Dataset Information

Design and initial characterization of the SC-200 proteomics standard mixture.

ABSTRACT: High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate = 1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels.

SUBMITTER: Bauman A

PROVIDER: S-EPMC3110723 | biostudies-literature | 2011 Jan-Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Design and initial characterization of the SC-200 proteomics standard mixture.

Bauman Andrew A Higdon Roger R Rapson Sean S Loiue Brenton B Hogan Jason J Stacy Robin R Napuli Alberto A Guo Wenjin W van Voorhis Wesley W Roach Jared J Lu Vincent V Landorf Elizabeth E Stewart Elizabeth E Kolker Natali N Collart Frank F Myler Peter P van Belle Gerald G Kolker Eugene E

Omics : a journal of integrative biology 20110121 1-2

High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimenta ...[more]

PMID: 21250827

Similar Datasets

Project description:MotivationSingle cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct counting of transcript copies using Unique Molecular Identifier (UMI). Despite the technology advances, statistical methods and computational tools are still lacking for analyzing droplet-based scRNA-Seq data. Particularly, model-based approaches for clustering large-scale single cell transcriptomic data are still under-explored.ResultsWe developed DIMM-SC, a Dirichlet Mixture Model for clustering droplet-based Single Cell transcriptomic data. This approach explicitly models UMI count data from scRNA-Seq experiments and characterizes variations across different cell clusters via a Dirichlet mixture prior. We performed comprehensive simulations to evaluate DIMM-SC and compared it with existing clustering methods such as K-means, CellTree and Seurat. In addition, we analyzed public scRNA-Seq datasets with known cluster labels and in-house scRNA-Seq datasets from a study of systemic sclerosis with prior biological knowledge to benchmark and validate DIMM-SC. Both simulation studies and real data applications demonstrated that overall, DIMM-SC achieves substantially improved clustering accuracy and much lower clustering variability compared to other existing clustering methods. More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods.Availability and implementationDIMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/∼wec47/singlecell.html.Contactwei.chen@chp.edu or hum@ccf.org.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:The increasing demand for natural alternatives to synthetic fungicides has prompted research into natural products like essential oils for postharvest disease management. This study investigated the antifungal, antioxidant, cytotoxic, and genotoxic potential of essential oil mixtures derived from oregano, rosemary, and mint against Penicillium digitatum, the predominant fungal pathogen causing green mold in orange fruits. P. digitatum NPAGRASU 2024 was isolated and identified as the most abundant species (60.2%) from infected oranges. It was deposited in GenBank with gene accession number PP930644 and deposited in MIRCEN culture collection as EMCC 358874. The antimicrobial activity of individual essential oils was evaluated, with oregano exhibiting the highest antifungal activity (inhibition zone diameter of 4.2 cm) against P. digitatum. L-optimal mixture design of response surface methodology (RSM) optimization revealed a highly effective mixture (Run 8) comprising 46.26% oregano and 53.74% rosemary, with a 99.65% actual growth reduction. The oregano oil demonstrated potent antioxidant activity, reaching approximately 75% DPPH radical scavenging at 3.125 mg/mL. Cytotoxicity assessment using the MTT assay showed morphological changes and reduced cell viability in liver cells treated with the PEOs mixture at 300 µg/mL. However, the optimized mixture did not induce significant chromosomal aberrations compared to the control, suggesting minimal genotoxic effects. In vivo, evaluation on oranges revealed 60% inhibition of green mold by the 1% (v/v) optimized mixture for 7 days. Histological analysis indicated low toxicity to the liver at the highest tested concentration (1% mixture). GC-MS analysis identified major compounds like cavarcol, caryophyllene, eucalyptol, phenols and levomenthol in oregano oil, contributing to its bioactivities. This study demonstrates the potential of optimized essential oil mixtures as effective and eco-friendly alternatives for postharvest disease control.

Dataset Information

Design and initial characterization of the SC-200 proteomics standard mixture.

Publications

Design and initial characterization of the SC-200 proteomics standard mixture.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets