Project description:Dataset contains mixture of 41 standards mixed at equimolar concentration of 10uM in Fecal background and then subsequently diluted down to 100 pM. Each sample ran in triplicate.
Project description: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.
Project description:Dataset contains 137 Standards mixed together at equimolar concentration of 10uM. Subsequent 1:10 dilutions down to 100pM are made. Sequence of all samples injected 3 times with washes between each run. This data set contains the positive mode data set. Negative mode data was also obtained at the same time
Project description:The PTM-SWATH MS Gold Standard data set consists of a previously published (Soste et al., 2014, PMID:25194849) set of 579 unpurified, synthetic, heavy-isotope labeled phosphopeptides (Thermo Scientific Biopolymers). These phosphopeptides represent biologically relevant sequences from S. cerevisiae proteins, which have been found altered in their phosphorylation status under various conditions. The complete peptide set contains a mixture of singly and doubly phosphorylated sequences with in average more than 3 modifiable residues per peptide (serines, threonines or tyrosins, often in close proximity to each other). All peptides were mixed with equal volumes (concentrations are unknown due to the unpurified status of the peptides) and the resulting peptide mix was either analyzed directly in DDA mode for assay library generation or spiked into a human cell line background proteome in a 13-step dilution series and analyzed in SWATH mode for the generation of the SWATH Gold Standard data set.
Project description:Dataset contains 137 Standards mixed together at equimolar concentration of 10uM. Subsequent 1:10 dilutions down to 100pM are made. Sequence of all samples injected 3 times on different days under the identical conditions
Project description:The Phosphopeptide Challenge of the MS Resource Pillar of the Human Proteome Project (HPP) provides a unique opportunity for the HUPO membership to evaluate and compare methods for peptide sequence analysis by mass spectrometry, phosphosite localization, phosphopeptide enrichment, and data processing. Each participant is to apply their own methods and chosen bioinformatic pipeline to fully characterize the provided Phosphopeptide Challenge samples. As a result of this collaborative endeavor, multiple purification schemes, analytical protocols and data processing strategies will be evaluated, making it possible to determine the approach(es) that provide the highest coverage of the phosphopeptides in the mixture.
Each participating laboratory will receive two sample vials. The vial labeled 'Phosphopeptide' contains a set of synthesized phosphorylated (Ser, Thr, or Tyr) peptides of human sequence origin at various concentrations, mixed with their non-phosphorylated counterparts. For some peptides, there is more than one phosphorylated form. The peptide sequences are included in the attached Excel file. The second vial labeled 'Phosphopeptide-Yeast' contains the same peptides in a background matrix consisting of 6 ug of trypsin-digested yeast lysate. Each vial is provided dry. Resuspension in 100 uL will result in synthetic peptide concentrations of 3 fmol/uL to 30 fmol/uL.
The goal for the study is for you to provide your best method(s) to:
1. Identify the peptide sequences in the vial and determine the number and location of phosphorylation sites on each peptide (Phosphopeptide).
2. Determine the relative abundance of phosphorylation at each modified site by comparison with its non-phosphorylated counterpart (Phosphopeptide).
3. Enrich for phosphorylated peptides from the sample containing the yeast background matrix and re-analyze by MS (Phosphopeptide-Yeast).
Project description: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.
Project description:Dataset contains mixture of 41 standards mixed at equimolar concentration of 10uM and then subsequently diluted down to 100 pM. Each sample ran in triplicate.
Project description:The purpose of this work was to describe a computational and analytical methodology for profiling small RNA by high-throughput sequencing. The datasets here were used to develop synthetic oligoribonucleotides as spike-in standards. We assessed the use of synthetic oligoribonucleotide standards as spike-in controls. These standards can be used to set an objective standard against which to compare samples. Standards were added to the total RNA (100 ug) in the following amounts: Std2 (TATATGCAAGTCCGGCCATAC) 0.01 pmol, Std3 (TAGCTAACGCATATCCGCATC) 0.1 pmol, Std6 (TGAAGCTGACATCGGTCATCC) 1.0 pmol.