Speeding Up Percolator

Halloran, John T.; Zhang, Hantian; Kara, Kaan; Renggli, Cédric; The, Matthew; Zhang, Ce; Rocke, David M.; Käll, Lukas; Noble, William Stafford

doi:10.1021/acs.jproteome.9b00288

Cited by 8 publications

(9 citation statements)

References 37 publications

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“…3) ( 34 ) with a concatenated decoy search against a Felis catus proteome database (Swiss-Prot and TrEMBL) retrieved on August 13, 2021 from http://uniprot.org ( 35 ). The database search results were postprocessed using Percolator (Crux version 4.0) ( 36 ) and peptides identified at 5% false discovery rate (FDR) were accepted for quantification. TMT reporter ion intensities were extracted using an R script ( 37 ) and differential expression analysis performed with the aid of limma v.3.49.0 in R ( 38 ).…”

Section: Methodsmentioning

confidence: 99%

Sex-specific responses to slow progressive pressure overload in a large animal model of HFpEF

Eaton

Berretta

Lynch

et al. 2022

American Journal of Physiology-Heart and Circulatory Physiology

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Approximately 50% of all heart failure (HF) diagnoses can be classified as HF with preserved ejection fraction (HFpEF). HFpEF is more prevalent in females compared to males, but the underlying mechanisms are unknown. We previously showed that pressure overload (PO) in male felines induces a cardiopulmonary phenotype with essential features of human HFpEF. The goal of this study was to determine if slow progressive PO induces distinct cardiopulmonary phenotypes in females and males in the absence of other pathological stressors. Female and male felines underwent aortic constriction (banding) or sham surgery after baseline echocardiography, pulmonary function testing, and blood sampling. These assessments were repeated at 2- and 4-months post-surgery to document the effects of slow progressive pressure overload. At 4-months, invasive hemodynamic studies were also performed. Left ventricle (LV) tissue was collected for histology, myofibril mechanics, extracellular matrix (ECM) mass spectrometry, and single nucleus RNA sequencing (snRNAseq). The induced pressure overload (PO) was not different between sexes. PO also induced comparable changes in LV wall thickness and myocyte cross sectional area in both sexes. Both sexes had preserved ejection fraction, but males had a slightly more robust phenotype in hemodynamic and pulmonary parameters. There was no difference in LV fibrosis and ECM composition between banded male and female animals. LV snRNAseq revealed changes in gene programs of individual cell types unique to males and females after PO. Based on these results, both sexes develop cardiopulmonary dysfunction but the phenotype is somewhat less advanced in females.

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Section: Methodsmentioning

confidence: 99%

Sex-specific responses to slow progressive pressure overload in a large animal model of HFpEF

Eaton

Berretta

Lynch

et al. 2022

American Journal of Physiology-Heart and Circulatory Physiology

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“…OpenSWATH [ 27,88] http://www.openswath.org/en/latest/ EncyclopeDIA [67] https://bitbucket.org/searleb/encyclopedia/wiki/Home Scaffold DIA [67] http://www.proteomesoftware.com/products/dia Skyline [55] https://skyline.ms Spector [66] https://github.com/rpeckner-broad/Specter Percolator [89] https://github.com/percolator/percolator/ PyProphet [52] https://github.com/PyProphet/pyprophet/issues SWATHProphet [90] https://sourceforge.net/projects/sashimi/files/SWATHProphet…”

Section: Library-basedmentioning

confidence: 99%

Data‐Independent Acquisition Mass Spectrometry‐Based Proteomics and Software Tools: A Glimpse in 2020

et al. 2020

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This review provides a brief overview of the development of data‐independent acquisition (DIA) mass spectrometry‐based proteomics and selected DIA data analysis tools. Various DIA acquisition schemes for proteomics are summarized first including Shotgun‐CID, DIA, MSE, PAcIFIC, AIF, SWATH, MSX, SONAR, WiSIM, BoxCar, Scanning SWATH, diaPASEF, and PulseDIA, as well as the mass spectrometers enabling these methods. Next, the software tools for DIA data analysis are classified into three groups: library‐based tools, library‐free tools, and statistical validation tools. The approaches are reviewed for generating spectral libraries for six selected library‐based DIA data analysis software tools which are tested by the authors, including OpenSWATH, Spectronaut, Skyline, PeakView, DIA‐NN, and EncyclopeDIA. An increasing number of library‐free DIA data analysis tools are developed including DIA‐Umpire, Group‐DIA, PECAN, PEAKS, which facilitate identification of novel proteoforms. The authors share their user experience of when to use DIA‐MS, and several selected DIA data analysis software tools. Finally, the state of the art DIA mass spectrometry and software tools, and the authors’ views of future directions are summarized.

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“…The second group adopts tool-dependent and data-dependent models to overcome the bias of pre-trained models. The tools in this group are gradient boosting-based Scavager [ 6 ], and support vector machine-based Percolator-related tools (Percolator [ 8 ], MS-GF + Percolator [ 12 ], Mascot Percolator [ 17 ], X!Tandem Percolator [ 10 , 11 ], OMSSA Percolator [ 9 ], speed-up version of Percolator [ 18 ]) and Qranker [ 13 ]. Among them, the most widely used tool is Percolator.…”

Section: Introductionmentioning

confidence: 99%

TIDD: tool-independent and data-dependent machine learning for peptide identification

Hwang

et al. 2022

BMC Bioinformatics

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Background In shotgun proteomics, database search engines have been developed to assign peptides to tandem mass (MS/MS) spectra and at the same time post-processing (or rescoring) approaches over the search results have been proposed to increase the number of confident peptide identifications. The most popular post-processing approaches such as Percolator and PeptideProphet have improved rates of peptide identifications by combining multiple scores from database search engines while applying machine learning techniques. Existing post-processing approaches, however, are limited when dealing with results from new search engines because their features for machine learning must be optimized specifically for each search engine. Results We propose a universal post-processing tool, called TIDD, which supports confident peptide identifications regardless of the search engine adopted. TIDD can work for any (including newly developed) search engines because it calculates universal features that assess peptide-spectrum match quality while it allows additional features provided by search engines (or users) as well. Even though it relies on universal features independent of search tools, TIDD showed similar or better performance than Percolator in terms of peptide identification. TIDD identified 10.23–38.95% more PSMs than target-decoy estimation for MSFragger, which is not supported by Percolator. TIDD offers an easy-to-use simple graphical user interface for user convenience. Conclusions TIDD successfully eliminated the requirement for an optimal feature engineering per database search tool, and thus, can be applied directly to any database search results including newly developed ones.

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Speeding Up Percolator

Cited by 8 publications

References 37 publications

Sex-specific responses to slow progressive pressure overload in a large animal model of HFpEF

Sex-specific responses to slow progressive pressure overload in a large animal model of HFpEF

Data‐Independent Acquisition Mass Spectrometry‐Based Proteomics and Software Tools: A Glimpse in 2020

TIDD: tool-independent and data-dependent machine learning for peptide identification

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