Literature DB >> 30667224

P-Mart: Interactive Analysis of Ion Abundance Global Proteomics Data.

Lisa M Bramer1, Kelly G Stratton1, Amanda M White1, Ameila H Bleeker1, Markus A Kobold1, Katrina M Waters2, Thomas O Metz2, Karin D Rodland2,3, Bobbie-Jo M Webb-Robertson1.   

Abstract

The use of mass-spectrometry-based techniques for global protein profiling of biomedical or environmental experiments has become a major focus in research centered on biomarker discovery; however, one of the most important issues recently highlighted in the new era of omics data generation is the ability to perform analyses in a robust and reproducible manner. This has been hypothesized to be one of the issues hindering the ability of clinical proteomics to successfully identify clinical diagnostic and prognostic biomarkers of disease. P-Mart ( https://pmart.labworks.org ) is a new interactive web-based software environment that enables domain scientists to perform quality-control processing, statistics, and exploration of large-complex proteomics data sets without requiring statistical programming. P-Mart is developed in a manner that allows researchers to perform analyses via a series of modules, explore the results using interactive visualization, and finalize the analyses with a collection of output files documenting all stages of the analysis and a report to allow reproduction of the analysis.

Entities:  

Keywords:  exploratory data analysis; proteomics; reproducibility; software; statistics; visualization; web service

Year:  2019        PMID: 30667224      PMCID: PMC7032029          DOI: 10.1021/acs.jproteome.8b00840

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  38 in total

Review 1.  Reproducibility in science: improving the standard for basic and preclinical research.

Authors:  C Glenn Begley; John P A Ioannidis
Journal:  Circ Res       Date:  2015-01-02       Impact factor: 17.367

2.  ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity.

Authors:  T Xu; S K Park; J D Venable; J A Wohlschlegel; J K Diedrich; D Cociorva; B Lu; L Liao; J Hewel; X Han; C C L Wong; B Fonslow; C Delahunty; Y Gao; H Shah; J R Yates
Journal:  J Proteomics       Date:  2015-07-11       Impact factor: 4.044

3.  Proteomics wants cRacker: automated standardized data analysis of LC-MS derived proteomic data.

Authors:  Henrik Zauber; Waltraud X Schulze
Journal:  J Proteome Res       Date:  2012-09-28       Impact factor: 4.466

4.  DanteR: an extensible R-based tool for quantitative analysis of -omics data.

Authors:  Tom Taverner; Yuliya V Karpievitch; Ashoka D Polpitiya; Joseph N Brown; Alan R Dabney; Gordon A Anderson; Richard D Smith
Journal:  Bioinformatics       Date:  2012-07-19       Impact factor: 6.937

Review 5.  Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.

Authors:  Bobbie-Jo M Webb-Robertson; Holli K Wiberg; Melissa M Matzke; Joseph N Brown; Jing Wang; Jason E McDermott; Richard D Smith; Karin D Rodland; Thomas O Metz; Joel G Pounds; Katrina M Waters
Journal:  J Proteome Res       Date:  2015-04-22       Impact factor: 4.466

6.  A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors.

Authors:  Bobbie-Jo M Webb-Robertson; Melissa M Matzke; Jon M Jacobs; Joel G Pounds; Katrina M Waters
Journal:  Proteomics       Date:  2011-11-17       Impact factor: 3.984

7.  P-MartCancer-Interactive Online Software to Enable Analysis of Shotgun Cancer Proteomic Datasets.

Authors:  Bobbie-Jo M Webb-Robertson; Lisa M Bramer; Jeffrey L Jensen; Markus A Kobold; Kelly G Stratton; Amanda M White; Karin D Rodland
Journal:  Cancer Res       Date:  2017-11-01       Impact factor: 12.701

8.  A combination of proteomics, principal component analysis and transcriptomics is a powerful tool for the identification of biomarkers for macrophage maturation in the U937 cell line.

Authors:  Kitty C M Verhoeckx; Sabina Bijlsma; Els M de Groene; Renger F Witkamp; Jan van der Greef; Richard J T Rodenburg
Journal:  Proteomics       Date:  2004-04       Impact factor: 3.984

9.  An automated proteomic data analysis workflow for mass spectrometry.

Authors:  Ken Pendarvis; Ranjit Kumar; Shane C Burgess; Bindu Nanduri
Journal:  BMC Bioinformatics       Date:  2009-10-08       Impact factor: 3.169

10.  The FAIR Guiding Principles for scientific data management and stewardship.

Authors:  Mark D Wilkinson; Michel Dumontier; I Jsbrand Jan Aalbersberg; Gabrielle Appleton; Myles Axton; Arie Baak; Niklas Blomberg; Jan-Willem Boiten; Luiz Bonino da Silva Santos; Philip E Bourne; Jildau Bouwman; Anthony J Brookes; Tim Clark; Mercè Crosas; Ingrid Dillo; Olivier Dumon; Scott Edmunds; Chris T Evelo; Richard Finkers; Alejandra Gonzalez-Beltran; Alasdair J G Gray; Paul Groth; Carole Goble; Jeffrey S Grethe; Jaap Heringa; Peter A C 't Hoen; Rob Hooft; Tobias Kuhn; Ruben Kok; Joost Kok; Scott J Lusher; Maryann E Martone; Albert Mons; Abel L Packer; Bengt Persson; Philippe Rocca-Serra; Marco Roos; Rene van Schaik; Susanna-Assunta Sansone; Erik Schultes; Thierry Sengstag; Ted Slater; George Strawn; Morris A Swertz; Mark Thompson; Johan van der Lei; Erik van Mulligen; Jan Velterop; Andra Waagmeester; Peter Wittenburg; Katherine Wolstencroft; Jun Zhao; Barend Mons
Journal:  Sci Data       Date:  2016-03-15       Impact factor: 6.444

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  1 in total

1.  Deoxyhypusine synthase promotes a pro-inflammatory macrophage phenotype.

Authors:  Emily Anderson-Baucum; Annie R Piñeros; Abhishek Kulkarni; Bobbie-Jo Webb-Robertson; Bernhard Maier; Ryan M Anderson; Wenting Wu; Sarah A Tersey; Teresa L Mastracci; Isabel Casimiro; Donalyn Scheuner; Thomas O Metz; Ernesto S Nakayasu; Carmella Evans-Molina; Raghavendra G Mirmira
Journal:  Cell Metab       Date:  2021-09-07       Impact factor: 31.373

  1 in total

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