To accurately determine the quantitative change of peptides and proteins in complex proteomics samples requires knowledge of how well each ion has been measured. The precision of each ions' calculated area is predicated on how uniquely it occupies its own space in m/z and elution time. Given an initial assumption that prior to the addition of the "heavy" label, all other ion detections are unique, which is arguably untrue, an initial attempt at quantifying the pervasiveness of ion interference events in a representative binary SILAC experiment was made by comparing the centered m/z and retention time of the ion detections from the "light" variant to its "heavy" companion. Ion interference rates were determined for LC-MS data acquired at mass resolving powers of 20 and 40 K with and without ion mobility separation activated. An ion interference event was recorded, if present in the companion dataset was an ion within ± its Δ mass at half-height, ±15 s of its apex retention time and if utilized by ±1 drift bin. Data are presented illustrating a definitive decrease in the frequency of ion interference events with each additional increase in selectivity of the analytical workflow. Regardless of whether the quantitative experiment is a composite of labeled samples or label free, how well each ion is measured can be determined given knowledge of the instruments mass resolving power across the entire m/z scale and the ion detection algorithm reporting both the centered m/z and Δ mass at half-height for each detected ion. Given these measurements, an effective resolution can be calculated and compared with the expected instrument performance value providing a purity score for the calculated ions' area based on mass resolution. Similarly, chromatographic and drift purity scores can be calculated. In these instances, the error associated to an ions' calculated peak area is estimated by examining the variation in each measured width to that of their respective experimental median. Detail will be disclosed as to how a final ion purity score was established, providing a first measure of how accurately each ions' area was determined as well as how precise the calculated quantitative change between labeled or unlabelled pairs were determined. Presented is how common ion interference events are in quantitative proteomics LC-MS experiments and how ion purity filters can be utilized to overcome and address them, providing ultimately more accurate and precise quantification results across a wider dynamic range.
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Evidence ID | Analyze ID | Gene/Complex | Systematic Name/Complex Accession | Qualifier | Gene Ontology Term ID | Gene Ontology Term | Aspect | Annotation Extension | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Phenotype | Experiment Type | Experiment Type Category | Mutant Information | Strain Background | Chemical | Details | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Disease Ontology Term | Disease Ontology Term ID | Qualifier | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Regulator | Regulator Systematic Name | Target | Target Systematic Name | Direction | Regulation of | Happens During | Regulator Type | Direction | Regulation Of | Happens During | Method | Evidence | Strain Background | Reference |
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Site | Modification | Modifier | Source | Reference |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Allele | Assay | Annotation | Action | Phenotype | SGA score | P-value | Source | Reference | Note |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Assay | Annotation | Action | Modification | Source | Reference | Note |
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Complement ID | Locus ID | Gene | Species | Gene ID | Strain background | Direction | Details | Source | Reference |
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Evidence ID | Analyze ID | Dataset | Description | Keywords | Number of Conditions | Reference |
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Evidence ID | Analyze ID | File | Description |
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