scholarly journals DIAmeter: Matching peptides to data-independent acquisition mass spectrometry data

2021 ◽  
Author(s):  
Yang Young Lu ◽  
Jeff Bilmes ◽  
Ricard A Rodriguez-Mias ◽  
Judit Villén ◽  
William Stafford Noble
Keyword(s):  
Mass Spectrometry ◽  
Complex Structure ◽  
Mass Spectrometry Data ◽  
Peptide Sequence ◽  
Sequence Database ◽  
Post Translational Modifications ◽  
Data Independent Acquisition ◽  
Using Data ◽  
Tandem Mass Spectrometry Data ◽  
Analyze Data

AbstractTandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a “spectral library”), but this approach is expensive because the libraries do not typically generalize well across laboratories. Here we propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Unlike other library-free DIA analysis methods, DIAmeter supports data generated using both wide and narrow isolation windows, can readily detect peptides containing post-translational modifications, can analyze data from a variety of instrument platforms, and is capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan.

scholarly journals PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data

2017 ◽  
Vol 14 (9) ◽  
pp. 903-908 ◽  
Author(s):  
Ying S Ting ◽  
Jarrett D Egertson ◽  
James G Bollinger ◽  
Brian C Searle ◽  
Samuel H Payne ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Tandem Mass ◽  
Free Peptide ◽  
Data Independent Acquisition ◽  
Peptide Detection ◽  
Tandem Mass Spectrometry Data

scholarly journals A flexible workflow for building spectral libraries from narrow window data independent acquisition mass spectrometry data

2021 ◽  
Author(s):  
Lilian R. Heil ◽  
William E. Fondrie ◽  
Christopher D. McGann ◽  
Alexander J. Federation ◽  
William S. Noble ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Spectral Library ◽  
Post Translational Modifications ◽  
Data Independent Acquisition ◽  
Peptide Detection ◽  
Phosphorylated Peptides ◽  
Single Mass ◽  
Fragmentation Patterns ◽  
Spectral Libraries

Advances in library-based methods for peptide detection from data independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.

scholarly journals Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments

2020 ◽  
Vol 19 (6) ◽  
pp. 944-959 ◽  
Author(s):  
Tsung-Heng Tsai ◽  
Meena Choi ◽  
Balazs Banfai ◽  
Yansheng Liu ◽  
Brendan X. MacLean ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Profile ◽  
Protein Quantification ◽  
Selected Reaction Monitoring ◽  
Estimation Accuracy ◽  
Spectral Features ◽  
Data Set ◽  
Post Translational Modifications ◽  
Data Independent Acquisition ◽  
Manual Curation

In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats.

scholarly journals Proteomic comparison of osteoarthritic and reference human menisci using data-independent acquisition mass spectrometry

2020 ◽  
Vol 28 (8) ◽  
pp. 1092-1101
Author(s):  
E. Folkesson ◽  
A. Turkiewicz ◽  
N. Ali ◽  
M. Rydén ◽  
H.V. Hughes ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Independent Acquisition ◽  
Using Data

scholarly journals Proteomic characterization of the normal human medial meniscus body using data‐independent acquisition mass spectrometry

2020 ◽  
Vol 38 (8) ◽  
pp. 1735-1745 ◽  
Author(s):  
Elin Folkesson ◽  
Aleksandra Turkiewicz ◽  
Martin Rydén ◽  
Harini Velocity Hughes ◽  
Neserin Ali ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Medial Meniscus ◽  
Data Independent Acquisition ◽  
Normal Human ◽  
Using Data
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