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StPeter: Seamless Label-Free Quantification with the Trans-Proteomic Pipeline.

Michael R HoopmannJason M WingetLuis MendozaRobert L Moritz
Published in: Journal of proteome research (2018)
Label-free quantification has grown in popularity as a means of obtaining relative abundance measures for proteomics experiments. However, easily accessible and integrated tools to perform label-free quantification have been lacking. We describe StPeter, an implementation of Normalized Spectral Index quantification for wide availability through integration into the widely used Trans-Proteomic Pipeline. This implementation has been specifically designed for reproducibility and ease of use. We demonstrate that StPeter outperforms other state-of-the art packages using a recently reported benchmark data set over the range of false discovery rates relevant to shotgun proteomics results. We also demonstrate that the software is computationally efficient and supports data from a variety of instrument platforms and experimental designs. Results can be viewed within the Trans-Proteomic Pipeline graphical user interfaces and exported in standard formats for downstream statistical analysis. By integrating StPeter into the freely available Trans-Proteomic Pipeline, users can now obtain high-quality label-free quantification of any data set in seconds by adding a single command to the workflow.
Keyphrases
  • label free
  • electronic health record
  • healthcare
  • primary care
  • big data
  • quality improvement
  • computed tomography
  • high throughput
  • machine learning
  • microbial community