scholarly journals Label-free absolute protein quantification with data-independent acquisition

2019 ◽  
Vol 200 ◽  
pp. 51-59 ◽  
Author(s):  
Bing He ◽  
Jian Shi ◽  
Xinwen Wang ◽  
Hui Jiang ◽  
Hao-Jie Zhu
Keyword(s):  
Protein Quantification ◽  
Label Free ◽  
Data Independent Acquisition

scholarly journals DIA-NN: Deep neural networks substantially improve the identification performance of Data-independent acquisition (DIA) in proteomics

2018 ◽  
Author(s):  
Vadim Demichev ◽  
Christoph B. Messner ◽  
Kathryn S. Lilley ◽  
Markus Ralser
Keyword(s):  
Software Tool ◽  
Protein Quantification ◽  
Neural Nets ◽  
Label Free ◽  
Software Analysis ◽  
Data Independent Acquisition ◽  
Typical Data ◽  
Computational Reconstruction ◽  
Selection Of ◽  
Precursor Ions

AbstractData-independent acquisition (DIA-MS) strategies, like SWATH-MS, have been developed to increase consistency, quantification precision and proteomic depth in label-free proteomic experiments. They aim to overcome stochasticity in the selection of precursor ions by utilising (mass-) windowed acquisition that is followed by computational reconstruction of the chromatograms. While DIA methods increasingly outperform typical data-dependent methods in identification consistency and precision specifically on large sample series, possibilities remain for further improvements. At present, only a fraction of the information recorded in the complex DIA spectra is extracted by the software analysis pipelines. Here we present a software tool (DIA-NN) that introduces artificial neural nets and a new quantification strategy to enhance signal processing in DIA-data. DIA-NN greatly improves identification of precursor ions and, as a consequence, protein quantification accuracy. The performance of DIA-NN demonstrates that deep learning provides opportunities to boost the analysis of data-independent acquisition workflows in proteomics.

scholarly journals iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics

2020 ◽  
Vol 36 (8) ◽  
pp. 2611-2613 ◽  
Author(s):  
Thang V Pham ◽  
Alex A Henneman ◽  
Connie R Jimenez
Keyword(s):  
Open Source ◽  
State Of The Art ◽  
R Package ◽  
Protein Quantification ◽  
Supplementary Information ◽  
Label Free ◽  
Software Suite ◽  
Data Independent Acquisition ◽  
Free Data ◽  
Acquisition Mode

Abstract Summary We present an R package called iq to enable accurate protein quantification for label-free data-independent acquisition (DIA) mass spectrometry-based proteomics, a recently developed global approach with superior quantitative consistency. We implement the popular maximal peptide ratio extraction module of the MaxLFQ algorithm, so far only applicable to data-dependent acquisition mode using the software suite MaxQuant. Moreover, our implementation shows, for each protein separately, the validity of quantification over all samples. Hence, iq exports a state-of-the-art protein quantification algorithm to the emerging DIA mode in an open-source implementation. Availability and implementation The open-source R package is available on CRAN, https://github.com/tvpham/iq/releases and oncoproteomics.nl/iq. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals LFAQ: Toward Unbiased Label-Free Absolute Protein Quantification by Predicting Peptide Quantitative Factors

2018 ◽  
Vol 91 (2) ◽  
pp. 1335-1343 ◽  
Author(s):  
Cheng Chang ◽  
Zhiqiang Gao ◽  
Wantao Ying ◽  
Yan Fu ◽  
Yan Zhao ◽  
...  
Keyword(s):  
Protein Quantification ◽  
Label Free

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 NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

2020 ◽  
Vol 48 (14) ◽  
pp. e83-e83 ◽  
Author(s):  
Shisheng Wang ◽  
Wenxue Li ◽  
Liqiang Hu ◽  
Jingqiu Cheng ◽  
Hao Yang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Missing Values ◽  
Protein Complexes ◽  
Label Free ◽  
Missing Value ◽  
Imputation Methods ◽  
Data Independent Acquisition ◽  
Low Performance ◽  
User Friendly

Abstract Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

scholarly journals DIALib-QC an assessment tool for spectral libraries in data-independent acquisition proteomics

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Mukul K. Midha ◽  
David S. Campbell ◽  
Charu Kapil ◽  
Ulrike Kusebauch ◽  
Michael R. Hoopmann ◽  
...  
Keyword(s):  
Quality Control ◽  
Assessment Tool ◽  
Software Tool ◽  
Mass Range ◽  
Systematic Evaluation ◽  
Label Free ◽  
Mass Spectral ◽  
Data Independent Acquisition ◽  
Theoretical Mass ◽  
Spectral Libraries

Abstract Data-independent acquisition (DIA) mass spectrometry, also known as Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH), is a popular label-free proteomics strategy to comprehensively quantify peptides/proteins utilizing mass spectral libraries to decipher inherently multiplexed spectra collected linearly across a mass range. Although there are many spectral libraries produced worldwide, the quality control of these libraries is lacking. We present the DIALib-QC (DIA library quality control) software tool for the systematic evaluation of a library’s characteristics, completeness and correctness across 62 parameters of compliance, and further provide the option to improve its quality. We demonstrate its utility in assessing and repairing spectral libraries for correctness, accuracy and sensitivity.

Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics

2019 ◽  
Vol 25 (13) ◽  
pp. 1536-1553 ◽  
Author(s):  
Jing Tang ◽  
Yunxia Wang ◽  
Yi Li ◽  
Yang Zhang ◽  
Runyuan Zhang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Information Sources ◽  
Protein Quantification ◽  
Protein Profiling ◽  
Label Free ◽  
Dynamic Information ◽  
Data Repositories ◽  
Research Directions ◽  
Technological Advances ◽  
Recent Trends

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

Sign in / Sign up

Export Citation Format

Share Document