CharmeRT: Boosting Peptide Identifications by Chimeric Spectra Identification and Retention Time Prediction

Dorfer, Viktoria; Maltsev, S.; Winkler, Stephan; Mechtler, Karl

doi:10.1021/acs.jproteome.7b00836

Cited by 66 publications

(100 citation statements)

References 42 publications

(72 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DART-ID aims to use all MS2 spectra, including those of very low confidence PSMs, and combines them with accurate RT estimates to update peptide-spectrum-match (PSM) confidence within a principled Bayesian framework. Unlike previous MS2-based methods which incorporate RT estimates into features for FDR recalculation [12], discriminants [13], filters [14][15][16], or scores [17,18], we update the ID confidence directly with a Bayesian model [19,20] Crucial to this method is the accuracy of the alignment method; the higher the accuracy of RT estimates, the more informative they are for identifying the peptide sequence.…”

Section: Introductionmentioning

confidence: 99%

“…These approaches either (i) predict RTs from peptide sequences or (ii) align empirically measured RTs. Estimated peptide RTs have a wide range of uses, such as scheduling targeted MS/MS experiments [21], building efficient inclusion and exclusion lists for LC-MS/MS [22,23], or augmenting MS2 mass spectra to increase identification rates [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…These features predict the relative hydrophobicity of peptide sequences and thus RTs for LC used with MS [24][25][26][27][28][29][30]. The predicted RTs can be improved by implementing machine learning algorithms that incorporate confident, observed peptides as training data [14,18,[31][32][33][34]. Predicted peptide RTs are mostly used for scheduling targeted MS/MS analyses where acquisition time is limited, e.g., multiple reaction monitoring [21].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

DART-ID increases single-cell proteome coverage

Chen¹,

Franks

Slavov³

2018

Preprint

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DART-ID increases single-cell proteome coverage

Chen¹,

Franks

Slavov³

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Results of the MS/MS search engine were filtered to 1 % FDR on protein and peptide level using the Elutator algorithm [8], implemented as node to Proteome Discoverer 2.3. Identified peptide features were extracted from the raw-files and quantified, using the in-house-developed Proteome Discover-node apQuant [9].…”

Section: Technical Briefmentioning

confidence: 99%

Improved Sensitivity in Low-Input Proteomics using Micro-Pillar Array-based Chromatography

Stadlmann

Hudecz

Krššáková

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Capitalizing on the massive increase in sample concentrations which are produced by extremely low elution volumes, nano-LC-ESI-MS/MS is currently one of the most sensitive analytical technologies for the comprehensive characterization of complex protein samples.However, despite tremendous technological improvements made in the production and the packing of monodisperse spherical particles for nano-flow HPLC, current state-of-the-art systems still suffer from limits in operation at the maximum potential of the technology.With the recent introduction of the µPAC system, which provides perfectly ordered micro-pillar array based chromatographic support materials, completely new chromatographic concepts for optimization towards the needs of ultra-sensitive proteomics become available.Here we report on a series of benchmarking experiments comparing the performance of a commercially available 50 cm micro-pillar array column to a widely used nano-flow HPLC column for the proteomics analysis of 10 ng tryptic HeLa cell digest.Comparative analysis of LC-MS/MS-data corroborated that micro-pillar array cartridges provide outstanding chromatographic performance, excellent retention time stability, increase sensitivity in the analysis of low-input proteomics samples, and thus repeatedly yielded almost twice as many unique peptide and unique protein group identifications when compared to conventional nano-flow HPLC columns.

show abstract

“…Of these 200 feature groups, 58 were identified through closer inspection as, often modified, UPS peptides and often came from chimeric spectra. One helpful approach in identifying these chimeric spectra was by filtering out the fragment peaks of an already identified peptide species and applying another open modification search [8].…”

Section: Ups-yeast Datasetmentioning

confidence: 99%

Focus on the spectra that matter by clustering of quantification data in shotgun proteomics

Käll

2018

Preprint

View full text Add to dashboard Cite

In shotgun proteomics, the information extractable from label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data. This generally causes a low sensitivity in differential expression analysis on protein level. Here, we propose a quantification-first approach for peptides that reverses the classical identification-first workflow. This prevents valuable information from being discarded prematurely in the identification stage and allows us to spend more effort on the identification process. Specifically, we introduce a method, Quandenser, that applies unsupervised clustering on both MS1 and MS2 level to summarize all analytes of interest without assigning identities. Not only does this eliminate the need for redoing the quantification for each new set of search parameters and engines, but it also reduces search time due to the data reduction by MS2 clustering. For a dataset of partially known composition, we could now employ open modification and de novo searches to identify analytes of interest that would have gone unnoticed in traditional pipelines. Moreover, Quandenser reports error rates for feature matching, which we integrated into our probabilistic protein quantification method, Triqler. This propagates error probabilities from feature to protein level and appropriately deals with the noise in quantitative signals caused by false positives and missing values. Quandenser+Triqler outperformed the state-of-the-art method MaxQuant+Perseus, consistently reporting more differentially abundant proteins at 5% FDR: 123 vs. 117 true positives with 2 vs. 25 false positives in a dataset of partially known composition; 62 vs. 3 proteins in a bladder cancer set; 8 vs. 0 proteins in a hepatic fibrosis set; and 872 vs. 661 proteins in a nanoscale type 1 diabetes set. Compellingly, in all three clinical datasets investigated, the differentially abundant proteins showed enrichment for functional annotation terms.The source code and binary packages for all major operating systems are available from https: //github.com/statisticalbiotechnology/quandenser, under Apache 2.0 license.

show abstract

CharmeRT: Boosting Peptide Identifications by Chimeric Spectra Identification and Retention Time Prediction

Cited by 66 publications

References 42 publications

DART-ID increases single-cell proteome coverage

DART-ID increases single-cell proteome coverage

Improved Sensitivity in Low-Input Proteomics using Micro-Pillar Array-based Chromatography

Focus on the spectra that matter by clustering of quantification data in shotgun proteomics

Contact Info

Product

Resources

About