ArticleInternational journal of mass spectrometry2007
Differential quantitative proteomics of Porphyromonas gingivalis by linear ion trap mass spectrometry: non-label methods comparison, q-values and LOWESS curve fitting.
Article in International journal of mass spectrometry, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 36 citations in OpenAlex.
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- Proteomics and antivenomics of Echis carinatus carinatus venom: Correlation with pharmacological properties and pathophysiology of envenomation.Scientific reports · 2017Article
- Proteomics of Fusobacterium nucleatum within a model developing oral microbial community.MicrobiologyOpen · 2014Article
- Proteome analysis of coinfection of epithelial cells with Filifactor alocis and Porphyromonas gingivalis shows modulation of pathogen and host regulatory pathways.Infection and immunity · 2014Article
- Proteomics of Streptococcus gordonii within a model developing oral microbial community.BMC microbiology · 2012Article
- Review
- Analysis of a membrane-enriched proteome from postmortem human brain tissue in Alzheimer's disease.Proteomics. Clinical applications · 2012Article
- Is central dogma a global property of cellular information flow?Frontiers in physiology · 2012Article
- Relative, label-free protein quantitation: spectral counting error statistics from nine replicate MudPIT samples.Journal of the American Society for Mass Spectrometry · 2010Article
- Article
- Normalization and statistical analysis of quantitative proteomics data generated by metabolic labeling.Molecular & cellular proteomics : MCP · 2009Article
- Pathway analysis for intracellular Porphyromonas gingivalis using a strain ATCC 33277 specific database.BMC microbiology · 2009Article
- Quantitative proteomics of nutrient limitation in the hydrogenotrophic methanogen Methanococcus maripaludis.BMC microbiology · 2009Article
- Proteomics of Porphyromonas gingivalis within a model oral microbial community.BMC microbiology · 2009Article
- Discovering new bioactive neuropeptides in the striatum secretome using in vivo microdialysis and versatile proteomics.Molecular & cellular proteomics : MCP · 2009Article
- iTRAQ-based shotgun neuroproteomics.Methods in molecular biology (Clifton, N.J.) · 2009Article
- Significance analysis of spectral count data in label-free shotgun proteomics.Molecular & cellular proteomics : MCP · 2008Article
- Science, marketing and wishful thinking in quantitative proteomics.Proteomics · 2008Article
- Comprehensive proteomics of Methylobacterium extorquens AM1 metabolism under single carbon and nonmethylotrophic conditions.Proteomics · 2008Article
- Up-regulation of NG2 proteoglycan and interferon-induced transmembrane proteins 1 and 3 in mouse astrocytoma: a membrane proteomics approach.Cancer letters · 2008Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Differential analysis of whole cell proteomes by mass spectrometry has largely been applied using various forms of stable isotope labeling. While metabolic stable isotope labeling has been the method of choice, it is often not possible to apply such an approach. Four different label free ways of calculating expression ratios in a classic "two-state" experiment are compared: signal intensity at the peptide level, signal intensity at the protein level, spectral counting at the peptide level, and spectral counting at the protein level. The quantitative data were mined from a dataset of 1245 qualitatively identified proteins, about 56% of the protein encoding open reading frames from Porphyromonas gingivalis, a Gram-negative intracellular pathogen being studied under extracellular and intracellular conditions. Two different control populations were compared against P. gingivalis internalized within a model human target cell line. The q-value statistic, a measure of false discovery rate previously applied to transcription microarrays, was applied to proteomics data. For spectral counting, the most logically consistent estimate of random error came from applying the locally weighted scatter plot smoothing procedure (LOWESS) to the most extreme ratios generated from a control technical replicate, thus setting upper and lower bounds for the region of experimentally observed random error.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.