Dataset Information

MaxLFQ label-free quantification algorithm benchmark datasets

ABSTRACT: We developed a set of algorithms for label-free quantification, termed MaxLFQ, embedded into MaxQuant. This contains two datasets to benchmark MaxLFQ: The proteome benchmark dataset consists of of HeLa and E. coli lysates mixed at defined ratios. The dynamic range benchmark dataset consists of UPS1/UPS2 standards (Sigma) spiked into E. coli lysates and quantified against each other.

INSTRUMENT(S): LTQ Orbitrap, Q Exactive

ORGANISM(S): Homo Sapiens (human) Escherichia Coli

SUBMITTER: Mario Oroshi

LAB HEAD: Mario Oroshi

PROVIDER: PXD000279 | Pride | 2014-09-17

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	20070904_CL_Orbi4_Offgel_XIC_Hela60_Ecoli10_black_Frac12_3.RAW	Raw
	20070904_CL_Orbi4_Offgel_XIC_Hela60_Ecoli10_black_Frac13_3.RAW	Raw
	20070904_CL_Orbi4_Offgel_XIC_Hela60_Ecoli10_green_Frac12_2.RAW	Raw
	20070904_CL_Orbi4_Offgel_XIC_Hela60_Ecoli10_green_Frac13_2.RAW	Raw
	20070904_CL_Orbi4_Offgel_XIC_Hela60_Ecoli10_red_Frac12_1.RAW	Raw

Items per page:

1 - 5 of 153

Publications

Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.

Cox Jürgen J Hein Marco Y MY Luber Christian A CA Paron Igor I Nagaraj Nagarjuna N Mann Matthias M

Molecular & cellular proteomics : MCP 20140617 9

Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the pr ...[more]

PMID: 24942700

Similar Datasets

Project description:This dataset derives from experiments testing quantitation using label-free methods. We used the Sigma-Aldrich UPS1 and UPS2 protein standard sets, containing proteins of 6-83 kD; the UPS1 set includes 49 human proteins at a single molar concentration, while the UPS2 set includes six groups of eight human proteins spanning a six orders of magnitude in concentration. In both cases, dilutions of UPS1 or UPS2 proteins were made into an E. coli extract, which allows these experiments to mimic detection of low abundance proteins in a complex protein mixture. The UPS1/E. coli and UPS2/E. coli mixtures were run on three mass spectrometers, an Orbitrap Velos Elite and two ion-trap instruments (Velos and LTQ). Proteins were quantified using MS1 peak volume (Orbitrap) or MS2 intensities and spectral counts (Orbitrap, Velos, LTQ). The analyzed results show that the ion-trap instruments perform nearly as well for label-free quantitation as does the Orbitrap. In addition, the \"Top 3\" method for quantitation—measuring only the three most abundant peptides—performed no better than the \"iBAQ\" quantitation method. Data analysis: MS1 data were analyzed using MaxQuant. MaxQuant version 1.2.2.5 software was used for protein identification and quantitation. Using the search engine Andromeda, mass spectrometry data were searched against the database containing UPS and E. coli proteins. MaxQuant reports summed intensity for each protein, as well as its iBAQ value. In the iBAQ algorithm, the intensities of the precursor peptides that map to each protein are summed together and divided by the number of theoretically observable peptides, which is considered to be all tryptic peptides between 6 and 30 amino acids in length. This operation converts a measure that is expected to be proportional to mass (intensity) into one that is proportional to molar amount (iBAQ). To determine relative molar abundances for each E. coli and human protein using MS1 data, two methods were used. In the first, we determined each proteins relative iBAQ (riBAQ), a normalized measure of molar abundance. We removed from the analysis all contaminant proteins that entered our sample-preparation workflow, for example keratins and trypsin, and then divided each remaining proteins iBAQ value by the sum of all non-contaminant iBAQ values. We also measured the average intensity of the three (or fewer, if fewer peptides were detected) peptides with the highest intensity, Top3, for each protein detected9. Peptides were chosen separately for each experiment, so the same peptides were not necessarily used across the four experiments. We generated a normalized \"top three\" abundance factor by dividing the average intensity for the three most abundant peptides of an individual protein and by the sum of all Top3 values in an experiment. Peptide intensities were summed for all charge states and variable modifications. For MS2 label-free analysis with MS2 spectra, MS2 data were searched against the database described above using SEQUEST. Setting to 1% the peptide false-discovery rate, estimated using a decoy (reversed) database, proteins were identified using the PAW pipeline. Normalized molar intensity (im) was calculated by dividing i, the summed intensity for an individual protein, by its molecular mass; the i/mr value for each protein was divided by the sum of all i/mr values. Normalized molar counts (cm) were calculated similarly, except the summed spectral counts for a protein (c) was used instead.