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Standard dataset of UPS1 spiked into yeast at various concentrations

ABSTRACT: A standard proteolytic digest of a human protein mixture, prepared at 1.5-fold to 3-fold protein concentration changes, and diluted into a constant background of yeast proteins. Similar to other datasets used for ground truth in quantitative studies, with the exception of being more granular, and much larger in terms of replicates, to enable more rigorous and accurate testing of quantitative algorithms.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Homo Sapiens (ncbitaxon:9606) Saccharomyces Cerevisiae (ncbitaxon:4932)

SUBMITTER: Robert L. Moritz

PROVIDER: MSV000083496 | MassIVE | Thu Feb 28 12:00:00 GMT 2019

SECONDARY ACCESSION(S): PXD006447

REPOSITORIES: MassIVE

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Publications

Robust determination of differential abundance in shotgun proteomics using nonparametric statistics.

Slama Patrick P Hoopmann Michael R MR Moritz Robert L RL Geman Donald D

Molecular omics 20181201 6

Label-free shotgun mass spectrometry enables the detection of significant changes in protein abundance between different conditions. Due to often limited cohort sizes or replication, large ratios of potential protein markers to number of samples, as well as multiple null measurements pose important technical challenges to conventional parametric models. From a statistical perspective, a scenario similar to that of unlabeled proteomics is encountered in genomics when looking for differentially ex ...[more]

PMID: 30259924

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Standard dataset of UPS1 spiked into yeast at various concentrations

Project description:A standard proteolytic digest of a human protein mixture, prepared at 1.5-fold to 3-fold protein concentration changes, and diluted into a constant background of yeast proteins. Similar to other datasets used for ground truth in quantitative studies, with the exception of being more granular, and much larger in terms of replicates, to enable more rigorous and accurate testing of quantitative algorithms.

2018-09-24 | PXD006447 | Pride

Absolute quantification of yeast proteome

Project description:Protein synthesis is the most energy consuming process in a proliferating cell, and understanding what controls protein abundances and how this impacts changes in metabolic fluxes is a key question in biology. We quantified mRNA and protein levels in Saccharomyces cerevisiae under ten environmental conditions. Linear correlation across all proteins predicted only 45% of the final protein abundances based on corresponding mRNAs, however, very good condition-dependent correlation was identified for individual proteins pointing to constant translation efficiency across the ten conditions. Hence, protein turnover was measured and combined with quantitative abundances to calculate translation efficiencies which were found to vary more than 400-fold. Non-linear regression analysis detected that mRNA abundance and translation elongation are the dominant factors controlling protein synthesis rate. Protein turnover was detected to play a minor role in determining protein levels, however, contributing markedly to overall cellular energy metabolism. Mitochondrial fluxes were detected to be the only major exception for flux control being at the posttranscriptional level. The here collected quantitative data provide a valuable resource for future studies.

2017-03-07 | PXD005041 | Pride

pyQms - partial labeled proteome gold standard

Project description:" The novel mass spectrometry quantitation tool pyQms can calculate isotope patterns for molecules that have any isotope enrichment or combinations of, e.g. 13C or 15N and allows partially labeled molecules to be quantified as well, as observed in pulse or pulse-chase experiments where enrichment levels change dynamically. In order to test how pyQms performs on such a task, we created a gold standard (GS) data set of partially labeled proteomes, which allows evaluation of algorithms detecting partial labeld isotopologues against the ground truth against 5 mixed samples. Cultures of Chlamydomonas reinhardtii were grown on medium containing 20, 40, 60, 80 and 100% 15N. Since partially labeled peptides will not produce interpretable fragmentation spectra, each of the partially labeled proteomes were mixed with an unlabeled (14N) or fully labeled (15N) proteome, yielding 5 mixed samples (0|100, 20|100, 40|100, 0|60 and 0|80). " (Modified from manuscript)

2017-08-10 | PXD003236 | Pride

Quantitative multiplexed ChIP-Seq comparing mouse embryonic stem cells grown in Serum or 2i condition.

Project description:Quantitative multiplexed ChIP-Seq reveals high levels of H3K27me3 broadly covering the genome and low levels of H3K4me3 in 2i condition (ground state). Bivalent promoters, a defining feature of pluripotent cells, are also present in the ground state. H3K27me3 is maintained at high levels at bivalent promoters, similar to those in Serum, while H3K4me3 is reduced more than twofold.

2019-03-06 | GSE126252 | GEO

AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics

Project description:Mass spectrometry (MS)-based proteomics continues to evolve rapidly, opening more and more application areas. The scale of data generated on novel instrumentation and acquisition strategies pose a challenge to bioinformatic analysis. Search engines need to make optimal use of the data for biological discoveries while remaining statistically rigorous, transparent and performant. Here we present alphaDIA, a modular open-source search framework for data independent acquisition (DIA) proteomics. We developed a feature-free identification algorithm particularly suited for detecting patterns in data produced by sensitive time-of-flight instruments. It naturally adapts to novel, more efficient scan modes that are not yet accessible to previous algorithms. Rigorous benchmarking demonstrates competitive identification and quantification performance. While supporting empirical spectral libraries, we propose a new search strategy named end-to-end transfer learning using fully predicted libraries. This entails continuously optimizing a deep neural network for predicting machine and experiment specific properties, enabling the generic DIA analysis of any post-translational modification (PTM). AlphaDIA provides a high performance and accessible framework running locally or in the cloud, opening DIA analysis to the community.

