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 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 Metaproteomics boosted up by untargeted data-independent acquisition data analysis framework

2020 ◽  
Author(s):  
Sami Pietilä ◽  
Tomi Suomi ◽  
Laura L. Elo
Keyword(s):  
Mass Spectrometry ◽  
Open Source Software ◽  
Previous Method ◽  
Mass Spectrometry Data ◽  
Analysis Framework ◽  
Spectral Library ◽  
Microbial Composition ◽  
Data Independent Acquisition ◽  
Open Source Software Package ◽  
First Time

AbstractMass spectrometry based metaproteomics is a relatively new field of research that provides the ability to characterize the functionality of microbiota. Recently, we were the first to demonstrate the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the conventionally used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the previous method still required additional DDA data on the samples to assist the DIA analysis. Here, we introduce, for the first time, a DIA metaproteomics approach that does not require any DDA data, but instead replaces a spectral library generated from DDA data with a pseudospectral library generated directly from the metaproteomics DIA samples. We demonstrate that using the new DIA-only approach, we can achieve higher peptide yields than with the DDA-assisted approach, while the amount of required mass spectrometry data is reduced to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as open-source software package DIAtools 2.0, which is freely available from DockerHub.

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 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 Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Brian C. Searle ◽  
Lindsay K. Pino ◽  
Jarrett D. Egertson ◽  
Ying S. Ting ◽  
Robert T. Lawrence ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Independent Acquisition ◽  
Peptide Detection ◽  
Detection And Quantification

Group-DIA: analyzing multiple data-independent acquisition mass spectrometry data files

2015 ◽  
Vol 12 (12) ◽  
pp. 1105-1106 ◽  
Author(s):  
Yuanyue Li ◽  
Chuan-Qi Zhong ◽  
Xiaozheng Xu ◽  
Shaowei Cai ◽  
Xiurong Wu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Data Independent Acquisition ◽  
Multiple Data ◽  
Data Files

scholarly journals Quantification and Identification of Post-Translational Modifications Using Modern Proteomics

2021 ◽  
pp. 225-235
Author(s):  
Anja Holtz ◽  
Nathan Basisty ◽  
Birgit Schilling
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
High Throughput ◽  
High Resolution Mass Spectrometry ◽  
Label Free ◽  
Post Translational Modifications ◽  
Data Independent Acquisition ◽  
Lysine Residues ◽  
Detailed Protocol ◽  
Resolution Mass

AbstractPost-translational modifications (PTMs) occur dynamically, allowing cells to quickly respond to changes in the environment. Lysine residues can be targeted by several modifications including acylations (acetylation, succinylation, malonylation, glutarylation, and others), methylation, ubiquitination, and other modifications. One of the most efficient methods for the identification of post-translational modifications is utilizing immunoaffinity enrichment followed by high-resolution mass spectrometry. This workflow can be coupled with comprehensive data-independent acquisition (DIA) mass spectrometry to be a high-throughput, label-free PTM quantification approach. Below we describe a detailed protocol to process tissue by homogenization and proteolytically digest proteins, followed by immunoaffinity enrichment of lysine-acetylated peptides to identify and quantify relative changes of acetylation comparing different conditions.

scholarly journals Arabidopsis Proteome and the Mass Spectral Assay Library

2019 ◽  
Author(s):  
Huoming Zhang ◽  
Pei Liu ◽  
Tiannan Guo ◽  
Huayan Zhao ◽  
Dalila Bensaddek ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Abscisic Acid ◽  
Acid Stress ◽  
Model Organism ◽  
Mass Spectrometry Data ◽  
Mass Spectral ◽  
Novel Proteins ◽  
Data Independent Acquisition ◽  
Arabidopsis Proteome ◽  
Expression Atlas

AbstractArabidopsis is an important model organism and the first plant with its genome sequenced. Knowledge from studying this species has either direct or indirect applications to agriculture and human health. Quantitative proteomics by data-independent acquisition (SWATH/DIA-MS) was recently developed and considered as a high-throughput targetedlike approach for accurate proteome quantitation. In this approach, a high-quality and comprehensive library is a prerequisite. Here, we generated a protein expression atlas of 10 organs of Arabidopsis and created a library consisting of 15,514 protein groups, 187,265 unique peptide sequences, and 278,278 precursors. The identified protein groups correspond to ~56.5% of the predicted proteome. Further proteogenomics analysis identified 28 novel proteins. We subsequently applied DIA-mass spectrometry using this library to quantify the effect of abscisic acid on Arabidopsis. We were able to recover 8,793 protein groups with 1,787 of them being differentially expressed which includes 65 proteins known to respond to abscisic acid stress. Mass spectrometry data are available via ProteomeXchange with identifier PXD012710 for data-dependent acquisition and PXD014032 for DIA analyses.

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