scholarly journals Removing the hidden data dependency of DIA with predicted spectral libraries

2019 ◽  
Author(s):  
B. Van Puyvelde ◽  
S. Willems ◽  
R. Gabriels ◽  
S. Daled ◽  
L. De Clerck ◽  
...  
Keyword(s):  
Data Analysis ◽  
Clinical Proteomics ◽  
Spectrometric Data ◽  
Stochastic Sampling ◽  
Data Independent Acquisition ◽  
Use Of Data ◽  
Analysis Strategies ◽  
Stochastic Data ◽  
Hidden Data ◽  
Spectral Libraries

Data-Independent Acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data-dependent acquisition (DDA) libraries for deep peptide-centric detection. We here show that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter-laboratory comparison.Significance of the StudyData-independent acquisition (DIA) is quickly developing into the most comprehensive strategy to analyse a sample on a mass spectrometer. Correspondingly, a wave of data analysis strategies has followed suit, improving the yield from DIA experiments with each iteration. As a result, a worldwide wave of investments in DIA is already taking place in anticipation of clinical applications. Yet, there is considerable confusion about the most useful and efficient way to handle DIA data, given the plethora of possible approaches with little regard for compatibility and complementarity. In our manuscript, we outline the currently available peptide-centric DIA data analysis strategies in a unified graphic called the DIAmond DIAgram. This leads us to an innovative and easily adoptable approach based on predicted spectral information. Most importantly, our contribution removes what is arguably the biggest bottleneck in the field: the current need for Data Dependent Acquisition (DDA) prior to DIA analysis. Fractionation, stochastic data acquisition, processing and identification all introduce bias in the library. By generating libraries through data independent, i.e. deterministic acquisition, stochastic sampling in the DIA workflow is now fully omitted. This is a crucial step towards increased standardization. Additionally, our results demonstrate that a proteome-wide predicted spectral library can surrogate an exhaustive DDA Pan-Human library that was built based on 331 prior DDA runs.

scholarly journals In-Depth Benchmarking of DIA-type Proteomics Data Analysis Strategies Using a Large-Scale Benchmark Dataset Comprising Inter-Patient Heterogeneity

2021 ◽  
Author(s):  
Klemens Fröhlich ◽  
Eva Brombacher ◽  
Matthias Fahrner ◽  
Daniel Vogele ◽  
Lucas Kook ◽  
...  
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Statistical Tests ◽  
Benchmark Dataset ◽  
Clinical Samples ◽  
Proteomics Data ◽  
Data Independent Acquisition ◽  
Analysis Strategies ◽  
Spectral Libraries ◽  
Differentially Abundant Proteins

Abstract An overwhelming number of proteomics software tools and algorithms have been published for different steps of Data Independent Acquisition analysis of clinical samples. Nonetheless, there is still a lack of comprehensive benchmark studies evaluating which combinations of those isolated components perform best. Here, we used 92 lymph nodes from distinct patients to create a unique benchmark dataset representing real-world inter-individual heterogeneity. The publicly available dataset comprises 118 LC-MS/MS runs with > 12 million MS2 spectra and allowed us to objectively evaluate how well different combinations of spectral libraries, DIA software, sparsity reduction, normalization and statistical tests can detect differentially abundant proteins, while also taking sample size into account. Evaluation of 2 million data analysis workflows showed that a gas phase fractionation refined spectral library in combination with DIA-NN and Significance Analysis of Microarrays reliably detected differentially abundant proteins. Furthermore, DIA-NN and Spectronaut robustly avoided the false detection of truly absent proteins.

scholarly journals Optimization of data-independent acquisition using predicted libraries for deep and accurate proteome profiling

2020 ◽  
Author(s):  
Joerg Doellinger ◽  
Christian Blumenscheit ◽  
Andy Schneider ◽  
Peter Lasch
Keyword(s):  
Data Analysis ◽  
In Silico ◽  
Scanning Speed ◽  
Clinical Proteomics ◽  
Full Potential ◽  
Optimization Strategy ◽  
Peptide Separation ◽  
Proteome Profiling ◽  
Data Independent Acquisition ◽  
Orbitrap Mass Spectrometer