2024-06-24 | MSV000095138 | MassIVE

yeast-UPS1 standard LC-MS/MS dataset

Project description:Proteomic workflows based on nanoLC-MS/MS data-dependent-acquisition analysis have progressed tremendously in recent years due to the technical improvement of mass spectrometers, and now allow to extensively characterize complex protein mixtures. High-resolution and fast sequencing instruments have enabled the use of label-free quantitative methods, which appear as an attractive way to analyze differential protein expression in complex biological samples. Classical label-free quantitative workflows are based either on spectral counting of MS/MS sequencing scans for each protein, or on the extraction of peptide ion peak area values in the LC-MS map composed of all the survey MS scans acquired during the chromatographic gradient. However, the computational processing of the data for label-free quantification still remains a challenge. Here, we provide a dual proteomic standard composed of an equimolar mixture of 48 human proteins (Sigma UPS1) spiked at different concentrations into a background of yeast cell lysate, that was used to benchmark several label-free quantitative workflows, involving different software packages developed in recent years. This experimental design allowed to finely assess their performances in terms of sensitivity and false discovery rate, by measuring the number of true and false-positive (respectively UPS1 or yeast background proteins found as differential). This dataset can also be used to benchmark other label-free workflows, adjust software parameter settings, improve algorithms for extraction of the quantitative metrics from raw MS data, or evaluate downstream statistical methods

2015-12-01 | PXD001819 | Pride

The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster

Project description:Here we present a statistically rigorous approach to quantifying microarray expression data that allows the relative effects of multiple classes of treatment to be compared and incorporates analytical methods that are common to quantitative genetics. From the magnitude of gene effects and contributions of variance components, we find that gene expression in adult flies is affected most strongly by sex, less so by genotype and only weakly by age (for 1- and 6-wk flies); in addition, sex-genotype interactions may be present for as much as 10% of the Drosophila transcriptome. This interpretation is compromised to some extent by statistical issues relating to power and experimental design. Nevertheless, we show that changes in expression as small as 1.2 fold can be highly significant. Genotypic contributions to transcriptional variance may be of a similar magnitude to those relating to some quantitative phenotypes and should be considered when assessing the significance of experimental treatments.

2008-12-17 | GSE13928 | GEO

Benchmarking of 4C-seq pipelines based on real and simulated data

Project description:With its capacity for high-resolution data output in one region of interest, chromosome conformation capture combined with high-throughput sequencing (4C-seq) is a state-of-the-art next-generation sequencing technique that provides epigenetic insights, and regularly advances current medical research. However, 4C-seq data is complex and prone to biases, and while specialized programs exist, an unbiased, extensive benchmarking is still lacking. Furthermore, neither substantial datasets with fully characterized ground truth, nor simulation programs for realistic 4C-seq data have been published. We conducted a benchmarking study on 54 4C-seq samples from 12 datasets, including original murine BMM, T-cell, and 416B data, and developed a novel 4C-seq simulation software to allow for more detailed comparisons of 4C-seq algorithms on 50 simulated datasets with 10 to 120 samples each.

2019-05-29 | GSE123131 | GEO

Comparative analysis between single-cell RNA-seq and single-molecule RNA FISH indicates that the pyrimidine nucleobase idoxuridine (IdU) globally amplifies transcriptional noise

Project description:Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability, but the physiological roles of noise have remained difficult to determine in the absence of generalized noise-modulation approaches. Previous single-cell RNA-sequencing (scRNA-seq) suggested that the pyrimidine-base analog (5′-iodo-2′-deoxyuridine, IdU) could generally amplify noise without substantially altering mean-expression levels but scRNA-seq technical drawbacks potentially obscured the penetrance of IdU-induced transcriptional noise amplification. Here we quantify global-vs.-partial penetrance of IdU-induced noise amplification by assessing scRNA-seq data using numerous normalization algorithms and complement our previous results with data from Human Jurkat T Lymphocytes, including two biological replicates to allow rigorous statistical analysis. Collectively, this analysis indicates which scRNA-seq algorithms are appropriate for quantifying noise and argues that IdU is a globally penetrant noise-enhancer molecule that could enable investigations of the physiological impacts of transcriptional noise in various cell types and organisms.

2024-08-28 | GSE263194 | GEO

Adaptation of Pseudomonas aeruginosa to cultivation in standard laboratory conditions

Project description:We studied adaptation of the metabolically versatile bacterium Pseudomonas aeruginosa to standard laboratory conditions by propagating mismatch repair-deficient P. aeruginosa in exponential phase for 24 days in rich medium. In the selective environment of this large-bottleneck mutation accumulation experiment, the bacteria developed shorter lag phases, higher growth rates and higher maximum cell densities. Transcriptional profiling and phenotyping for growth in different media revealed that higher fitness under laboratory conditions evolved via different pathways. Although common adaptive mutations or mutations that define trade-offs were not identified, there was a convergent evolution of transcriptional profiles associated with a shift from biofilm-associated to planktonic lifestyles. Our results indicate that under constant planktonic conditions P. aeruginosa uses several genetic pathways in order to fine-tune adaptation towards faster growth. The selected mutations in the different genetic pathways show a great variety of biofilm, virulence and motility phenotypic trade-offs, thus implying that on the population level, the adaptation of P. aeruginosa to constant conditions does not compromise its versatility. Methods: mRNA profiles were generated for Pseudomonas aeruginosa samples derived from LB-cultures grown to an OD600 =0.4-0.6. The removal of ribosomal RNA was performed using the Ribo-Zero Bacteria Kit (Illumina) and cDNA libraries were generated with the ScriptSeq v2 Kit (Illumina). The samples were sequenced in single end mode on an Illumina HiSeq 2500 device and mRNA reads were trimmed and mapped to the NC_008463.1 (PA14) reference genome from NCBI using Stampy pipeline with default settings.

2020-11-26 | GSE146906 | GEO

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