NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses
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Title
NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses
Authors
Keywords
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Journal
NUCLEIC ACIDS RESEARCH
Volume -, Issue -, Pages -
Publisher
Oxford University Press (OUP)
Online
2020-06-09
DOI
10.1093/nar/gkaa498
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Note: Only part of the references are listed.- Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries
- (2020) Dorte B. Bekker-Jensen et al. Nature Communications
- Combining Rapid Data Independent Acquisition and CRISPR Gene Deletion for Studying Potential Protein Functions: A Case of HMGN1
- (2019) Martin Mehnert et al. PROTEOMICS
- Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma
- (2019) Ying Jiang et al. NATURE
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- (2019) Lili Niu et al. Molecular Systems Biology
- DIAlignR provides precise retention time alignment across distant runs in DIA and targeted proteomics
- (2019) Shubham Gupta et al. MOLECULAR & CELLULAR PROTEOMICS
- Assessing the Relationship Between Mass Window Width and Retention Time Scheduling on Protein Coverage for Data-Independent Acquisition
- (2019) Wenxue Li et al. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY
- Analysis of 1508 plasma samples by capillary flow data-independent acquisition profiles proteomics of weight loss and maintenance
- (2019) Roland Bruderer et al. MOLECULAR & CELLULAR PROTEOMICS
- GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis
- (2019) Qian Li et al. BIOINFORMATICS
- Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study
- (2019) Marietta Kokla et al. BMC BIOINFORMATICS
- Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma
- (2019) Qiang Gao et al. CELL
- Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma
- (2019) David J. Clark et al. CELL
- motifeR: An Integrated Web Software for Identification and Visualization of Protein Posttranslational Modification Motifs
- (2019) Shisheng Wang et al. PROTEOMICS
- BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes
- (2018) Florian Meier et al. NATURE METHODS
- IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts
- (2018) Xiaomeng Shen et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies
- (2018) Runmin Wei et al. PLoS Computational Biology
- Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data
- (2018) Runmin Wei et al. Scientific Reports
- The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments
- (2018) Jonathon J. O’Brien et al. Annals of Applied Statistics
- ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies
- (2018) Jing Tang et al. BRIEFINGS IN BIOINFORMATICS
- The PRIDE database and related tools and resources in 2019: improving support for quantification data
- (2018) Yasset Perez-Riverol et al. NUCLEIC ACIDS RESEARCH
- Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results
- (2017) Roland Bruderer et al. MOLECULAR & CELLULAR PROTEOMICS
- Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes
- (2017) Kevin Drew et al. Molecular Systems Biology
- Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS
- (2017) George Rosenberger et al. NATURE BIOTECHNOLOGY
- Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses
- (2017) George Rosenberger et al. NATURE METHODS
- Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry
- (2017) Ben C. Collins et al. Nature Communications
- Human SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome
- (2016) Ulrike Kusebauch et al. CELL
- Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies
- (2016) Cosmin Lazar et al. JOURNAL OF PROTEOME RESEARCH
- Mass-spectrometric exploration of proteome structure and function
- (2016) Ruedi Aebersold et al. NATURE
- A multicenter study benchmarks software tools for label-free proteome quantification
- (2016) Pedro Navarro et al. NATURE BIOTECHNOLOGY
- TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics
- (2016) Hannes L Röst et al. NATURE METHODS
- The Perseus computational platform for comprehensive analysis of (prote)omics data
- (2016) Stefka Tyanova et al. NATURE METHODS
- Review, Evaluation, and Discussion of the Challenges of Missing Value Imputation for Mass Spectrometry-Based Label-Free Global Proteomics
- (2015) Bobbie-Jo M. Webb-Robertson et al. JOURNAL OF PROTEOME RESEARCH
- Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues
- (2015) Roland Bruderer et al. MOLECULAR & CELLULAR PROTEOMICS
- Quantitative variability of 342 plasma proteins in a human twin population
- (2015) Y. Liu et al. Molecular Systems Biology
- A Review on Missing Value Imputation Algorithms for Microarray Gene Expression Data
- (2014) Kohbalan Moorthy et al. Current Bioinformatics
- Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ
- (2014) Jürgen Cox et al. MOLECULAR & CELLULAR PROTEOMICS
- OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data
- (2014) Hannes L Röst et al. NATURE BIOTECHNOLOGY
- A repository of assays to quantify 10,000 human proteins by SWATH-MS
- (2014) George Rosenberger et al. Scientific Data
- Identification of genetic variants influencing the human plasma proteome
- (2013) A. Johansson et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Normalization and missing value imputation for label-free LC-MS analysis
- (2012) Yuliya V Karpievitch et al. BMC BIOINFORMATICS
- Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis
- (2012) Ludovic C. Gillet et al. MOLECULAR & CELLULAR PROTEOMICS
- Selected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions
- (2012) Paola Picotti et al. NATURE METHODS
- MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation
- (2011) Laurent Gatto et al. BIOINFORMATICS
- Iterative stepwise regression imputation using standard and robust methods
- (2011) Matthias Templ et al. COMPUTATIONAL STATISTICS & DATA ANALYSIS
- On the Accuracy and Limits of Peptide Fragmentation Spectrum Prediction
- (2010) Sujun Li et al. ANALYTICAL CHEMISTRY
- Missing value imputation for gene expression data: computational techniques to recover missing data from available information
- (2010) A. W.-C. Liew et al. BRIEFINGS IN BIOINFORMATICS
- Options and considerations when selecting a quantitative proteomics strategy
- (2010) Bruno Domon et al. NATURE BIOTECHNOLOGY
- A HUPO test sample study reveals common problems in mass spectrometry–based proteomics
- (2009) Alexander W Bell et al. NATURE METHODS
- Missing value imputation for microarray gene expression data using histone acetylation information
- (2008) Qian Xiang et al. BMC BIOINFORMATICS
- Robust data imputation
- (2008) Karlien Vanden Branden et al. COMPUTATIONAL BIOLOGY AND CHEMISTRY
- MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification
- (2008) Jürgen Cox et al. NATURE BIOTECHNOLOGY
- A quantitative atlas of mitotic phosphorylation
- (2008) N. Dephoure et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
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