Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra
peer-reviewed · Patterns · 2020
| Date | 2020-12-01 |
| Type | peer-reviewed |
| Venue | Patterns |
| Publisher | Elsevier BV |
| Contribution | adjacent |
| DOI | 10.1016/j.patter.2020.100137 |
| Citations (OpenAlex) | 4 |
Preprint version: Parallel factor analysis enables quantification and identification of highly-convolved data independent-acquired protein spectra (2020-04-23, bioRxiv)
Abstract
High-throughput data-independent acquisition (DIA) is the method of choice for quantitative proteomics, combining the best practices of targeted and shotgun approaches. The resultant DIA spectra are, however, highly convolved and with no direct precursor-fragment correspondence, complicating biological sample analysis. Here, we present CANDIA (canonical decomposition of data-independent-acquired spectra), a GPU-powered unsupervised multiway factor analysis framework that deconvolves multispectral scans to individual analyte spectra, chromatographic profiles, and sample abundances, using parallel factor analysis. The deconvolved spectra can be annotated with traditional database search engines or used as high-quality input for de novo sequencing methods. We demonstrate that spectral libraries generated with CANDIA substantially reduce the false discovery rate underlying the validation of spectral quantification. CANDIA covers up to 33 times more total ion current than library-based approaches, which typically use less than 5% of total recorded ions, thus allowing quantification and identification of signals from unexplored DIA spectra.
Methods and tools
- CANDIA: GPU-based parallel factor analysis that deconvolves DIA scans into individual analyte spectra; the deconvolved spectra give more high-confidence de novo sequences with DeepNovo and Novor than DIA-Umpire output.