Dataset Information

A Bothrops jararaca venom subproteome to benchmark a spectral clustering algorithm by controlling the selection bias

ABSTRACT: The clustering of mass spectra is a critical component of many proteomics applications. The clustering validation science is just as important, having evolved side by side with the clustering algorithms themselves. In this work, we build on Rieder et al. 's cluster validation framework, and we discuss the problem of selection bias in cluster validation measures; we introduce an assessment measure that is biased toward the number of peptide ion species; we introduce a cluster assessment framework for proteomics and demonstrate its importance by evaluating the performance of 8 clustering algorithms in 7 proteomics datasets, and we discuss the tradeoff between assessment measures. Finally, the validation methods presented here can be of broad applicability be-yond the clustering of mass spectra. This PRIDE entry describes in detail sample preparation, LC-MS/MS analysis, and protein identification of one of the proteomics datasets used in this work (> 10 kDa Bothrops jararaca snake venom proteome).

INSTRUMENT(S): Q Exactive

ORGANISM(S): Bothrops Jararaca (jararaca) (bothrops Jajaraca)

SUBMITTER: Richard Hemmi Valente

LAB HEAD: Richard Hemmi Valente

PROVIDER: PXD022124 | Pride | 2022-03-16

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	20150429_Bjararaca_Amicon_PT10_1_5xdil_Run_01.raw	Raw
	20150429_Bjararaca_Amicon_PT10_1_5xdil_Run_01.sqt	Other
	20150429_Bjararaca_Amicon_PT10_1_5xdil_Run_02.raw	Raw
	20150429_Bjararaca_Amicon_PT10_1_5xdil_Run_02.sqt	Other
	20150429_Bjararaca_Amicon_PT10_1_5xdil_Run_03.raw	Raw

Items per page:

1 - 5 of 7

Publications

Leveraging the partition selection bias to achieve a high-quality clustering of mass spectra.

Silva André R F ARF Lima Diogo B DB Kurt Louise U LU Dupré Mathieu M Chamot-Rooke Julia J Santos Marlon D M MDM Nicolau Carolina Alves CA Valente Richard Hemmi RH Barbosa Valmir C VC Carvalho Paulo C PC

Journal of proteomics 20210602

In proteomics, the identification of peptides from mass spectral data can be mathematically described as the partitioning of mass spectra into clusters (i.e., groups of spectra derived from the same peptide). The way partitions are validated is just as important, having evolved side by side with the clustering algorithms themselves and given rise to many partition assessment measures. An assessment measure is said to have a selection bias if, and only if, the probability that a randomly chosen p ...[more]

PMID: 34089898

Similar Datasets

Project description:BACKGROUND: Evaluation of the airway transcriptome may reveal patterns of gene expression that are associated with clinical phenotypes of asthma. To define transcriptomic endotypes of asthma (TEA) we analyzed gene expression in induced sputum that correlate with phenotypes of disease. METHODS: Gene expression was measured in sputum of subjects with asthma using Affymetrix HuGene ST 1.0 microarrays. Unsupervised clustering analysis of genes in pathways selected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) identified TEA clusters. Clinical characteristics were compared and logistic regression analysis of matched blood samples defined an expression profile to determine the TEA cluster assignment in a cohort of children with asthma for validation. RESULTS: Three TEA clusters were identified. TEA cluster 1 had the most subjects with a history of intubation (P = 0.05), a lower pre-bronchodilator FEV1 (P = 0.006), a higher bronchodilator response (P = 0.03), and higher exhaled nitric oxide levels (P = 0.04), compared to the other TEA clusters. TEA cluster 2, the smallest cluster had the most subjects that were hospitalized for asthma (P = 0.04). Subjects in TEA cluster 3, the largest cluster, had normal lung function, low exhaled nitric oxide levels, and lower inhaled steroid requirements. Evaluation of TEA clusters in children confirmed that TEA clusters 1 and 2 are associated with a history of intubation (P = 5.58 x 10-06) and hospitalization (P = 0.01), respectively. CONCLUSIONS: Patterns of gene expression in the sputum and blood reveal TEA clusters that are associated with severe asthma phenotypes in children and adults. Gene expression was measured in sputum of subjects with asthma using Affymetrix HuGene ST 1.0 microarrays. Unsupervised clustering analysis of genes in pathways selected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) identified TEA clusters. Clinical characteristics were compared and logistic regression analysis of matched blood samples defined an expression profile to determine the TEA cluster assignment in a cohort of children with asthma for validation.