ABSTRACTIn silico spectral library prediction of all possible peptides from whole organisms has a great potential for improving proteome profiling by data-independent acquisition (DIA) and extending its scope of application. In combination with other recent improvements in the field of mass spectrometry (MS)-based proteomics, including sample preparation, peptide separation and data analysis, we aimed to uncover the full potential of such an advanced DIA strategy by optimization of the data acquisition. The results demonstrate that the combination of high-quality in silico libraries, reproducible and high-resolution peptide separation using micro-pillar array columns as well as neural network supported data analysis enables the use of long MS scan cycles without impairing the quantification performance. The study demonstrates that mean coefficient of variations of 4 % were obtained even at only 1.5 data points per peak (full width at half maximum) across different gradient lengths, which in turn improved proteome coverage up to more than 8000 proteins from HeLa cells using empirically-corrected libraries and more than 7000 proteins using a whole human in silico predicted library. These data were obtained using a Q Exactive orbitrap mass spectrometer with moderate scanning speed (12 Hz) and perform very well in comparison to recent studies using more advanced MS instruments, which underline the high potential of this optimization strategy for various applications in clinical proteomics, microbiology and molecular biology.

scholarly journals Deep representation features from DreamDIAXMBD improve the analysis of data-independent acquisition proteomics

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mingxuan Gao ◽  
Wenxian Yang ◽  
Chenxin Li ◽  
Yuqing Chang ◽  
Yachen Liu ◽  
...  
Keyword(s):  
Data Analysis ◽  
State Of The Art ◽  
Data Driven ◽  
High Coverage ◽  
Discriminative Models ◽  
Data Independent Acquisition ◽  
Fragment Ions ◽  
Comprehensive Information ◽  
Spectral Libraries ◽  
Negative Controls

AbstractWe developed DreamDIAXMBD (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis.

scholarly journals DIAproteomics: A multi-functional data analysis pipeline for data-independent-acquisition proteomics and peptidomics

2020 ◽  
Author(s):  
Leon Bichmann ◽  
Shubham Gupta ◽  
George Rosenberger ◽  
Leon Kuchenbecker ◽  
Timo Sachsenberg ◽  
...  
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Dynamic Range ◽  
Expert Knowledge ◽  
Data Sets ◽  
Data Independent Acquisition ◽  
Large Scale Data ◽  
False Discovery ◽  
Spectral Libraries ◽  
Scale Data

ABSTRACTData-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. Main advantages include greater reproducibility, sensitivity and dynamic range compared to data-dependent acquisition (DDA). However, data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics a multi-functional, automated high-throughput pipeline implemented in Nextflow that allows to easily process proteomics and peptidomics DIA datasets on diverse compute infrastructures. Central components are well-established tools such as the OpenSwathWorkflow for DIA spectral library search and PyProphet for false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and carry out retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from statistical post-processing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is open-source software and available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.

scholarly journals The use of hybrid data-dependent and -independent acquisition spectral libraries empower dual-proteome profiling

2020 ◽  
Author(s):  
Patrick Willems ◽  
Ursula Fels ◽  
An Staes ◽  
Kris Gevaert ◽  
Petra Van Damme
Keyword(s):  
Bacterial Infections ◽  
Physiological Responses ◽  
A Priori ◽  
Bacterial Pathogen ◽  
Data Independent Acquisition ◽  
Use Of Data ◽  
Peptide Detection ◽  
Hybrid Data ◽  
Spectral Libraries ◽  
Hybrid Libraries

