Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

LFQ_benchmark_artificial_batch_effect

ABSTRACT: LFQ benchmark from commercial digests according to Kuharev, Joerg, et al. (2015) Sample A 5%, 30%, 65% E.coli, Yeast, Human; 1ug on column Sample B 20%, 15%, 65% E.coli, Yeast, Human; 0.7ug on column Sample C Average of Sample A and B for Spectral Library by Gas-Phase Fractionationation; 1ug on column (just for completeness, I do not use GPF anymore for my applications, please use predicted libraries or use the GPF with caution). The dilution of sample B to 70% of total concentration lifts up measured log2 fold-changes by +0.5. For non-normalized data, expected log2 fold-changes are -1.5 (E.coli), +0.5(Human), +1.5 (Yeast). A perfect cross-run normalization would restore log2 fold-changes to the classical values of -2,0,+1. If a normalization does not perform as expected or over-corrects, this LFQ benchmark can show this especially on the precursor level. 30 min linear gradient on a classical nanflow trap-elute setup using Dionex3000 and Acclaim PepMap columns.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Escherichia Coli (ncbitaxon:562) Homo Sapiens (ncbitaxon:9606) Saccharomyces Cerevisiae (ncbitaxon:4932)

SUBMITTER: Andrej Shevchenko

PROVIDER: MSV000090832 | MassIVE | Mon Dec 05 05:45:00 GMT 2022

REPOSITORIES: MassIVE

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

DIA_staggered_LFQ_Benchmark_High-Performance_QE-HF_30,90,150minGradient

Project description:LFQ Benchmark with samples from commercial digests according to Kuharev, Joerg, et al (2015). Sample A: 5% E.coli, 30 % Yeast, 65% Human, 0.9ug on column. Sample B: 20% E.coli, 15% Yeast, 65% Human, 0.9ug on column. (4 replicates per gradient length, one aliquot per 4 samples from one mastermix per sample type). Expected log2 fold-changes of -2,0,+1 for E.coli, Human, Yeast. Sample C: 0.9ug on column, average of A and B used to build enhanced spectral library by Gas-Phase-Fractionation. Sample C files are here for completeness, obtained libraries can lead to improvements but I do not use refined libraries for my applications anymore, use at own caution and risk. Notably, the chromatography was performed with uPAC analytical column, resulting in sharp and symmetric <20s short chromatograms. Gradients (30,90,150 min) were 2-sloped and linear, with the last third of the gradient encompassing half of the %B increase. Analysis with Spectronaut might not be ideal as MS1 was recorded at 30k resolution. This dataset, in particular the 90min gradient ones, can possibly lead to the strongest LFQ benchmark results and might serve as upper standards in comparisons.

2022-12-05 | MSV000090837 | MassIVE

In-depth and reproducibly quantitation of benchmark dataset consiting of 20 files

Project description:We prepared a benchmark dataset where the various levels of spikeed-in E. Coli proteome that true fold change (i.e. 1 fold, 1.5 fold, 2 fold, 2.5 fold and 3 fold) and true identities of positives/negatives (i.e. E.Coli proteins are true positives while Human proteins are true negatives) are known. To best mimic the proteomics application in comparison of multiple replicates, each fold change

2017-04-20 | PXD005590 | Pride

Quantitative Proteomics Benchmark Dataset to evaluate label-free quantitative methods- LC/Orbitrap Fusion MS analysis of E coli proteomes spiked-in Human proteins at 5 different levels (N=20)

Project description:To unbiasedly evaluate the quantitative performance of different quantitative methods, and compare different popular proteomics data processing workflows, we prepared a benchmark dataset where the various levels of spikeed-in E. Coli proteome that true fold change (i.e. 1 fold, 1.5 fold, 2 fold, 2.5 fold and 3 fold) and true identities of positives/negatives (i.e. E.Coli proteins are true positives while Human proteins are true negatives) are known. To best mimic the proteomics application in comparison of multiple replicates, each fold change group contains 4 replicates, so there are 20 LC-MS/MS analysis in this benchmark dataset. To our knowledge, this spike-in benchmark dataset is largest-scale ever that encompasses 5 different spike level, >500 true positive proteins, and >3000 true negative proteins (2peptide criteria, 1% protein FDR), with a wide concentration dynamic range. The dataset is ideal to test quantitative accuracy, precision, false-positive biomarker discovery and missing data level.

