Philosopher: a versatile toolkit for shotgun proteomics data analysis

Leprevost, Felipe da Veiga; Haynes, Sarah E.; Avtonomov, Dmitry M.; Chang, Hui‐Chen; Shanmugam, Avinash Kumar; Mellacheruvu, Dattatreya; Kong, Andy T.; Nesvizhskii, Alexey I.

doi:10.1038/s41592-020-0912-y

Cited by 387 publications

(309 citation statements)

References 10 publications

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“…For each analysis, the MS/MS search results were further processed using the Philosopher toolkit (da Veiga Leprevost et al, 2020). First, MSFragger output files (in pepXML format) were processed using PeptideProphet (Keller et al, 2002) (with the high-mass accuracy binning and semi-parametric mixture modeling options) to compute the posterior probability of correct identification for each peptide to spectrum match (PSM).…”

Section: Spectral Library Generation In Fragpipementioning

confidence: 99%

“…Moreover, we have optimized the generation of spectral libraries from offline-fractionated PASEF (DDA) data using FragPipe computational platform. FragPipe provides fast, sensitive, and fully automated solution for the analysis of DDA data, from peptide identification with MSFragger search engine (Kong et al, 2017;Yu et al, 2020a), to peptide validation, protein inference and false discovery rate (FDR) based filtering using Philosopher (da Veiga Leprevost et al, 2020), to effective nonlinear retention time and ion mobility alignment between different DDA runs and generation of the final spectral library with EasyPQP. We show that the analysis of dia-PASEF data with this extended DIA-NN version and FragPipe-generated libraries increases the proteomic depth of existing raw data by up to 69%, while simultaneously increasing data consistency as well as quantification accuracy and precision.…”

Section: Introductionmentioning

confidence: 99%

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High sensitivity dia-PASEF proteomics with DIA-NN and FragPipe

Demichev

Yuan

et al. 2021

Preprint

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The dia-PASEF technology exploits ion mobility separation for high-sensitivity analysis of complex proteomes. Here, we demonstrate neural network-based processing of the ion mobility data, which we implement in the DIA-NN software suite. Using spectral libraries generated with the MSFragger-based FragPipe computational platform, the DIA-NN analysis of dia-PASEF raw data increases the proteomic depth by up to 69% compared to the originally published dia-PASEF workflow. For example, we quantify over 5200 proteins from 10ng of HeLa peptides separated with a 95-minute nanoflow gradient, and over 5000 proteins from 200ng using a 4.8-minute separation with an Evosep One system. In complex samples, featuring a mix of human and yeast lysates, the workflow detects over 11700 proteins in single runs acquired with a 100-minute nanoflow gradient, while demonstrating quantitative precision. Hence, the combination of FragPipe and DIA-NN provides a simple-to-use software platform for dia-PASEF data analysis, yielding significant gains in high-sensitivity proteomics.

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Section: Spectral Library Generation In Fragpipementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High sensitivity dia-PASEF proteomics with DIA-NN and FragPipe

Demichev

Yuan

et al. 2021

Preprint

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“…PTM-Prophet (13), for example, is limited to localizing mass differences for each PSM but does not provide data summaries that can inform subsequent searches nor does it provide identities for mass differences. Philosopher (14) only provides mappings of mass differences to UniMod and generates a basic mass shift histogram. Here we present PTM-Shepherd, an automated tool that calls modifications from open search peptide-spectrum match (PSM) lists and characterizes them based on attributes such as amino acid localization, fragmentation spectra similarity, effect on retention time, and relative modification rates.…”

Section: Introductionmentioning

confidence: 99%

PTM-Shepherd: Analysis and Summarization of Post-Translational and Chemical Modifications From Open Search Results

Geiszler

Kong

Avtonomov

et al. 2021

Molecular & Cellular Proteomics

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Open searching has proven to be an effective strategy for identifying both known and unknown modifications in shotgun proteomics experiments. Rather than being limited to a small set of user-specified modifications, open searches identify peptides with any mass shift that may correspond to a single modification or a combination of several modifications. Here we present PTM-Shepherd, a bioinformatics tool that automates characterization of PTM profiles detected in open searches based on attributes such as amino acid localization, fragmentation spectra similarity, retention time shifts, and relative modification rates. PTM-Shepherd can also perform multi-experiment comparisons for studying changes in modification profiles, e.g. in data generated in different laboratories or under different conditions. We demonstrate how PTM-Shepherd improves the analysis of data from formalin-fixed paraffin-embedded samples, detects extreme underalkylation of cysteine in some datasets, discovers an artefactual modification introduced during peptide synthesis, and uncovers site-specific biases in sample preparation artifacts in a multi-center proteomics profiling study.

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“…Crystal-C, 27 processing of peptide and protein identifications using PeptideProphet 29 and ProteinProphet, 30 false discovery-rate (FDR) filtering via Philosopher 31 and summarisation of the mass shifts observed on peptides using PTM-Shepherd. 28 Using this procedure, we identified the expected alkylation with IA-alkyne as the main modification (m exp ) in the form of two peaks corresponding to the isotopically differentiated isoDTB tags ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Profiling the Proteome-Wide Selectivity of Diverse Electrophiles

Zanon¹,

Yu²,

Zhang³

et al. 2021

Preprint

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<p><a>Targeted covalent inhibitors are powerful entities in drug discovery, but their application has so far mainly been limited to addressing cysteine residues. The development of cysteine-directed covalent inhibitors has largely profited from determining their proteome-wide selectivity using competitive residue-specific proteomics. Several probes have recently been described to monitor other amino acids using this technology and many more electrophiles exist to modify proteins. Nevertheless, a direct, proteome‑wide comparison of the selectivity of diverse probes is still entirely missing. Here, we developed a completely unbiased workflow to analyse electrophile selectivity proteome‑wide and applied it to directly compare 54 alkyne probes containing diverse reactive groups. In this way, we verified and newly identified probes to monitor a total of nine different amino acids as well as the <i>N</i>‑terminus proteome‑wide. This selection includes the first probes to globally monitor tryptophans, histidines and arginines as well as novel tailored probes for methionines, aspartates and glutamates.</a></p>

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Philosopher: a versatile toolkit for shotgun proteomics data analysis

Cited by 387 publications

References 10 publications

High sensitivity dia-PASEF proteomics with DIA-NN and FragPipe

High sensitivity dia-PASEF proteomics with DIA-NN and FragPipe

PTM-Shepherd: Analysis and Summarization of Post-Translational and Chemical Modifications From Open Search Results

Profiling the Proteome-Wide Selectivity of Diverse Electrophiles

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