ABSTRACTIn the context of bacterial infections, it is imperative that physiological responses can be studied in an integrated manner, meaning a simultaneous analysis of both the host and the pathogen responses. To improve the sensitivity of detection, data-independent acquisition (DIA) based proteomics was found to outperform data-dependent acquisition (DDA) workflows in identifying and quantifying low abundant proteins. Here, by making use of representative bacterial pathogen/host proteome samples, we report an optimized hybrid library generation workflow for data-independent acquisition mass spectrometry relying on the use of data-dependent and in silico predicted spectral libraries. When compared to searching DDA experiment-specific libraries only, the use of hybrid libraries significantly improved peptide detection to an extent suggesting that infection relevant host-pathogen conditions could be profiled in sufficient depth without the need of a priori bacterial pathogen enrichment when studying the bacterial proteome.GRAPHICAL ABSTRACT

Analisis Pengaruh Kecerdasan Intelektual Terhadap Kemampuan Akuntansi dengan Kecerdasan Emosional dan Spiritual sebagai Pemoderasi

2018 ◽  
Vol 16 (1) ◽  
pp. 1
Author(s):  
Ria Manurung
Keyword(s):  
Data Analysis ◽  
Graduate Program ◽  
Descriptive Analysis ◽  
Research Result ◽  
Secondary Data ◽  
Spiritual Intelligence ◽  
Primary Data ◽  
Moderating Variables ◽  
Use Of Data ◽  
Independent Variable

Research conducted to obtain empirical evidence how the influence of independent variables of intellectual intelligence to accounting with moderating variables of emotional and spiritual intelligence. The research method used is descriptive quantitative with explanatory descriptive or explanatory research. This method is an explanatory research that proves the existence of causal relationship of independent variable (independent variable) that is intellectual intelligence; moderating variable (emotional and spiritual intelligence); and dependent variable (accounted dependent variable). Research begins by conducting library search, followed by primary data collection conducted by using questionnaires and secondary data through data analysis. And for the use of data analysis consists of descriptive analysis, classical assumption test and verification analysis with the method of Moderated Regression Analysis (MRA). This study is a census study with homogeneous and limited population of 92 students, all students of Accounting Graduate Program at UNSOED. Conclusion of research result that is: (1) Intellectual intelligence have influence either positively or signifikan to accountancy. Thus intellectual intelligence can lead students to more easily understand accounting, (2) Intellectual intelligence can be strengthened by emotional intelligence on accounting both positively and significantly. (3) Spiritual intelligence can strengthen the influence of intellectual intelligence on accounting both positively and significantly.

Application of Factor Analysis in Imaging Mass Spectrometric Data Analysis

2014 ◽  
Vol 42 (8) ◽  
pp. 1099-1103 ◽  
Author(s):  
Yi CHEN ◽  
Fei TANG ◽  
Tie-Gang LI ◽  
Jiu-Ming HE ◽  
Zeper ABLIZ ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Data Analysis ◽  
Mass Spectrometric ◽  
Mass Spectrometric Data ◽  
Spectrometric Data

scholarly journals PENANAMAN NILAI-NILAI MORAL OLEH ORANG TUA DALAM KELUARGA MENURUT REMAJA DI JORONG KUBANG KENAGARIAN MAGEK KECAMATAN KAMANG MAGEK KABUPATEN AGAM

2014 ◽  
Vol 2 (1) ◽  
Author(s):  
Fajrin Alfarabi
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Moral Values ◽  
Use Of Data ◽  
The Family

This research in the background by the availability of good in JorongKubang.This study aims to describe the cultivation of moral values ​​in children by teenagers in the family in JorongKubangKenagarianMagekKecamatanKamangMagekKabupatenAgam. This research is descriptive quantitative. The population in this study were all teenagers at Jorongkubang totaling 26 people, A survey of data collection with the use of data collection tools and quisioner. While the techniques of data analysis using the percentage formula.From the results of the study found that the cultivation of moral values ​​in children in aspects: (1) through habituation, (2) by example, (3) through advice, (4) through attention and (5) through rulemaking. From the above findings it can be concluded that the cultivation of moral values ​​in children by adolescents has been running well this is evident from the results of the percentage of each variable is declared good. General suggestion that the cultivation of moral values ​​can be enhanced and become a major concern by the parents in the family

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