Proteomics

Dataset Information

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Membrane enriched temperature series proteome of Francisella tularensis subspecies novicida in wild type and lipid A acyltransferase null mutants.


ABSTRACT: Quantitative proteomics has matured into an established tool and longitudinal proteomic experiments have begun to emerge. However, no effective, simple-to-use differential expression method for longitudinal proteomics data has been released. Typically, such data is noisy, contains missing values, has only few time points and biological replicates. To address this need, we provide a comprehensive evaluation of several existing differential expression methods for high-throughput longitudinal omics data and introduce a new method, Robust longitudinal Differential Expression (RolDE). To evaluate the longitudinal differential expression methods in a real proteomics study setting, we generated and used an experimental membrane enriched longitudinal proteomics data of Francisella tularensis subspecies novicida (Fn). For evaluation of the methods, differential expression between the null mutant and the wild type strains was investigated,while their growth at different temperatures offered a surrogate for the different time points. Technical replicates of the samples were used to form technical replicate datasets analyzed with the different methods to evaluate the reproducibility of the methods. In addition, the Fn dataset with known protein modifications, was used to explore the evaluated methods ability to detect these proteins and other closely associated pathway proteins expected to be affected.

INSTRUMENT(S): LTQ Orbitrap Elite

ORGANISM(S): Francisella Tularensis Subsp. Novicida (strain U112)

SUBMITTER: Tommi Välikangas  

LAB HEAD: David R. Goodlett

PROVIDER: PXD025439 | Pride | 2023-01-24

REPOSITORIES: Pride

Dataset's files

Source:
Action DRS
04242015_002_WT1_37_1.raw Raw
04242015_003_WT1_37_2.raw Raw
04242015_004_WT1_37_3.raw Raw
04242015_007_WT2_37_1.raw Raw
04242015_008_WT2_37_2.raw Raw
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Publications

Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach.

Välikangas Tommi T   Suomi Tomi T   Chandler Courtney E CE   Scott Alison J AJ   Tran Bao Q BQ   Ernst Robert K RK   Goodlett David R DR   Elo Laura L LL  

Nature communications 20221222 1


Quantitative proteomics has matured into an established tool and longitudinal proteomics experiments have begun to emerge. However, no effective, simple-to-use differential expression method for longitudinal proteomics data has been released. Typically, such data is noisy, contains missing values, and has only few time points and biological replicates. To address this need, we provide a comprehensive evaluation of several existing differential expression methods for high-throughput longitudinal  ...[more]

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