an important topic in mass spectrometry–based proteomics, particularly when analyzing very large datasets. One performant method for this purpose is the Picked Protein FDR approach which is based on a target-decoy competition strategy on the protein level that ensures that FDRs scale to large datasets. Here, we present an extension to this method that can also deal with protein groups, that is, proteins that share common peptides such as protein isoforms of the same gene. To obtain well-calibrated FDR estimates that preserve protein identification sensitivity, we introduce two novel ideas. First, the picked group target-decoy and second, the rescued subset grouping strategies. Using entrapment searches and simulated data for validation, we demonstrate that the new Picked Protein Group FDR method produces accurate protein group-level FDR estimates regardless of the size of the data set. The validation analysis also uncovered that applying the commonly used Occam’s razor principle leads to anticonservative FDR estimates for large datasets. This is not the case for the Picked Protein Group FDR method. Reanalysis of deep proteomes of 29 human tissues showed that the new method identified up to 4% more protein groups than MaxQuant. Applying the method to the reanalysis of the entire human section of ProteomicsDB led to the identification of 18,000 protein groups at 1% protein group-level FDR. The analysis also showed that about 1250 genes were represented by ≥2 identified protein groups. To make the method accessible to the proteomics community, we provide a software tool including a graphical user interface that enables merging results from multiple MaxQuant searches into a single list of identified and quantified protein groups.

Graphical Abstract

Highlights

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  Evaluating protein group FDR estimation methods with entrapment and simulated data.

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  Accurate & sensitive protein group FDR method on databases with protein isoforms.

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  Tool for combining multiple large-scale MaxQuant searches on protein group-level.

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  Analysis on ProteomicsDB identified >1200 human genes with multiple protein groups.

In Brief

Distinguishing between protein products of a gene is complicated by many peptides that such isoforms have in common. Grouping indistinguishable proteins alleviates this issue but leads to problems with estimating false discovery rates (FDRs) in large-scale experiments as false positives accumulate. Here, protein group FDR estimation methods were evaluated on accuracy and sensitivity. Our new Picked Protein Group FDR method performed best and reanalysis of the draft human proteome in ProteomicsDB found >1200 genes with multiple protein products.