Proteome Discoverer—A Community Enhanced Data Processing Suite for Protein Informatics

Orsburn, Benjamin C.

doi:10.3390/proteomes9010015

Cited by 167 publications

(118 citation statements)

References 77 publications

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“…The spectra used in this evaluation were from 12 instrument runs: six from a 2 ng sample which approximates 10 cells and six from a 0.2 ng sample which approximates 1 cell (see Methods). We benchmark 5 commonly used peptide identification tools: MetaMorpheus 8 , MsFragger 9 , MSGF+ 10 , MaxQuant 11 , and Proteome Discoverer 12 .…”

Section: Resultsmentioning

confidence: 99%

Benchmarking PSM identification tools for single cell proteomics

Watt

Boekweg

Truong

et al. 2021

Preprint

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Single cell proteomics is an emerging sub-field within proteomics with the potential to revolutionize our understanding of cellular heterogeneity and interactions. Recent efforts have largely focused on technological advancements in sample preparation, chromatography and instrumentation to enable measuring proteins present in these ultra-limited samples. Although advancements in data acquisition have rapidly improved our ability to analyze single cells, the software pipelines used in data analysis were originally written for traditional bulk samples and their performance on single cell data has not been investigated. We benchmarked five popular peptide identification tools on single cell proteomics data. We found that MetaMorpheus achieved the greatest number of peptide spectrum matches at a 1% false discovery rate. Depending on the tool, we also find that post processing machine learning can improve spectrum identification results by up to ~40%. Although rescoring leads to a greater number of peptide spectrum matches, these new results typically are generated by 3rd party tools and have no way of being utilized by the primary pipeline for quantification. Exploration of novel metrics for machine learning algorithms will continue to improve performance.

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Section: Resultsmentioning

confidence: 99%

Benchmarking PSM identification tools for single cell proteomics

Watt

Boekweg

Truong

et al. 2021

Preprint

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“…In versions 1.0–2.0, the protein group is assigned to the protein with the highest total percentage coverage. In versions 2.1 onward, the largest protein in a group bearing equivalent evidence to all other proteins is assigned [ 17 ]. A recent analysis of 22 search engines demonstrated that resulting protein lists change little from the same MS/MS evidence [ 18 ], and therefore the two will be considered as equivalent in this light reanalysis.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric

Orsburn

2021

Proteomes

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Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of a lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single-shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to date, we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single- and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.

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“…There are many software platforms that enable MS/MS-based de novo and/or DB search enginebased identification (Box 4; e.g., MaxQuant [95], PEAKS [96], MS-GF+ [97], MSFragger [98], and Proteome Discoverer [99]). However, only a few computational MS tools are currently available that have been specifically designed for antibody proteomics data (e.g., PASA [100] and PEAKS AB 2.0 [101]).…”

Section: Computational Considerations For Ab-seqmentioning

confidence: 99%

Progress and challenges in mass spectrometry-based analysis of antibody repertoires

Snapkov

Chernigovskaya

Sinitcyn

et al. 2022

Trends in Biotechnology

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Humoral immunity is divided into the cellular B cell and protein-level antibody responses. High-throughput sequencing has advanced our understanding of both these fundamental aspects of B cell immunology as well as aspects pertaining to vaccine and therapeutics biotechnology. Although the protein-level serum and mucosal antibody repertoire make major contributions to humoral protection, the sequence composition and dynamics of antibody repertoires remain underexplored. This limits insight into important immunological and biotechnological parameters such as the number of antigen-specific antibodies, which are for example, relevant for pathogen neutralization, microbiota regulation, severity of autoimmunity, and therapeutic efficacy. High-resolution mass spectrometry (MS) has allowed initial insights into the antibody repertoire. We outline current challenges in MS-based sequence analysis of antibody repertoires and propose strategies for their resolution.

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Proteome Discoverer—A Community Enhanced Data Processing Suite for Protein Informatics

Cited by 167 publications

References 77 publications

Benchmarking PSM identification tools for single cell proteomics

Benchmarking PSM identification tools for single cell proteomics

Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric

Progress and challenges in mass spectrometry-based analysis of antibody repertoires

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