pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes
Abstract
The pmartR (https://github.com/pmartR/pmartR) package was designed for the quality control (QC) and analysis of mass spectrometry (MS) data, tailored to specific characteristics of proteomic (isobaric or labelled), metabolomic, and lipidomic datasets. Since its initial release, the tool has been expanded to address the needs of its growing userbase and now includes QC and statistics for nuclear magnetic resonance (NMR) metabolomic data, and leverages the DESeq2, edgeR, and limma-voom R packages for some transcriptomic data analyses. These improvements have made progress towards a unified omics processing pipeline for ease of reporting and streamlined statistical purposes. The package’s statistics and visualization capabilities have also been expanded by adding support for paired data and by integrating pmartR with the trelliscopejs R package for the quick creation of trellis displays (https://github.com/hafen/trelliscopejs). Here, we present relevant examples of each of these enhancements to pmartR and highlight how each new feature benefits the omics community.
- Authors:
-
[1];
[1];
[1];
[1];
[1];
[1]
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1968883
- Report Number(s):
- PNNL-SA-177968
Journal ID: ISSN 1535-3893
- Grant/Contract Number:
- AC05-76RL01830
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Proteome Research
- Additional Journal Information:
- Journal Volume: 22; Journal Issue: 2; Journal ID: ISSN 1535-3893
- Publisher:
- American Chemical Society (ACS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; mass spectrometry; nuclear magnetic resonance; rna sequencing; omics; metabolomics; lipidomics; proteomics; transcriptomics
Citation Formats
Degnan, David J., Stratton, Kelly G., Richardson, Rachel, Claborne, Daniel, Martin, Evan A., Johnson, Nathan A., Leach, Damon, Webb-Robertson, Bobbie-Jo M., and Bramer, Lisa M. pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes. United States: N. p., 2023.
Web. doi:10.1021/acs.jproteome.2c00610.
Degnan, David J., Stratton, Kelly G., Richardson, Rachel, Claborne, Daniel, Martin, Evan A., Johnson, Nathan A., Leach, Damon, Webb-Robertson, Bobbie-Jo M., & Bramer, Lisa M. pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes. United States. https://doi.org/10.1021/acs.jproteome.2c00610
Degnan, David J., Stratton, Kelly G., Richardson, Rachel, Claborne, Daniel, Martin, Evan A., Johnson, Nathan A., Leach, Damon, Webb-Robertson, Bobbie-Jo M., and Bramer, Lisa M. Mon .
"pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes". United States. https://doi.org/10.1021/acs.jproteome.2c00610. https://www.osti.gov/servlets/purl/1968883.
@article{osti_1968883,
title = {pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes},
author = {Degnan, David J. and Stratton, Kelly G. and Richardson, Rachel and Claborne, Daniel and Martin, Evan A. and Johnson, Nathan A. and Leach, Damon and Webb-Robertson, Bobbie-Jo M. and Bramer, Lisa M.},
abstractNote = {The pmartR (https://github.com/pmartR/pmartR) package was designed for the quality control (QC) and analysis of mass spectrometry (MS) data, tailored to specific characteristics of proteomic (isobaric or labelled), metabolomic, and lipidomic datasets. Since its initial release, the tool has been expanded to address the needs of its growing userbase and now includes QC and statistics for nuclear magnetic resonance (NMR) metabolomic data, and leverages the DESeq2, edgeR, and limma-voom R packages for some transcriptomic data analyses. These improvements have made progress towards a unified omics processing pipeline for ease of reporting and streamlined statistical purposes. The package’s statistics and visualization capabilities have also been expanded by adding support for paired data and by integrating pmartR with the trelliscopejs R package for the quick creation of trellis displays (https://github.com/hafen/trelliscopejs). Here, we present relevant examples of each of these enhancements to pmartR and highlight how each new feature benefits the omics community.},
doi = {10.1021/acs.jproteome.2c00610},
journal = {Journal of Proteome Research},
number = 2,
volume = 22,
place = {United States},
year = {Mon Jan 09 00:00:00 EST 2023},
month = {Mon Jan 09 00:00:00 EST 2023}
}
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Works referenced in this record:
Sources of Technical Variability in Quantitative LC–MS Proteomics: Human Brain Tissue Sample Analysis
journal, April 2013
- Piehowski, Paul D.; Petyuk, Vladislav A.; Orton, Daniel J.
