Literature DB >> 18453552

DAnTE: a statistical tool for quantitative analysis of -omics data.

Ashoka D Polpitiya1, Wei-Jun Qian, Navdeep Jaitly, Vladislav A Petyuk, Joshua N Adkins, David G Camp, Gordon A Anderson, Richard D Smith.   

Abstract

UNLABELLED: Data Analysis Tool Extension (DAnTE) is a statistical tool designed to address challenges associated with quantitative bottom-up, shotgun proteomics data. This tool has also been demonstrated for microarray data and can easily be extended to other high-throughput data types. DAnTE features selected normalization methods, missing value imputation algorithms, peptide-to-protein rollup methods, an extensive array of plotting functions and a comprehensive hypothesis-testing scheme that can handle unbalanced data and random effects. The graphical user interface (GUI) is designed to be very intuitive and user friendly. AVAILABILITY: DAnTE may be downloaded free of charge at http://omics.pnl.gov/software/. SUPPLEMENTARY INFORMATION: An example dataset with instructions on how to perform a series of analysis steps is available at http://omics.pnl.gov/software/

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Year:  2008        PMID: 18453552      PMCID: PMC2692489          DOI: 10.1093/bioinformatics/btn217

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  7 in total

1.  Missing value estimation methods for DNA microarrays.

Authors:  O Troyanskaya; M Cantor; G Sherlock; P Brown; T Hastie; R Tibshirani; D Botstein; R B Altman
Journal:  Bioinformatics       Date:  2001-06       Impact factor: 6.937

2.  TM4: a free, open-source system for microarray data management and analysis.

Authors:  A I Saeed; V Sharov; J White; J Li; W Liang; N Bhagabati; J Braisted; M Klapa; T Currier; M Thiagarajan; A Sturn; M Snuffin; A Rezantsev; D Popov; A Ryltsov; E Kostukovich; I Borisovsky; Z Liu; A Vinsavich; V Trush; J Quackenbush
Journal:  Biotechniques       Date:  2003-02       Impact factor: 1.993

3.  Statistical issues in cDNA microarray data analysis.

Authors:  Gordon K Smyth; Yee Hwa Yang; Terry Speed
Journal:  Methods Mol Biol       Date:  2003

Review 4.  Microarray data normalization and transformation.

Authors:  John Quackenbush
Journal:  Nat Genet       Date:  2002-12       Impact factor: 38.330

5.  Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics.

Authors:  Stephen J Callister; Richard C Barry; Joshua N Adkins; Ethan T Johnson; Wei-Jun Qian; Bobbie-Jo M Webb-Robertson; Richard D Smith; Mary S Lipton
Journal:  J Proteome Res       Date:  2006-02       Impact factor: 4.466

6.  An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database.

Authors:  J K Eng; A L McCormack; J R Yates
Journal:  J Am Soc Mass Spectrom       Date:  1994-11       Impact factor: 3.109

7.  Bioconductor: open software development for computational biology and bioinformatics.

Authors:  Robert C Gentleman; Vincent J Carey; Douglas M Bates; Ben Bolstad; Marcel Dettling; Sandrine Dudoit; Byron Ellis; Laurent Gautier; Yongchao Ge; Jeff Gentry; Kurt Hornik; Torsten Hothorn; Wolfgang Huber; Stefano Iacus; Rafael Irizarry; Friedrich Leisch; Cheng Li; Martin Maechler; Anthony J Rossini; Gunther Sawitzki; Colin Smith; Gordon Smyth; Luke Tierney; Jean Y H Yang; Jianhua Zhang
Journal:  Genome Biol       Date:  2004-09-15       Impact factor: 13.583

  7 in total
  212 in total

1.  Proteomic analysis of bronchoalveolar lavage fluid proteins from mice infected with Francisella tularensis ssp. novicida.

Authors:  Susan M Varnum; Bobbie-Jo M Webb-Robertson; Joel G Pounds; Ronald J Moore; Richard D Smith; Charles W Frevert; Shawn J Skerrett; David Wunschel
Journal:  J Proteome Res       Date:  2012-06-22       Impact factor: 4.466

2.  A hybrid approach to protein differential expression in mass spectrometry-based proteomics.

Authors:  Xuan Wang; Gordon A Anderson; Richard D Smith; Alan R Dabney
Journal:  Bioinformatics       Date:  2012-04-19       Impact factor: 6.937

3.  Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data.

Authors:  Carmen D Tekwe; Raymond J Carroll; Alan R Dabney
Journal:  Bioinformatics       Date:  2012-05-24       Impact factor: 6.937

4.  Increased power for the analysis of label-free LC-MS/MS proteomics data by combining spectral counts and peptide peak attributes.

Authors:  Lee Dicker; Xihong Lin; Alexander R Ivanov
Journal:  Mol Cell Proteomics       Date:  2010-09-07       Impact factor: 5.911

Review 5.  Profiling of protein interaction networks of protein complexes using affinity purification and quantitative mass spectrometry.

Authors:  Robyn M Kaake; Xiaorong Wang; Lan Huang
Journal:  Mol Cell Proteomics       Date:  2010-05-05       Impact factor: 5.911

Review 6.  Overcoming key technological challenges in using mass spectrometry for mapping cell surfaces in tissues.

Authors:  Noelle M Griffin; Jan E Schnitzer
Journal:  Mol Cell Proteomics       Date:  2010-06-14       Impact factor: 5.911

7.  Quantitative proteomics analysis of adsorbed plasma proteins classifies nanoparticles with different surface properties and size.

Authors:  Haizhen Zhang; Kristin E Burnum; Maria L Luna; Brianne O Petritis; Jong-Seo Kim; Wei-Jun Qian; Ronald J Moore; Alejandro Heredia-Langner; Bobbie-Jo M Webb-Robertson; Brian D Thrall; David G Camp; Richard D Smith; Joel G Pounds; Tao Liu
Journal:  Proteomics       Date:  2011-11-04       Impact factor: 3.984

8.  Dynamics of Hippocampal Protein Expression During Long-term Spatial Memory Formation.

Authors:  Natalia Borovok; Elimelech Nesher; Yishai Levin; Michal Reichenstein; Albert Pinhasov; Izhak Michaelevski
Journal:  Mol Cell Proteomics       Date:  2015-11-23       Impact factor: 5.911

9.  mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry.

Authors:  Guoshou Teo; Sinae Kim; Chih-Chiang Tsou; Ben Collins; Anne-Claude Gingras; Alexey I Nesvizhskii; Hyungwon Choi
Journal:  J Proteomics       Date:  2015-09-15       Impact factor: 4.044

10.  InvS Coordinates Expression of PrgH and FimZ and Is Required for Invasion of Epithelial Cells by Salmonella enterica serovar Typhimurium.

Authors:  Lu Wang; Xia Cai; Shuyan Wu; Rajdeep Bomjan; Ernesto S Nakayasu; Kristian Händler; Jay C D Hinton; Daoguo Zhou
Journal:  J Bacteriol       Date:  2017-06-13       Impact factor: 3.490

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