Correction to “Analyzing the First Drafts of the Human Proteome”

Correction to “Analyzing the First Drafts of the Human Proteome

Analyzing the First Drafts of the Human Proteome

This letter analyzes two large-scale proteomics studies published in the same issue of <i>Nature</i>. At the time of the release, both studies were portrayed as draft maps of the human proteome and great advances in the field. As with the initial publication of the human genome, these papers have broad appeal and will no doubt lead to a great deal of further analysis by the scientific community. However, we were intrigued by the number of protein-coding genes detected by the two studies, numbers that far exceeded what has been reported for the multinational Human Proteome Project effort. We carried out a simple quality test on the data using the olfactory receptor family. A high-quality proteomics experiment that does not specifically analyze nasal tissues should not expect to detect many peptides for olfactory receptors. Neither of the studies carried out experiments on nasal tissues, yet we found peptide evidence for more than 100 olfactory receptors in the two studies. These results suggest that the two studies are substantially overestimating the number of protein coding genes they identify. We conclude that the experimental data from these two studies should be used with caution

Most Highly Expressed Protein-Coding Genes Have a Single Dominant Isoform

Although eukaryotic cells express a wide range of alternatively spliced transcripts, it is not clear whether genes tend to express a range of transcripts simultaneously across cells, or produce dominant isoforms in a manner that is either tissue-specific or regardless of tissue. To date, large-scale investigations into the pattern of transcript expression across distinct tissues have produced contradictory results. Here, we attempt to determine whether genes express a dominant splice variant at the protein level. We interrogate peptides from eight large-scale human proteomics experiments and databases and find that there is a single dominant protein isoform, irrespective of tissue or cell type, for the vast majority of the protein-coding genes in these experiments, in partial agreement with the conclusions from the most recent large-scale RNAseq study. Remarkably, the dominant isoforms from the experimental proteomics analyses coincided overwhelmingly with the reference isoforms selected by two completely orthogonal sources, the consensus coding sequence variants, which are agreed upon by separate manual genome curation teams, and the principal isoforms from the APPRIS database, predicted automatically from the conservation of protein sequence, structure, and function

General Statistical Framework for Quantitative Proteomics by Stable Isotope Labeling

The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large data sets generated by these techniques remains a challenge because appropriate statistical standards are lacking. Here, we present a generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including ¹⁸O, iTRAQ, and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide, and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was at least similar than that of other existing methods. The model also provides a general framework to integrate quantitative and error information fully, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H₂O₂ concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data