Literature DB >> 29400476

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

Michael R Hoopmann1, Jason M Winget1, Luis Mendoza1, Robert L Moritz1.   

Abstract

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.

Entities:  

Keywords:  automation; data analysis pipeline; label-free quantification; open-source software; quantitative proteomics; trans-proteomic pipeline

Mesh:

Year:  2018        PMID: 29400476      PMCID: PMC5891225          DOI: 10.1021/acs.jproteome.7b00786

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  25 in total

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Journal:  J Proteome Res       Date:  2016-09-06       Impact factor: 4.466

Review 7.  Thousand and one ways to quantify and compare protein abundances in label-free bottom-up proteomics.

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Journal:  Biochim Biophys Acta       Date:  2016-03-03

8.  Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset.

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Journal:  J Proteomics       Date:  2015-11-14       Impact factor: 4.044

9.  MS-GF+ makes progress towards a universal database search tool for proteomics.

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Journal:  Nat Commun       Date:  2014-10-31       Impact factor: 14.919

10.  Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.

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Journal:  Mol Cell Proteomics       Date:  2014-06-17       Impact factor: 5.911

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