- Journal of Proteome Research, Vol. 12, Issue 5
pmartR : Quality Control and Statistics for Mass Spectrometry-Based Biological Data
journal, January 2019
- Stratton, Kelly G.; Webb-Robertson, Bobbie-Jo M.; McCue, Lee Ann
- Journal of Proteome Research, Vol. 18, Issue 3
A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors
journal, November 2011
- Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Jacobs, Jon M.
- PROTEOMICS, Vol. 11, Issue 24
Combined Statistical Analyses of Peptide Intensities and Peptide Occurrences Improves Identification of Significant Peptides from MS-Based Proteomics Data
journal, November 2010
- Webb-Robertson, Bobbie-Jo M.; McCue, Lee Ann; Waters, Katrina M.
- Journal of Proteome Research, Vol. 9, Issue 11
NMR Spectroscopy for Metabolomics Research
journal, June 2019
- Emwas, Abdul-Hamid; Roy, Raja; McKay, Ryan T.
- Metabolites, Vol. 9, Issue 7
limma powers differential expression analyses for RNA-sequencing and microarray studies
journal, January 2015
- Ritchie, Matthew E.; Phipson, Belinda; Wu, Di
- Nucleic Acids Research, Vol. 43, Issue 7
Differential expression analysis for sequence count data
journal, October 2010
- Anders, Simon; Huber, Wolfgang
- Genome Biology, Vol. 11, Issue 10
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
journal, November 2009
- Robinson, M. D.; McCarthy, D. J.; Smyth, G. K.
- Bioinformatics, Vol. 26, Issue 1
MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights
journal, May 2021
- Pang, Zhiqiang; Chong, Jasmine; Zhou, Guangyan
- Nucleic Acids Research, Vol. 49, Issue W1
Remarkable Reproducibility of Enzyme Activity Profiles in Tomato Fruits Grown under Contrasting Environments Provides a Roadmap for Studies of Fruit Metabolism
journal, January 2014
- Biais, Benoît; Bénard, Camille; Beauvoit, Bertrand
- Plant Physiology, Vol. 164, Issue 3
Scaling and Normalization Effects in NMR Spectroscopic Metabonomic Data Sets
journal, February 2006
- Craig, Andrew; Cloarec, Olivier; Holmes, Elaine
- Analytical Chemistry, Vol. 78, Issue 7
Physiological roles of tryptophan decarboxylase revealed by overexpression of SlTDC1 in tomato
journal, January 2021
- Tsunoda, Yui; Hano, Shohei; Imoto, Nozomi
- Scientia Horticulturae, Vol. 275
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome
journal, January 2009
- Langmead, Ben; Trapnell, Cole; Pop, Mihai
- Genome Biology, Vol. 10, Issue 3
STAR: ultrafast universal RNA-seq aligner
journal, October 2012
- Dobin, Alexander; Davis, Carrie A.; Schlesinger, Felix
- Bioinformatics, Vol. 29, Issue 1
TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions
journal, January 2013
- Kim, Daehwan; Pertea, Geo; Trapnell, Cole
- Genome Biology, Vol. 14, Issue 4
Naught all zeros in sequence count data are the same
journal, January 2020
- Silverman, Justin D.; Roche, Kimberly; Mukherjee, Sayan
- Computational and Structural Biotechnology Journal, Vol. 18
Human Chorionic Gonadotropin Modulates the Transcriptome of the Myometrium and Cervix in Late Gestation
journal, March 2021
- Motomura, Kenichiro; Romero, Roberto; Galaz, Jose
- Reproductive Sciences, Vol. 28, Issue 8
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model
journal, December 2019
- Townes, F. William; Hicks, Stephanie C.; Aryee, Martin J.
- Genome Biology, Vol. 20, Issue 1
Improved quality control processing of peptide-centric LC-MS proteomics data
journal, August 2011
- Matzke, Melissa M.; Waters, Katrina M.; Metz, Thomas O.
- Bioinformatics, Vol. 27, Issue 20
mixOmics: An R package for ‘omics feature selection and multiple data integration
journal, November 2017
- Rohart, Florian; Gautier, Benoît; Singh, Amrit
- PLOS Computational Biology, Vol. 13, Issue 11