Identification of modified peptides using localization-aware open search

Yu, Fengchao; Teo, Guo Ci; Kong, Andy T.; Haynes, Sarah E.; Avtonomov, Dmitry M.; Geiszler, Daniel; Nesvizhskii, Alexey I.

doi:10.1038/s41467-020-17921-y

Cited by 208 publications

(216 citation statements)

References 39 publications

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“…For the MSFragger analysis, both precursor and (initial) fragment mass tolerances were set to 20 ppm. Spectrum deisotoping (Teo et al, 2021), mass calibration, and parameter optimization (Yu et al, 2020b) were enabled. Enzyme specificity was set to 'stricttrypsin' (i.e.…”

Section: Spectral Library Generation In Fragpipementioning

confidence: 99%

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%

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

Demichev

Yuan

et al. 2021

Preprint

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“…If ion mobility data are used, we take the same approach to locate the target range in the ion mobility dimension (controlled by the ‘MBR IM window’ parameter, 0.05 by default). The transferred ion’s m/z equals the donor ion’s m/z adjusted by mass calibration error (mass calibration is performed by MSFragger (27)). After locating the target region in m/z, retention time, and ion mobility if applicable, we trace all peaks within the region using our recently described algorithm (21).…”

Section: Introductionmentioning

confidence: 99%

IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs

Haynes

Nesvizhskii

2020

Preprint

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Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate that IonQuant with FDR-controlled MBR results in superior performance compared to MaxQuant. We further illustrate the need for FDR-controlled MBR in sparse datasets such as those from single-cell proteomics experiments.

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“…These programs have developed ways to restrict the computational search space considered to localise PTMs in unmatched spectra. The main examples include; Fragpipe [ 109 ], MetaMorpheus [ 91 ], Open-pfind [ 110 ], tagGraph [ 111 ], Peaks Studio [ 112 ], Proteome Discoverer [ 113 ], Byonic [ 114 ], MaxQuant [ 92 ], for more examples see Table 4 . These programs are essential to the exploration of NPAA localisation within the proteome.…”

Section: Data Analysis Techniquesmentioning

confidence: 99%

Misincorporation Proteomics Technologies: A Review

et al. 2021

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Proteinopathies are diseases caused by factors that affect proteoform conformation. As such, a prevalent hypothesis is that the misincorporation of noncanonical amino acids into a proteoform results in detrimental structures. However, this hypothesis is missing proteomic evidence, specifically the detection of a noncanonical amino acid in a peptide sequence. This review aims to outline the current state of technology that can be used to investigate mistranslations and misincorporations whilst framing the pursuit as Misincorporation Proteomics (MiP). The current availability of technologies explored herein is mass spectrometry, sample enrichment/preparation, data analysis techniques, and the hyphenation of approaches. While many of these technologies show potential, our review reveals a need for further development and refinement of approaches is still required.

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Identification of modified peptides using localization-aware open search

Cited by 208 publications

References 39 publications

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

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

IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs

Misincorporation Proteomics Technologies: A Review

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