Project description:Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.
Project description:Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.
Project description:Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.
Project description:Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.
Project description:Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.
Project description:In this study, we compared the proteomes of mouse CD4+Foxp3+ regulatory T cells (Treg) and CD4+Foxp3- conventional T cells (Tconv) in order to build a data set of proteins differentially regulated in these two cell populations. The data set contains mass spectrometry results from the analysis of 7 biological replicates of Treg/Tconv cell samples purified by flow cytometry, each experiment performed from a pool of 4-5 mice. Global proteomic analysis of each sample was performed by single-run nanoLC-MS/MS, using chromatographic separation of peptides on 50cm C18 reverse-phase columns, with either a 480min gradient on LTQ-Velos orbitrap mass spectrometer (replicates 1 and 2) or a 300min gradient on Q-Exactive orbitrap mass spectrometer (replicates 3-7). Several MS injection replicates were performed for some experiments, leading to 27 raw files composing the data set. The detailed description of each analysis (file name, sample type, biological replicate number, MS technical replicate number, MS instrument used, sample name in MaxQuant ouput) is given in the table “Files list.txt”.
Project description:To study the function of Physcomitrella DCL4, a knockout plant line was created. Small RNAs from ~10-day old protonemata were obtained and sequenced using a SOLiD 2 instrument. A single library each was made from dcl4 mutant protonemata and wild-type protonemata. The dcl4 library was sequenced in four separate technical replicates. The wild-type sample was subject to a single sequencing run.
Project description:The faster sequencing speed available on the latest Q Exactive HF mass spectrometer is investigated and evaluated by four different acquisition methods and benchmarked across three generations of Q Exactive instruments. Also the instrument capabilities for offline high pH reversed-phase peptide fractionation and single-shot phosphoproteomics are evaluated.