2018-05-16 | PXD003881 | Pride

Benchmarking label-free quantification based on data-independent acquisition (DIA) workflows

Project description:Label-free quantification based on data-independent acquisition (DIA) workflows is increasingly popular. Several software tools have been recently published or are commercially available. The present study focuses on the critical evaluation of three different software packages (Progenesis, synapter and ISOQuant) supporting ion-mobility enhanced DIA data. In order to benchmark the label-free quantification performance of the different tools, we generated two hybrid proteome samples of defined quantitative composition containing tryptically digested proteomes of three different species (mouse, yeast, E.coli). This model data set simulates complex biological samples containing large numbers of both unregulated (background) proteins as well as up- and down-regulated proteins with exactly known ratios between samples. We determined the number and dynamic range of quantifiable proteins and analyzed the influence of applied algorithms (retention time alignment, clustering, normalization, etc.) on the variation of reported protein quantities between technical replicates.

2017-04-06 | PXD001240 | Pride

Cell cycle dependent nucleosome occupancy at cohesin binding sites in yeast chromosomes

Project description:Nucleosome organization determines local chromatin structure and changes in nucleosome occupancy during the cell cycle are correlated with nuclear functions. We used oligonucleotide tiling arrays to analyze the cell cycle dependence of nucleosome occupancy at cohesin binding sites in yeast chromosomes. Processed data included in Series table. Data processing: To improve the signal to noise ratio, the raw data were first processed with dCHIP with its PM/MM difference model and invariant set normalization method, using the entire dataset. 8251 out of the total 92812 probe pairs were excluded from further analysis because they either have multiple hits in the genome or the signal from the genomic hybridization was too weak (cutoff at (PM-MM)/MM < 0.25). After Gaussian smoothing (s =75 bp), the data were normalized by that of the genomic DNA. These values were then converted to log2 scale. The mean was then computed for each array. To facilitate comparisons, the different datasets were rescaled to have the same mean with log2 value of 0. The log2 ratio along the four chromosomes at all the cell cycle stages are included. The ID represents "Chromosome No_chromosomal coordinate". Keywords: time course Yeast cells were arrested at G1/S boundary with alpha factor, then released for 15 min, 40 min, 65 min and 95 min respectively. Cells were collected at each time points, then mononucleosomal DNA was purified and hybridized to Affymetrix oligonucleotide tiling arrays. Total genomic DNA was hybridized as normalization signal.

2010-05-26 | E-GEOD-8130 | biostudies-arrayexpress

Label-free Quantification of Host-Cell Protein Impurity in a Recombinant Hemoglobin Reference Material

Project description:Quantitative analysis depends on pure-substance primary calibrators with known mass fractions of impurity. The datasets included were generated using mass spectrometry for the evaluation of label-free quantification (LFQ) as a method for determining the mass fraction of host-cell proteins (HCPs) in bioengineered proteins. For this purpose, hemoglobin-A2 (HbA2) was used, as obtained by overexpression in E.coli. Two different materials had been produced: natural, and U-15N-labeled HbA2. To quantify impurity, precursor ion (MS1-) intensities were integrated over all E.coli-proteins identified and divided by the intensities obtained for HbA2. This ratio was calibrated against the corresponding results for an E.coli-cell lysate, that had been spiked at known mass-ratios to pure HbA2. To demonstrate the universal applicability of LFQ, further proteomes (yeast and human K562) were then alternatively used for calibration and were found to produce comparable results. Valid results were also obtained when the complexity of the calibrator was reduced to a mix of nine proteins.

2023-05-09 | PXD041736 | Pride

Comparison of IS5 Deletion mutant to E.coli Motile and NonMotile Isolates

Project description:These series of experiments compares the expression profile of the motility variants of E.coli MG1655 ( Motile and NonMotile isolates) to an isogenic E.coli MG1655 strain in which the IS5 upstream of flhDC has been deleted. The expression profiles of genes in the E.coli MG1655 motile isolate and E.coli MG1655 Non_Motile isolate also compared. Keywords: parallel sample

2005-06-02 | GSE2694 | GEO

Caco-2 cells cocultured with E.coli Nissle 1917

Project description:Confluent Caco-2 cells cultured over 6 hours non-treated with E.coli Nissle 1917 (GSM40938 and GSM40941). Confluent Caco-2 cells cocultured over 6 hours with E.coli Nissle 1917 (GSM40881 and GSM40937). Keywords: parallel sample

2005-03-02 | GSE2232 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of E.coli, Wild Type and ΔrecA Transcriptomes

Project description:Purpose:Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived E.coli transcriptome profiling (RNA-seq) of Wild type and ΔrecA with or without antibiotics treatment. Methods:libraries with different indices were multiplexed and loaded on an Illumina HiSeq instrument, Sequencing was carried out using a 2x150bp paired-end (PE) configuration, image analysis and base calling were conducted by the HiSeq Control Software (HCS) + OLB + GAPipeline-1.6 (Illumina) on the HiSeq instrument. Main data analysis include Quality Control(Cutadapt), Mapping(Bowtie2 (v2.2.6)),Expression analysis(HTSeq (v0.6.1p1)), Differential expression analysis (DESeq2 Bioconductor package). Results:Using an optimized data analysis workflow, we mapped about 20 million sequence reads per sample to the E.coli genome, The treatment of ampicillin affected the transcriptomic profile of either in the wild type or the ΔrecA strain, compared with that of untreated control cells, with changes to the expression of 4373 and 4286 coding sequences, respectively. Analysis of only the genes with a log2 fold change (log2FC) of ≥ ± 2 showed that 161 and 248 genes were differentially expressed in the ampicillin-treated wild type and ΔrecA strains, respectively. Conclusions:Our study represents the detailed analysis of E.coli transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

2021-07-06 | GSE179434 | GEO

Assessment of microRNA differential expression and detection in multiplexed small RNA sequencing data

Project description:Small RNA sequencing can be used to gain an unprecedented amount of detail into the microRNA transcriptome. The relatively high cost and low throughput of sequencing bases technologies can potentially be offset by the use of multiplexing. However multiplexing involves a tradeoff between increased number of sequenced samples and reduced number of reads per sample (i.e. lower coverage). To assess the effect of different depths of coverage due to multiplexing on microRNA differential expression and detection, we sequenced the small RNA of lung tissue samples collected in a clinical setting by multiplexing 1, 3, 6, 9, or 12 samples per lane using the Illumina HiSeq 2000. As expected, the numbers of reads obtained per sample decreased as the number of samples in a multiplex increased. Furthermore, after normalization replicate samples included in distinct multiplexes were highly correlated (R > 0.97). When detecting differential microRNA expression between groups of samples, microRNAs with average expression greater than 1 reads per million (RPM) had reproducible fold change estimates (signal to noise) independent of the degree of multiplexing. The number of microRNAs detected was strongly correlated with the Log2 number of reads aligning to microRNA loci (R=0.96). However, most additional microRNAs detected in samples with greater sequencing coverage were in the range of expression which had lower fold change reproducibility. These findings elucidate the tradeoff between increasing the number of samples in a multiplex with decreasing coverage and will aid in the design of large-scale clinical studies exploring microRNA expression and its role in disease.

2014-12-29 | GSE56861 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data